Data Analysis is the ability to systematically examine complex datasets to uncover trends, patterns, and actionable insights that drive informed decision-making. This skill involves leveraging advanced analytical techniques, statistical methods, and tools to interpret data accurately and align findings with strategic objectives, such as innovation in AI, sustainability, and market leadership. Proficiency in data analysis requires a strong focus on precision, critical thinking, and problem-solving to translate raw data into meaningful outcomes that support business initiatives, optimize processes, and enhance organizational performance. It also encompasses the ability to effectively communicate insights through data visualization and reporting, enabling cross-functional teams to achieve shared goals and contribute to the company’s mission of enriching lives and creating a sustainable future.
Learning Resources
Data science involves using scientific analysis, tools, and mathematics to extract useful insight from raw data, and then applying that knowledge for effective business strategy. Increasingly, companies are turning to data science. They can use scientific analysis, tools, and mathematics to extract useful insight from Big Data, to guide their decisions and drive business improvements. In this cour…
Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagn…
Data Analysis is a primary method for deriving valuable insight from raw and unstructured data. The appropriate application of data analysis techniques is vital in deriving only the relevant insight and factual knowledge from available data. Picking the correct data distribution or visualization technique can become critical to the overall data analysis results. Using this course, become familiar …
Proficiency Levels
Novice
- Describes the purpose and structure of standard company data models and dashboards.
- Identifies and defines key data types and sources relevant to the company’s electronics and semiconductor business.
- Lists common data visualization tools and reporting formats used by cross functional teams.
- Explains the company’s data governance policies and their importance for data quality and security.
Intermediate
- Works with data collection and organization for routine business reporting.
- Uses established data models to generate standard reports.
- Follows data governance protocols to ensure accuracy and integrity.
- Participates in populating dashboards with up to date data.
- Assists in communicating simple data findings to team members.
Advanced ★ Required Level
- Evaluates the effectiveness of current data models and recommends improvements to enhance insight generation.
- Monitors data trends to assess the impact of new AI features or sustainability initiatives.
- Advises on solutions to improve data integrity across business units.
- Designs processes to analyze complex datasets to uncover patterns and correlations that inform product development or market strategy.
- Trains others on how to assess the reliability of external data sources before integrating them into company analyses.
- Oversees collaboration with cross functional teams to clarify data requirements and ensure analytical outputs meet business needs.
Expert
- Leads the creation of custom analytical frameworks to evaluate emerging market opportunities.
- Designs and implements advanced data models tailored to support innovation in AI integrated devices.
- Establishes new data visualization standards that improve clarity and stakeholder engagement.
- Develops interactive dashboards that enable real time monitoring of sustainability metrics and business performance.
- Demonstrates expertise in integrating multiple data sources to build comprehensive reports that inform executive decision making.
- Creates cross departmental projects to optimize data driven processes and enhance organizational efficiency.
SQL (Structured Query Language) is a critical skill for a Data Analyst, enabling the efficient extraction, manipulation, and management of data from relational databases. Proficiency in SQL allows the analyst to query large datasets, join tables, and perform complex aggregations to uncover actionable insights that drive strategic decision-making. In the context of this role, SQL expertise is essential for analyzing data related to AI innovation, sustainability, and market trends, ensuring alignment with the company’s objectives. Additionally, SQL supports the creation of accurate and dynamic data visualizations and reports, facilitating collaboration with cross-functional teams to optimize processes and enhance organizational performance. Mastery of SQL ensures the ability to handle data with precision and reliability, contributing to the company’s mission of innovation and sustainability.
Learning Resources
Manipulating databases is a necessary skill. Explore Structured Query Language (SQL) and dive into the architecture. Discover efficient and easily manageable databases using features like SELECT, data types, UPDATE, and ORDER BY.
Structured query language (SQL) is a powerful query language designed for managing and manipulating relational databases. Its declarative nature allows users to interact with databases by specifying the desired result, leaving the system to determine the optimal method of execution. Begin this course with an introduction to SQL, including the features of SQL and how and where SQL is used. Then, yo…
SQL is the main query language used by most DBMSs. Learn how to use SQL to manage data, how transactions and concurrency control ensure data integrity and consistency, and how security is implemented in a relational database.
Proficiency Levels
Novice
- Describes the basic structure of relational databases used within the organization.
- Identifies common SQL commands and explains their functions in the context of data extraction.
- Explains the importance of data quality, integrity, and security when using SQL for data analysis.
- Lists the types of business data relevant to AI innovation, sustainability, and market trends.
Intermediate ★ Required Level
- Uses SQL to extract data from company databases for routine business reports.
- Follows established data governance protocols when accessing and manipulating data using SQL.
- Performs data validation checks in SQL to ensure accuracy and integrity of extracted data.
- Works with SQL filters and aggregations to summarize data for standard dashboards and visualizations.
- Participates in generating routine data reports using SQL to support ongoing business initiatives in AI and sustainability.
Advanced
- Evaluates the effectiveness of existing SQL queries and optimizes them for improved performance and accuracy.
- Monitors the impact of data driven insights generated through SQL on business decision making processes.
- Advises on solutions to enhance data integrity after analyzing data quality issues using SQL.
- Designs complex SQL queries to identify trends and patterns in large, multifaceted datasets.
- Trains others on how to use SQL to compare and evaluate different data sources for consistency and reliability.
- Oversees the interpretation of SQL based findings and their alignment with strategic objectives in collaboration with cross functional teams.
Expert
- Leads the integration of new data sources into existing SQL databases to expand analytical capabilities.
- Designs SQL based solutions to address emerging business challenges in market leadership and process optimization.
- Establishes best practices for SQL usage within the team to ensure high standards of data quality and security.
- Develops dynamic, interactive dashboards and reports using SQL driven data pipelines for executive stakeholders.
- Demonstrates advanced SQL skills to support innovative AI and sustainability initiatives.
- Creates automated SQL scripts to streamline data collection, processing, and reporting workflows.
Python (Programming Language) is a critical skill for the Data Analyst role, enabling the efficient manipulation, analysis, and visualization of complex datasets. Proficiency in Python allows for the development of advanced analytical models, automation of data processing workflows, and integration with machine learning libraries to uncover trends and insights that align with the company’s strategic objectives, such as AI innovation and sustainability. This skill supports the creation of dynamic, data-driven visualizations and reports that enhance decision-making across cross-functional teams. Mastery of Python ensures accuracy, scalability, and the ability to translate raw data into actionable outcomes that drive organizational performance and contribute to the company’s mission of enriching lives through technology and sustainability.
Learning Resources
Python is a general-purpose programming language used for web development, machine learning, game development, and education. It is known for its simplicity, readability, and large community of users and resources. Begin this course by exploring Python with the help of AI tools like ChatGPT, focusing on the importance of prompt engineering. You will install Python using the Anaconda distribution a…
Python is a powerful programming language for data science, and pandas is a popular open-source data manipulation and analysis library in Python. Combined with prompt engineering techniques, working with data in Python is easy and intuitive, which allows you to be more productive and efficient. You will start this course by leveraging prompt engineering to work with pandas. You will explore librar…
Data that lives in a database is only part of the equation when considering data analytics. Data needs to be accessed and processed in order to be useful. The importance of data in the modern world can easily be observed by considering the sheer number of data analytics tools. Without these tools, data loses some of its usefulness. In this course, you will explore popular data analytics tools, beg…
Proficiency Levels
Novice
- Describes the basic syntax and data structures of Python relevant to data analysis, such as lists, dictionaries, and data frames.
- Names common Python libraries used in data analysis, including pandas, numpy, and matplotlib, and explains their primary functions.
- Lists the steps involved in importing, cleaning, and preparing datasets using Python.
- Explains the process of loading data from various sources (CSV, Excel, databases) into Python for analysis.
Intermediate ★ Required Level
- Uses Python to import, clean, and preprocess raw datasets in preparation for analysis, ensuring data quality and consistency.
- Applies Python libraries to perform basic statistical analysis and generate summary statistics for business reports.
- Develops and executes Python scripts to automate routine data collection and processing tasks for recurring business needs.
- Creates basic data visualizations (bar charts, line graphs, histograms) in Python to support stakeholder presentations.
- Utilizes Python to merge, filter, and transform datasets according to defined business requirements.
Advanced
- Advises on the use of Python for complex data analysis and decision making processes.
- Designs advanced Python scripts to automate data processing workflows, enhancing efficiency and accuracy.
- Evaluates the effectiveness of Python based data models and analytical approaches for specific business needs.
- Monitors data quality and integrity by implementing comprehensive validation routines in Python.
- Oversees the optimization of existing Python code for performance, scalability, and compliance with data governance.
- Trains others in using Python for exploratory data analysis and generating actionable insights.
Expert
- Leads the integration of Python with machine learning libraries to build predictive models that address emerging business opportunities.
- Designs and implements robust, reusable Python based data models and pipelines that support strategic business initiatives such as AI innovation and sustainability.
- Creates custom Python modules and tools that streamline data analysis processes across multiple business units.
- Develops interactive dashboards and advanced visualizations in Python to communicate complex insights to diverse stakeholders.
- Establishes scalable Python solutions for real time data processing and reporting to support enterprise wide decision making.
- Demonstrates new methods in Python for automating data governance and ensuring compliance with organizational standards.
Dashboard expertise involves the ability to design, develop, and maintain dynamic and visually compelling dashboards that effectively communicate complex data insights to diverse stakeholders. This skill requires proficiency in leveraging advanced data visualization tools and techniques to transform raw data into actionable insights, aligning with the organization’s strategic objectives such as AI innovation, sustainability, and market leadership. A strong focus on accuracy, clarity, and user-centric design is essential to ensure dashboards provide meaningful, real-time insights that drive informed decision-making across cross-functional teams. Mastery of this skill enables the Data Analyst to optimize processes, enhance performance, and contribute to the company’s mission of enriching lives and fostering a sustainable future.
Learning Resources
Data visualization prepares data for presentation to make it easier for non-data analysts to understand what the data is saying. Often, non-data analysts can't see relationships in data without visual aids, but effective data presentation allows them to change parameters and understand and view the data in a similar manner to a data scientist. In this course, explore the purpose and key considerat…
Explore the role played by dashboards in data exploration and deep analytics in this 11-video course. Dashboards are especially useful in visualizing data for a wide variety of business users, so that they can better understand the data being analyzed. First, learners examine the essential patterns of dashboard design and how to implement appropriate dashboards by using Kibana, Tableau, and Qlikvi…
Google Analytics allows you stay on top of your website's audience and activity. Dashboards are important as they let you monitor metrics and visualize data. During this course you will learn how to create a dashboard to let you quickly review reports, metrics, account health, and report correlations. Learn to create a custom blank dashboard to optimize your experience with custom widgets, filters…
Proficiency Levels
Novice
- Explains the purpose and benefits of using dashboards for data visualization and decision making.
- Identifies key features and components of dashboards used within the organization.
- Describes the process for accessing and navigating existing dashboards relevant to business units.
- Lists common data visualization types and their appropriate use cases in dashboards.
Intermediate
- Uses standard tools and techniques to create and update dashboards for a specific business function.
- Follows established procedures to ensure data quality and security when developing dashboards.
- Performs basic dashboard development tasks with minimal supervision.
- Explains the purpose and use of dashboards for a specific business function.
- Assists in the development of dashboards for a specific business function.
Advanced ★ Required Level
- Evaluates the effectiveness of dashboards in communicating actionable insights to stakeholders.
- Monitors gaps or inconsistencies in dashboard data and recommends improvements to enhance accuracy.
- Advises on dashboard usability and relevance for different business units.
- Designs dashboards that integrate multiple data sources to provide comprehensive views of business performance.
- Trains others on the impact of dashboard insights on business decision making and process optimization.
- Oversees the diagnosis and resolution of issues related to data integrity or visualization errors in dashboards.
Expert
- Leads cross functional workshops to gather requirements and co create dashboards that address complex business challenges.
- Designs and develops advanced, interactive dashboards tailored to support strategic initiatives such as AI innovation or sustainability.
- Establishes best practices for dashboard development and documentation to ensure consistency and scalability.
- Develops real time dashboards that enable proactive monitoring of key performance indicators across business units.
- Demonstrates user centric design principles to enhance dashboard accessibility and engagement for diverse stakeholders.
- Creates dashboards to visualize predictive analytics and scenario modeling for strategic planning.
Power BI is a critical skill for the Data Analyst role, enabling the creation of dynamic and visually compelling dashboards and reports that translate complex datasets into actionable insights. This skill involves proficiency in data modeling, DAX (Data Analysis Expressions), and integrating diverse data sources to support strategic decision-making. In the context of the company’s focus on innovation, sustainability, and market leadership, Power BI empowers analysts to identify trends, patterns, and opportunities that align with organizational objectives. Mastery of Power BI ensures the ability to effectively communicate data-driven narratives to cross-functional teams, driving performance optimization and supporting the company’s mission of enriching lives and fostering a sustainable future.
Learning Resources
Business intelligence (BI) has become a cornerstone for organizations aiming to make data-driven decisions, and Power BI is one of the leading tools in the BI space. In this course, explore foundational BI concepts and discover how to install and configure Power BI for effective data analysis. Next, learn how to transform raw data into actionable insights by creating visually compelling reports an…
Microsoft Power BI is a powerful and versatile visualization technology widely used in data analytics, especially business data analysis. Business analysts can use this service to build and publish interactive reports for executive audiences as well as collaborators. Get your head around the specifics of data visualization in this introductory course. Explore different types of visualizations and …
Most businesses have an enormous amount of data in their possession. But this data is only as valuable as the quality of the processes used to understand it. So how do you gather seemingly disparate data from multiple sources and turn it into digestible insights for all to use? One way to do this is to use Power BI, Microsoft's business analytics service. Use this course to comprehend exactly what…
Proficiency Levels
Novice
- Describes the basic components and interface elements of Power BI, including dashboards, reports, and datasets.
- Explains the purpose of data visualization in supporting business decision making at the company.
- Lists key data governance and security principles relevant to handling company data in Power BI.
- Names the types of data sources compatible with Power BI used within the company’s analytics ecosystem.
Intermediate
- Uses Power BI to create basic dashboards and reports for routine business reporting.
- Follows data governance policies when sharing Power BI reports with internal teams.
- Works with Power BI’s basic DAX functions to perform calculations such as year over year growth.
- Participates in integrating multiple data sources into a Power BI report to provide a consolidated view of business performance.
- Assists in applying data filters and slicers in Power BI to allow stakeholders to interact with and explore data relevant to their functions.
Advanced ★ Required Level
- Evaluates the effectiveness of business initiatives by comparing pre and post implementation metrics using Power BI dashboards.
- Monitors the performance of sustainability initiatives by visualizing and interpreting relevant KPIs in Power BI.
- Advises on the use of advanced DAX expressions to uncover hidden patterns in semiconductor manufacturing data.
- Designs complex datasets in Power BI to identify emerging market trends and evaluate their impact on product performance.
- Trains others on diagnosing data quality issues within Power BI reports and recommending corrective actions to ensure data integrity.
- Oversees cross functional teams to interpret Power BI insights and provide recommendations for process optimization.
Expert
- Leads the development of Power BI dashboards that integrate AI driven analytics to support strategic decision making.
- Designs custom data models in Power BI that align with the company’s sustainability and innovation objectives.
- Establishes interactive Power BI reports tailored for executive leadership to monitor global market performance.
- Develops new visualization techniques in Power BI to communicate complex data narratives to diverse stakeholders.
- Demonstrates how to automate recurring business reports in Power BI to improve efficiency and reduce manual effort across teams.
- Creates and documents Power BI best practices for use within the data analytics function.
Proficiency in Tableau is essential for the Data Analyst role, enabling the creation of dynamic and visually compelling dashboards and reports that translate complex datasets into actionable insights. This skill involves leveraging Tableau's advanced features to identify trends, patterns, and opportunities that align with the company’s strategic objectives, such as driving innovation in AI, promoting sustainability, and maintaining market leadership in technology. Expertise in Tableau supports collaboration with cross-functional teams by delivering clear, data-driven visualizations that optimize decision-making and enhance business performance. Mastery of this tool ensures the ability to communicate data findings effectively, contributing to the organization’s mission of enriching lives and fostering a sustainable future.
Learning Resources
Tableau is a data visualization tool suitable for a variety of purposes and situations. Knowing the basics of this tool will help you share necessary data with stakeholders and peers in a meaningful and engaging way. This course will introduce you to Tableau's basic features and cover the fundamental operations performed with this tool. You'll start by loading data into Tableau using a variety of …
Generally speaking, the ultimate goal of analyzing data is to be able to communicate any interesting relationships that you have found. In this 10-video course, learners will discover how Tableau's analysis tools can further data analysis and communicate those relationships. Share your findings by observing how to create dashboards with interactive tools and exploring data. Begin by using Explain …
To become a data science expert, you must master the art of data visualization. This 12-video course explores how to create and use real time dashboards with Tableau. Begin with an introduction to real-time dashboards and differences between real time and streaming data. Next, take a look at different cloud data sources. Learn how to build a dashboard in Tableau and update it in real time. Discove…
Proficiency Levels
Novice
- Describes the core features and functions of Tableau relevant to data analysis in the technology sector.
- Identifies the types of data sources commonly integrated into Tableau dashboards for electronics and semiconductor analytics.
- Explains the importance of data visualization in supporting business decision making at a global technology enterprise.
- Lists the steps required to connect Tableau to internal company databases while adhering to data governance policies.
Intermediate
- Uses Tableau to generate routine reports on supply chain performance, ensuring data accuracy and clarity for stakeholders.
- Works with Tableau to build basic dashboards to visualize sales trends for a specific product line, such as smartphones or home appliances.
- Performs data segmentation by region, product, or customer type using Tableau filters and parameters for regular business reviews.
- Assists in integrating multiple data sources in Tableau to create consolidated views of market performance for cross functional teams.
- Follows Tableau’s built in security features to ensure sensitive business data is protected in routine reporting scenarios.
Advanced ★ Required Level
- Evaluates the effectiveness of existing Tableau dashboards in communicating actionable insights to executive leadership.
- Monitors data quality and integrity within Tableau visualizations, identifying and resolving inconsistencies or anomalies.
- Advises on the use of advanced Tableau calculations to uncover patterns in semiconductor production efficiency and recommend process optimizations.
- Designs complex datasets in Tableau to identify emerging market trends and evaluate their impact on business strategy.
- Trains cross functional teams to interpret Tableau findings and provide data driven recommendations for product innovation.
- Oversees multiple Tableau dashboards to determine which best supports decision making for AI integrated device initiatives.
Expert
- Leads the development of interactive Tableau reports that enable real time monitoring of sustainability metrics across global operations.
- Designs and implements advanced Tableau dashboards that integrate AI driven analytics to support strategic business initiatives.
- Creates custom Tableau data models tailored to the needs of different business units, such as R&D, marketing, or supply chain.
- Develops innovative Tableau visualizations that communicate complex data stories to diverse stakeholders, driving engagement and understanding.
- Establishes best practices for Tableau dashboard design and data governance, ensuring consistency and scalability across the organization.
- Initiates and manages cross functional projects to deploy Tableau solutions that optimize business processes and enhance performance.
Data Visualization is the ability to transform complex datasets into clear, compelling visual representations that facilitate understanding and decision-making. This skill involves selecting appropriate visualization techniques, tools, and frameworks to highlight trends, patterns, and insights that align with organizational objectives, such as innovation in AI, sustainability, and market leadership. Proficiency in data visualization enables effective communication of analytical findings to diverse stakeholders, ensuring that data-driven insights are actionable and aligned with strategic goals. It requires a combination of technical expertise, creativity, and a deep understanding of the audience to present data in a way that drives informed decisions and supports the company’s mission of enriching lives and creating a sustainable future.
Learning Resources
The business world is full of data. Using this data strategically presents a company with serious competitive advantages. The tricky part is making sense of the data and then communicating what it means. Data visualization (or data viz, for short) provides actionable insights out of what can be complex sets of data. In this course, you'll learn the considerations for creating data visualizations a…
In today's data-driven world, mastering data visualization is essential for driving digital transformation and making well-informed decisions. In this course, you'll gain the skills needed to create clear, accurate, and impactful visual representations of data. In this course, you'll learn about the importance and types of data visualization and how it supports informed decision-making. You'll als…
Using data visualizations effectively and correctly is a part of building a data-driven culture in your team. Data visualization creates accessible, understandable, and effective graphic representations of data to help teams understand the patterns and trends in their data and make data-driven decisions. In this course, you will learn about the fundamentals of data visualization, why it is importa…
Proficiency Levels
Novice
- Describes the primary data visualization tools and platforms used within the organization.
- Explains how data visualization supports decision making in the context of AI innovation and sustainability initiatives.
- Identifies and describes common data visualization types (e.g., bar charts, line graphs, scatter plots) used in business analytics.
- Lists the key components of a well structured dashboard and explains their purposes.
Intermediate
- Uses basic data visualization tools to create charts and graphs for routine business metrics.
- Follows established procedures to ensure data visualizations meet quality and security standards.
- Assists in providing visual summaries of key performance indicators for ongoing projects.
- Performs data formatting and labeling to ensure clarity and consistency in visual reports.
- Participates in populating dashboards with updated data for business reviews and meetings.
Advanced ★ Required Level
- Evaluates the effectiveness of existing dashboards and recommends improvements to enhance stakeholder understanding.
- Monitors the clarity and impact of visual reports by gathering feedback from stakeholders and iterating on design.
- Advises on the selection and implementation of the most effective visualization techniques to highlight trends and patterns in complex datasets aligned with strategic objectives.
- Designs data visualizations to identify anomalies or outliers that may impact business decisions.
- Trains others to interpret visualized data to provide actionable insights for cross functional teams working on AI, sustainability, or market leadership initiatives.
- Oversees the diagnosis and resolution of inconsistencies or inaccuracies in data visualizations to maintain data integrity.
Expert
- Leads the creation of interactive visualizations that enable stakeholders to explore data relevant to innovation and sustainability goals.
- Designs and develops custom dashboards that integrate multiple data sources to support strategic business initiatives.
- Establishes visualization frameworks that standardize reporting across business units while allowing for tailored insights.
- Develops visual storytelling approaches that effectively communicate complex analytical findings to executive leadership.
- Demonstrates collaboration with IT and analytics teams to implement advanced visualization features, such as real time data updates or predictive analytics displays.
- Creates and manages projects to enhance the organization’s data visualization capabilities in support of new product launches or market expansions.
Data Science is the ability to extract meaningful insights and drive decision-making through the analysis of complex datasets using advanced statistical, analytical, and machine learning techniques. This skill involves proficiency in data manipulation, pattern recognition, and predictive modeling to identify trends and opportunities that align with organizational objectives, such as innovation in AI, sustainability, and market leadership. It also encompasses expertise in data visualization and storytelling to effectively communicate findings to diverse stakeholders, enabling actionable outcomes. Mastery of Data Science requires a strong focus on accuracy, problem-solving, and the ability to translate raw data into strategic insights that support business growth and innovation.
Learning Resources
Data science involves using scientific analysis, tools, and mathematics to extract useful insight from raw data, and then applying that knowledge for effective business strategy. Increasingly, companies are turning to data science. They can use scientific analysis, tools, and mathematics to extract useful insight from Big Data, to guide their decisions and drive business improvements. In this cour…
Data mining and data science are rapidly transforming decision-making business practices. For these activities to be worthwhile, raw data needs to be transformed into insights relevant to your business's goals. In this course, you'll walk through each stage of the data mining pipeline covering all requirements for reaching a conclusive and relevant business decision. You'll examine data preparatio…
Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagn…
Proficiency Levels
Novice
- Describes the basic steps involved in collecting, processing, and analyzing data within the company’s data governance framework.
- Identifies common data types, sources, and structures used in the organization’s analytics projects.
- Explains the importance of data quality, integrity, and security in supporting business decision making.
- Cites the primary business objectives that data analysis supports, such as AI innovation and sustainability.
Intermediate
- Works with data from multiple sources to support routine business reporting.
- Uses company approved tools to visualize data trends and present findings to immediate team members.
- Follows data governance policies when handling sensitive or proprietary information.
- Participates in developing basic data models and generating standard reports or dashboards for cross functional teams.
- Assists in supporting business initiatives by providing routine data driven insights to inform operational decisions.
Advanced ★ Required Level
- Monitors the effectiveness of data models and recommends improvements to enhance insight generation.
- Advises on the impact of data driven recommendations on business performance and strategic initiatives.
- Designs processes to diagnose data quality issues and implement corrective actions to maintain high standards of integrity.
- Oversees the analysis of complex datasets to identify emerging trends and patterns that impact strategic objectives.
- Trains others on how to critically review and validate data visualizations to ensure clarity and relevance for diverse stakeholders.
- Evaluates data requirements and tailors analyses to specific business needs.
Expert
- Leads the development of advanced predictive models to support innovation in AI integrated products and services.
- Designs new data visualization frameworks that enhance communication of complex insights to executive leadership.
- Establishes scalable data pipelines and reporting systems that align with sustainability and market leadership goals.
- Develops data analysis methodologies to uncover opportunities in emerging markets or technologies.
- Demonstrates the ability to integrate external and internal data sources to generate holistic insights supporting strategic decision making.
- Creates cross functional analytics projects that drive process optimization and business transformation.
Statistics is a foundational skill for the Data Analyst role, enabling the rigorous analysis of complex datasets to uncover trends, patterns, and actionable insights. This skill involves a deep understanding of statistical methods, including descriptive and inferential statistics, probability distributions, hypothesis testing, regression analysis, and predictive modeling. Proficiency in statistics ensures the ability to validate data accuracy, identify meaningful correlations, and support data-driven decision-making that aligns with the company’s strategic objectives, such as advancing AI innovation, sustainability, and market leadership. A strong command of statistical techniques is essential for translating raw data into impactful visualizations and reports that drive business performance and optimize processes.
Learning Resources
A fundamental understanding of statistical analysis methods increases your ability to effectively communicate with data analyst professionals so you can better employ analytics associated with your work. In this course, you'll learn fundamental concepts in distribution, deviation, correlation, regression, and clustering statistical analysis methods. This course was developed with subject matter pr…
Statistics is a branch of mathematics that involves the collection, analysis, interpretation, presentation, and organization of data. It provides a framework for making inferences and drawing generalizable conclusions from observed information and it offers great tools to uncover patterns, trends, and relationships within datasets. Begin this course by exploring two important types of statistics -…
Unlike descriptive statistics, which describe characteristics of datasets, inferential statistics are used to make inferences or predictions about data. Information not readily evident by analyzing datasets can be gleaned by talented data analysts in order to form conclusions, and while it may sound like educated guessing, descriptive statistics are far more sophisticated than simply guessing. In …
Proficiency Levels
Novice
- Describes the role of descriptive and inferential statistics in supporting business decision making at the company.
- Identifies and defines key statistical concepts such as mean, median, mode, standard deviation, and probability distributions relevant to company datasets.
- Explains how statistical methods contribute to the company’s strategic objectives, such as AI innovation and sustainability.
- Recognizes the importance of data quality and integrity in statistical analysis within the context of technology and electronics data.
Intermediate
- Uses statistical software to perform basic analyses on datasets related to product performance or customer feedback.
- Follows established procedures to ensure accuracy and reliability of statistical analyses in routine reporting.
- Participates in discussions to interpret statistical findings and their implications for business decisions.
- Performs basic hypothesis tests to compare performance metrics across different business units or product lines.
- Works with simple data visualizations to communicate statistical findings to cross functional teams.
Advanced ★ Required Level
- Evaluates the effectiveness of AI driven features by applying advanced statistical tests to user engagement and satisfaction data.
- Monitors the impact of sustainability initiatives by comparing pre and post implementation metrics using inferential statistics.
- Advises on factors influencing product adoption in global markets by interpreting multivariate regression results.
- Designs statistical models to uncover hidden patterns and correlations in complex datasets that inform strategic business decisions.
- Trains others to diagnose anomalies or outliers in manufacturing or supply chain data and recommend corrective actions based on statistical evidence.
- Oversees data models and reports for statistical validity and alignment with business objectives.
Expert
- Leads the design and implementation of advanced statistical models to predict market trends and guide product development strategies.
- Designs innovative data visualization dashboards that integrate statistical insights for executive decision making.
- Establishes standardized statistical analysis frameworks for evaluating new business initiatives across divisions.
- Develops predictive models to optimize inventory management and reduce supply chain risks.
- Demonstrates expertise in integrating statistical methodologies into AI and machine learning projects to enhance product features and user experiences.
- Creates statistical measurement systems for tracking progress toward environmental goals.
Problem Solving is the ability to approach complex challenges methodically, identify root causes, and develop innovative, data-driven solutions that align with organizational objectives. For a Data Analyst, this skill involves analyzing intricate datasets to uncover trends, patterns, and actionable insights that support strategic goals such as advancing AI innovation, enhancing sustainability, and maintaining market leadership in technology. It requires critical thinking, adaptability, and collaboration with cross-functional teams to address business needs, optimize processes, and drive impactful outcomes. Effective problem-solving in this role ensures the delivery of accurate, meaningful data interpretations that contribute to informed decision-making and the company’s mission of enriching lives and creating a sustainable future.
Learning Resources
For most business professionals, solving problems is a major part of their jobs. But what are often seen as problems are really symptoms of deeper challenges. Treating these symptoms may provide temporary relief, but the deeper issues remain. By investigating the scope of problems and addressing their root causes, you can work toward ultimately finding solutions for sustained success. In this cour…
Almost any business problem can have more than one solution, but some solutions are better than others. And one of those will be the best solution. Trying to find the best, right solution to a business problem can itself be a problem. Finding that right solution means applying the right process and methods to meet your organization's needs now, and in the future. In this course, you'll explore app…
Solving problems requires strategic thinking. You need to ask the right people the right questions to get to the source of the issue and find the solution. In this course, you'll learn about identifying stakeholders, gaining their input and trust, and using analysis techniques to get to the root cause of a problem.
Proficiency Levels
Novice
- Describes the purpose of data models, reports, and dashboards in supporting business initiatives.
- Explains the importance of data driven insights in maintaining market leadership and supporting cross functional teams.
- Identifies basic data types and sources relevant to the company’s electronics and semiconductor business.
- Lists standard data governance policies and best practices used within the organization.
Intermediate
- Works with data sets to support business reporting for consumer electronics or semiconductor operations.
- Uses standard data visualization tools to create basic dashboards that communicate trends to stakeholders.
- Follows established data validation techniques to ensure accuracy and integrity in daily data analysis tasks.
- Participates in data governance procedures when handling sensitive or proprietary information.
- Assists business units by providing timely, actionable insights from structured data sources.
Advanced ★ Required Level
- Evaluates the effectiveness of current data models and recommends improvements to enhance reporting accuracy.
- Monitors the impact of business process changes on data integrity and reporting outcomes.
- Advises cross functional teams on optimizing processes for sustainability or AI initiatives.
- Designs solutions to address data quality issues.
- Trains others on how to analyze complex datasets to identify emerging trends and patterns that impact product innovation or market positioning.
- Oversees the validation of insights before presenting them to senior stakeholders to support strategic decisions.
Expert
- Leads the design of advanced data models and dashboards tailored to new business initiatives in digital health or smart home sectors.
- Develops innovative analytical approaches to uncover opportunities for process optimization and cost reduction.
- Leads cross functional workshops to solve complex business challenges using data driven methodologies.
- Creates frameworks for integrating new data sources, such as IoT device data, into existing analytics systems.
- Initiates and manages pilot projects that leverage AI driven analytics to support sustainability goals.
- Develops and documents best practices for advanced problem solving techniques in data analysis.
Detail-oriented individuals possess a meticulous approach to analyzing and interpreting data, ensuring accuracy and precision in every aspect of their work. For a Data Analyst in a leading technology enterprise, this skill is critical in validating complex datasets, identifying subtle patterns, and delivering insights that align with strategic objectives such as AI innovation, sustainability, and market leadership. Being detail-oriented enables the analyst to detect anomalies, minimize errors, and maintain the integrity of data visualizations and reports, which are essential for driving informed decision-making. This skill also supports collaboration with cross-functional teams by ensuring that all data-driven recommendations are reliable, actionable, and aligned with the company’s mission of enriching lives and fostering a sustainable future.
Learning Resources
Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagn…
Dealing with large amounts of data is essential to any modern business and to become a data-driven organization, leaders and decision-makers must establish a deeply ingrained data culture. Use this course to understand the underlying principles of analyzing data and get familiar with terms related to data in order to properly deliver data-related projects. This course will help you identify the ba…
As industries, enterprises, and jobs become more data-intensive, data literacy is critical to effectively "talk data" with business colleagues and data and analytics professionals who support you in your work. This course covers fundamental concepts in data management, data quality, data privacy and protection, and data governance. This course was developed with subject matter provided by the Inte…
Proficiency Levels
Novice
- Describes the basic steps involved in collecting, processing, and analyzing organizational data.
- Identifies key data sources relevant to business decision making within the organization.
- Explains the significance of accuracy and precision when interpreting data for business insights.
- Lists common data quality issues and basic methods for ensuring data accuracy.
Intermediate
- Works with data entry and validation tasks, ensuring accuracy and adherence to procedures.
- Uses standard tools to create basic data visualizations for reports.
- Follows data governance protocols when handling sensitive information.
- Assists in providing precise data extracts for routine business questions.
- Detects and corrects simple data anomalies in daily processing tasks.
Advanced ★ Required Level
- Evaluates complex datasets to identify subtle trends, patterns, or anomalies that impact business outcomes.
- Monitors the accuracy and completeness of data models and recommends improvements to enhance reliability.
- Advises on the effectiveness of current data visualization methods and suggests refinements for clarity.
- Designs solutions to address root causes of recurring data quality issues.
- Trains others on how to review and validate data driven insights before presentation to ensure alignment with strategic objectives.
- Oversees cross functional teams to interpret nuanced data findings and evaluate their business implications.
Expert
- Leads the design and implementation of advanced data validation frameworks to proactively ensure data integrity across projects.
- Develops innovative reporting templates that highlight critical details for executive decision making.
- Creates comprehensive data quality monitoring systems tailored to evolving business needs.
- Designs robust models that integrate new data sources and support AI innovation and sustainability initiatives.
- Establishes best practices for detailed data analysis and visualization within the team.
- Demonstrates attention to detail by initiating and driving cross functional workshops to enhance collective attention to detail in data driven projects.
Proficiency in Microsoft Excel is essential for the Data Analyst role, enabling the efficient organization, analysis, and visualization of complex datasets. This skill involves advanced knowledge of Excel functions, such as pivot tables, VLOOKUP, INDEX-MATCH, and data analysis tools, to identify trends, patterns, and actionable insights that align with the company’s strategic objectives. Expertise in Excel also supports the creation of dynamic dashboards and reports, facilitating clear communication of data-driven findings to cross-functional teams. Mastery of this tool ensures accuracy, enhances problem-solving capabilities, and contributes to optimizing processes and driving innovation in alignment with the company’s mission and sustainability goals.
Learning Resources
Analyze data efficiently using Excel 2013. Discover how to work with PivotTables, including formatting, sorting, filtering, grouping, and using slicers. In addition, explore how to insert, modify, and analyze using PivotCharts.
Excel is not only used for aggregating and analyzing data, but also for data cleansing. There are several data cleaning, validation, and checking techniques available, some of which are among Excel's most well-known and widely-used functions. In this course, you'll start by using various string and data manipulation functions to clean data and fill in missing values. Next, you'll perform simple da…
Excel 365 offers a variety of advanced functions for data management and formula creation. In this course, discover how to build formulas to find information in tables, work with data arrays, and obtain date-related information. First, enhance your data management and analysis skills with powerful lookup functions such as VLOOKUP, HLOOKUP, and the newly introduced XLOOKUP. These tools are crucial …
Proficiency Levels
Novice
- Describes the process of importing raw data into Excel from internal company sources.
- Explains the difference between data types (e.g., text, numbers, dates) in Excel and their impact on analysis.
- Identifies and defines key Excel functions such as SUM, AVERAGE, and COUNT relevant to basic data analysis.
- Lists the steps to create simple charts (e.g., bar, line) to visualize basic trends in product performance.
Intermediate
- Uses Excel to create and update standardized templates for recurring business reports.
- Performs data analysis using Excel functions like VLOOKUP or INDEX MATCH to merge datasets.
- Works with Excel to generate routine performance dashboards for tracking metrics.
- Assists in summarizing data using pivot tables and shares findings with the team.
- Follows established procedures to apply conditional formatting for highlighting data anomalies.
Advanced ★ Required Level
- Evaluates the effectiveness of a recent marketing campaign by comparing pre and post campaign data in Excel.
- Monitors multi year sales trends using advanced Excel functions to identify emerging market opportunities.
- Advises on data inconsistencies across multiple sources and recommends corrective actions using Excel tools.
- Designs models to assess the impact of supply chain disruptions using Excel’s data analysis features.
- Trains others to interpret complex data relationships using nested formulas and present actionable insights to stakeholders.
- Oversees the auditing and validation of data models to ensure compliance with company data governance standards.
Expert
- Leads the creation of a standardized Excel based reporting system for sustainability initiatives.
- Designs dynamic Excel dashboards that integrate real time data feeds for executive decision making.
- Develops custom Excel macros to automate repetitive data processing tasks for cross functional teams.
- Creates advanced forecasting models in Excel to predict demand for new technology products.
- Demonstrates new methods for visualizing AI driven insights in Excel to support product development teams.
- Establishes processes to integrate Excel analytics with other business intelligence platforms.
Computer Science is a foundational skill for the Data Analyst role, encompassing expertise in algorithms, data structures, programming languages, and computational problem-solving. This skill enables the effective manipulation, analysis, and interpretation of large datasets, leveraging advanced techniques to uncover trends and insights that drive strategic decision-making. Proficiency in computer science supports the development and application of data models, machine learning algorithms, and automation tools, aligning with the company’s focus on AI innovation and technological leadership. A strong grounding in this skill ensures the ability to design efficient, scalable solutions that contribute to optimizing processes, enhancing performance, and achieving the organization’s sustainability and market leadership goals.
Learning Resources
Data science involves using scientific analysis, tools, and mathematics to extract useful insight from raw data, and then applying that knowledge for effective business strategy. Increasingly, companies are turning to data science. They can use scientific analysis, tools, and mathematics to extract useful insight from Big Data, to guide their decisions and drive business improvements. In this cour…
Dealing with large amounts of data is essential to any modern business and to become a data-driven organization, leaders and decision-makers must establish a deeply ingrained data culture. Use this course to understand the underlying principles of analyzing data and get familiar with terms related to data in order to properly deliver data-related projects. This course will help you identify the ba…
Data is rarely received in perfect form and often requires some sort of manipulation to make it sing. That is why the world needs data analysts. They can squeeze every bit of usefulness from datasets, and they also know how to prep datasets to extract meaning from them. In this course, you will explore key concepts of data manipulation, beginning with data manipulation tools. Then you will learn o…
Proficiency Levels
Novice
- Describes the basic components of a data model and their functions within business analytics.
- Explains the process of data collection, processing, and storage using foundational computer science principles.
- Lists common data visualization techniques and tools used to communicate analytical findings.
- Names key computer science concepts such as algorithms, data structures, and programming languages relevant to data analysis.
Intermediate
- Works with basic data structures and algorithms to organize and manipulate datasets for analysis and reporting.
- Uses data visualization tools to create standard dashboards that communicate key metrics to cross functional teams.
- Performs data quality checks to ensure accuracy and integrity in routine data analysis tasks.
- Participates in the development and maintenance of basic data models to support routine business reporting requirements.
- Follows standard algorithms to clean, filter, and transform raw data into usable formats for business insights.
Advanced ★ Required Level
- Evaluates the effectiveness of existing data models and recommends improvements to enhance analytical outcomes.
- Monitors the performance of data processing scripts and optimizes them for efficiency and scalability.
- Advises on the interpretation of analytical results and provides evidence based recommendations to cross functional teams.
- Designs advanced algorithms to identify trends, patterns, and anomalies that impact business decisions.
- Trains others on diagnosing and resolving data integrity issues by applying computational problem solving techniques.
- Oversees data governance practices and suggests enhancements to strengthen data security and compliance.
Expert
- Leads the design and implementation of advanced data models and machine learning algorithms to support strategic business initiatives such as AI innovation and sustainability.
- Develops custom automation tools that streamline data collection, processing, and reporting across multiple business units.
- Creates interactive dashboards and visualization systems that enable real time decision making for stakeholders.
- Designs scalable data solutions that integrate multiple data sources and support enterprise level analytics.
- Establishes new methods for ensuring data quality and integrity in complex, dynamic environments.
- Demonstrates collaboration with cross functional teams to design and deploy analytical frameworks that drive process optimization and performance enhancement.
Communication is a critical skill for a Data Analyst, enabling effective collaboration with cross-functional teams and stakeholders to drive data-driven decision-making. This skill involves the ability to clearly articulate complex data insights, trends, and recommendations through both verbal and written mediums, ensuring alignment with the company’s strategic objectives such as AI innovation, sustainability, and market leadership. Strong communication also includes proficiency in creating compelling data visualizations and reports that translate technical findings into actionable outcomes for diverse audiences. Mastery of this skill ensures that data insights are not only understood but also leveraged to optimize processes, enhance performance, and contribute to the organization’s mission of enriching lives and fostering a sustainable future.
Learning Resources
The final step in the data science pipeline is to communicate the results or findings. Explore communication and visualization concepts needed by data scientists.
Data is meaningful only when information is extracted from it. That information can tell a story, and the best data analysts are magnificent storytellers. But no matter how accomplished a data analyst, a story can't be told compellingly without visualizing what the data says and a key part of a data analyst's role is in reporting on what the data is saying. In this course, you will explore data vi…
Beyond tools and techniques, business analysts must rely on a strong set of personal skills to be effective in their work. These underlying competencies enable analysts to think critically, collaborate with stakeholders, and communicate clearly across diverse audiences. In this course, explore the personal skills essential to successful business analysis, including analytical thinking, systems and…
Proficiency Levels
Novice
- Describes the process for requesting data or insights from other teams.
- Identifies key data sources and basic terminology used in company data reports and dashboards.
- Explains the importance of data quality, integrity, and security in communication.
- Lists standard data visualization formats commonly used within the organization.
Intermediate
- Works with data visualization tools to present findings to team members.
- Participates in drafting emails or memos summarizing data trends for non technical stakeholders.
- Assists in communicating data requirements to IT or business partners for standard reporting needs.
- Uses company approved dashboards to explain performance metrics to cross functional teams.
- Explains data sources, definitions, and basic trends to stakeholders.
Advanced ★ Required Level
- Evaluates the effectiveness of different data visualization methods for communicating insights.
- Monitors stakeholder feedback to improve the clarity and relevance of data communications.
- Advises on the business implications of complex data trends to diverse audiences.
- Designs discussions with cross functional teams to clarify ambiguous data requirements.
- Trains others to critique and refine reports or presentations to ensure clarity and alignment with strategic objectives.
- Oversees meetings to discuss the impact of data driven recommendations on business processes.
Expert
- Leads the development of new data visualization standards to enhance stakeholder understanding.
- Designs and delivers tailored data presentations that address the unique needs of executive and technical audiences.
- Establishes communication strategies for sharing complex AI or sustainability insights across the organization.
- Develops training materials to help teams interpret and act on advanced data insights.
- Demonstrates the ability to author comprehensive data narratives that connect analytical findings to company mission and strategic goals.
- Creates and leads cross functional workshops to align on data definitions and reporting practices.
Management, as a skill for a Data Analyst, involves the ability to effectively organize, prioritize, and oversee data-related projects to ensure alignment with organizational goals. This includes coordinating with cross-functional teams, managing timelines, and ensuring the delivery of accurate and actionable insights that support strategic objectives such as AI innovation, sustainability, and market leadership. Strong management skills enable the Data Analyst to balance competing priorities, optimize workflows, and foster collaboration to drive data-driven decision-making. Additionally, this skill encompasses the ability to guide and influence stakeholders by presenting data in a clear and impactful manner, ensuring that insights translate into meaningful business outcomes.
Learning Resources
As data and analytics move ever closer to the core of enterprises, it's the contemporary manager's responsibility to both to set an example and to exert their influence to improve business performance and capability through data and analytics. This course covers best practices for encouraging analytics in everyday work, and enabling data and analytics-driven behaviors within the organizational env…
The rapidly growing fields of data analytics and artificial intelligence (AI) offer immense advantages to individuals and society. Nevertheless, there are also challenges related to data management and governance within the context of AI. Begin this course by exploring the practical knowledge and skills necessary for effective data management and governance in the context of AI projects. Discover …
Data science involves using scientific analysis, tools, and mathematics to extract useful insight from raw data, and then applying that knowledge for effective business strategy. Increasingly, companies are turning to data science. They can use scientific analysis, tools, and mathematics to extract useful insight from Big Data, to guide their decisions and drive business improvements. In this cour…
Proficiency Levels
Novice
- Identifies key data sources and their relevance to business objectives such as AI innovation and sustainability.
- Explains the basic steps involved in collecting, processing, and analyzing organizational data.
- Describes the structure and purpose of standard data models, reports, and dashboards used within the company.
- Cites company data governance policies and best practices for ensuring data quality and security.
Intermediate ★ Required Level
- Works with basic data analysis tools to support routine business reporting needs.
- Participates in the development of standard reports for predictable business scenarios.
- Assists in the collection and analysis of data for routine business reports.
- Uses established reporting formats to communicate routine data findings to stakeholders.
- Performs basic data analysis tasks to support cross functional teams.
Advanced
- Evaluates the effectiveness of current data models and recommends improvements to better align with strategic objectives.
- Monitors complex datasets to identify emerging trends and assess their impact on business performance.
- Advises on the quality and reliability of data sources, proposing solutions to address inconsistencies or gaps.
- Designs processes to prioritize multiple data projects based on business impact, resource availability, and alignment with company goals.
- Trains others to facilitate discussions with cross functional teams to clarify data requirements for complex initiatives.
- Oversees the review and refinement of data visualizations to ensure clarity and actionable value for diverse stakeholders.
Expert
- Leads the development of innovative dashboards and reporting systems tailored to strategic initiatives such as AI integration and sustainability.
- Designs and implements advanced data management workflows that optimize collaboration across business units.
- Establishes project management frameworks for large scale data analysis projects, ensuring alignment with organizational priorities.
- Develops and documents best practices for data quality assurance and governance within the analytics team.
- Demonstrates the ability to initiate and manage cross functional projects that leverage data insights to drive process improvements and innovation.
- Creates communication strategies to present complex data findings in impactful ways to executive leadership and non technical stakeholders.
Operations, as a skill for a Data Analyst, involves the ability to design, implement, and optimize data-driven processes that enhance organizational efficiency and decision-making. This skill requires a deep understanding of how to manage and streamline workflows, ensuring that data collection, analysis, and reporting align with the company’s strategic objectives, such as innovation in AI, sustainability, and market leadership. Proficiency in operations enables the Data Analyst to collaborate effectively with cross-functional teams, identify process inefficiencies, and develop solutions that improve performance and outcomes. A strong focus on operational excellence ensures that insights are delivered accurately, timely, and in a manner that supports the company’s mission of enriching lives and driving a sustainable future.
Learning Resources
DataOps processes help to manage data analytics and processing. It combines DevOps teams with other data roles to supply processes, tools, and organizational structures to support enterprises. Through this course, learn DataOps guiding principles and how to implement them properly in an organization. Explore DataOps platforms and tools, key DataOps principles, and best practices and challenges in …
Data is rarely received in perfect form and often requires some sort of manipulation to make it sing. That is why the world needs data analysts. They can squeeze every bit of usefulness from datasets, and they also know how to prep datasets to extract meaning from them. In this course, you will explore key concepts of data manipulation, beginning with data manipulation tools. Then you will learn o…
Dealing with large amounts of data is essential to any modern business and to become a data-driven organization, leaders and decision-makers must establish a deeply ingrained data culture. Use this course to understand the underlying principles of analyzing data and get familiar with terms related to data in order to properly deliver data-related projects. This course will help you identify the ba…
Proficiency Levels
Novice
- Describes the role of data analysis in supporting the company’s strategic objectives, such as AI innovation and sustainability.
- Identifies key data sources and understands their relevance to business operations.
- Explains the importance of data integrity and security in operational processes.
- Lists the steps involved in generating basic data reports and dashboards.
Intermediate
- Works with data to support business decision making.
- Participates in the collection and processing of data according to established operational workflows and guidelines.
- Assists in generating routine reports and dashboards to communicate findings to stakeholders.
- Uses company approved tools to visualize data.
- Follows standard procedures to maintain the security and confidentiality of operational data.
Advanced ★ Required Level
- Evaluates existing data collection and reporting processes to identify inefficiencies or gaps.
- Monitors adherence to data governance policies and suggests operational adjustments to strengthen compliance.
- Advises cross functional teams on optimizing workflows and processes based on data insights.
- Designs solutions to improve data quality and reliability.
- Trains others on how to analyze complex datasets to uncover trends and patterns that inform operational improvements.
- Oversees the assessment of current data models and recommends enhancements to better support business objectives.
Expert
- Leads cross functional teams to implement new data driven operational processes that align with strategic initiatives such as AI integration and sustainability.
- Designs advanced data models and dashboards tailored to evolving business needs.
- Establishes best practices for data collection, analysis, and reporting that support continuous improvement.
- Develops innovative solutions to address complex data management challenges within operations.
- Demonstrates expertise in optimizing data workflows to enhance organizational efficiency.
- Creates and documents operational guidelines to ensure consistent application of data governance policies.
Decision Making is the ability to evaluate complex datasets, identify key insights, and make informed, strategic choices that align with organizational objectives. For a Data Analyst, this skill involves synthesizing large volumes of data to uncover trends, patterns, and opportunities that drive innovation, sustainability, and market leadership. Effective decision-making requires balancing analytical rigor with practical considerations, ensuring that recommendations are both data-driven and actionable. This skill is critical for collaborating with cross-functional teams, optimizing processes, and delivering outcomes that support the company’s mission of enriching lives and fostering a sustainable future.
Learning Resources
Data science involves using scientific analysis, tools, and mathematics to extract useful insight from raw data, and then applying that knowledge for effective business strategy. Increasingly, companies are turning to data science. They can use scientific analysis, tools, and mathematics to extract useful insight from Big Data, to guide their decisions and drive business improvements. In this cour…
Organizations around the world are now realizing the many advantages of using data to overhaul their business strategies and gain a competitive edge. However, data-driven decision making doesn't mean you have to ignore your intuition or past experience. In fact, when used in combination with business acumen, building a data-driven decision-making culture can be a powerful tool for change. In this …
Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagn…
Proficiency Levels
Novice
- Describes the purpose of data visualization tools and basic reporting formats used within the company.
- Explains how data driven insights support innovation, sustainability, and market leadership initiatives.
- Identifies and defines key data sources relevant to business operations and strategic objectives.
- Lists the basic steps involved in collecting, processing, and analyzing organizational data.
Intermediate ★ Required Level
- Works with structured data to generate standard reports for business stakeholders.
- Uses established data models to visualize trends and patterns in consumer electronics sales.
- Follows data governance protocols when handling sensitive or proprietary information.
- Performs data quality checks to ensure accuracy and consistency in routine analyses.
- Assists business initiatives by providing timely, actionable insights from structured data.
Advanced
- Evaluates the effectiveness of current data models and recommends improvements for greater insight.
- Monitors the quality and reliability of data sources, identifying potential risks to data driven decisions.
- Advises on data patterns to provide actionable recommendations for process optimization.
- Designs complex datasets to identify emerging trends and evaluate their impact on strategic objectives.
- Trains cross functional teams to evaluate data requirements for new business initiatives.
- Oversees the outcomes of data driven decisions and suggests adjustments to improve results.
Expert
- Leads the design and implementation of innovative data models and reporting systems that address evolving business needs.
- Develops advanced dashboards that integrate AI driven analytics for enhanced decision support.
- Establishes new data driven strategies for sustainability and market growth.
- Demonstrates initiative in implementing process improvements based on deep analysis of operational data.
- Creates frameworks for integrating diverse data sources to support holistic business insights.
- Designs and documents best practices for data analysis and decision making within the team.
Research is the ability to systematically gather, evaluate, and synthesize information from diverse sources to support data-driven decision-making. For a Data Analyst, this skill involves identifying relevant datasets, exploring industry trends, and staying informed about advancements in technology, AI, and sustainability to uncover actionable insights. Effective research ensures the accuracy and relevance of analyses, enabling the identification of patterns and opportunities that align with the company’s strategic objectives. This skill also requires critical thinking and adaptability to address complex problems and provide meaningful contributions to cross-functional initiatives.
Learning Resources
Data science involves using scientific analysis, tools, and mathematics to extract useful insight from raw data, and then applying that knowledge for effective business strategy. Increasingly, companies are turning to data science. They can use scientific analysis, tools, and mathematics to extract useful insight from Big Data, to guide their decisions and drive business improvements. In this cour…
Data analytics is used across various industries to help companies make better-informed business decisions. Data analysts capture, process, and organize data in addition to establishing the best way to present that data. Through this course, learn about the uses and benefits of data analytics and the tools to leverage it. Examine the data analytics maturity model and compare the descriptive, diagn…
Business analysts rely on a wide array of analytical techniques to uncover insights, evaluate data, define solutions, and support strategic decisions. Choosing the right technique ensures that analysis is thorough, data-driven, and aligned with business needs. In this course, explore essential analytical techniques used in business analysis, including benchmarking, document and process analysis, o…
Proficiency Levels
Novice
- Describes the basic principles of data quality, integrity, and security as applied to business analytics.
- Identifies and lists key internal and external data sources relevant to the company’s electronics and semiconductor markets.
- Recognizes common industry trends in AI, sustainability, and consumer electronics by reviewing recent company and competitor reports.
- Summarizes the company’s strategic objectives and explains how data analysis supports these goals.
Intermediate ★ Required Level
- Works with data collection and analysis tools to support routine business performance reports.
- Participates in research on emerging technology trends and summarizes findings for use in team meetings.
- Assists in applying data governance policies when collecting and processing data for regular analysis tasks.
- Uses company approved tools to create simple visualizations that communicate key metrics to stakeholders.
- Performs routine data quality checks and flags inconsistencies for review.
Advanced
- Evaluates multiple data sources to determine the most reliable and relevant information for a specific business initiative.
- Monitors industry and market research to identify patterns that could impact the company’s AI or sustainability strategies.
- Advises on the effectiveness of current data models and recommends improvements to enhance insight generation.
- Designs solutions to address data integrity issues after investigating anomalies in data sets.
- Trains others on how to compare competitor trends and internal performance data to inform strategic decision making.
- Oversees the outcomes of data driven projects and provides feedback to improve future research approaches.
Expert
- Leads cross functional research initiatives to support the launch of innovative products or sustainability programs.
- Designs and implements advanced research methodologies to uncover new business opportunities in emerging technology sectors.
- Develops comprehensive data models and dashboards that integrate external market research with internal performance metrics.
- Creates frameworks for ongoing monitoring of industry trends and competitor activities relevant to the company’s strategic focus.
- Establishes and documents best practices for research and data analysis tailored to the organization’s needs.
- Demonstrates initiative in managing pilot projects that leverage research insights to optimize business processes or product development.
The skill of Presentations for a Data Analyst involves the ability to effectively communicate complex data insights and analytical findings to diverse audiences, including technical teams, business stakeholders, and executives. This skill requires proficiency in creating clear, visually compelling, and impactful data visualizations and reports that align with the organization’s strategic objectives, such as innovation in AI, sustainability, and market leadership. A strong emphasis is placed on tailoring the presentation style and content to the audience, ensuring that data-driven recommendations are easily understood and actionable. Mastery of this skill enables the Data Analyst to bridge the gap between data analysis and decision-making, driving alignment and fostering informed strategies across the organization.
Learning Resources
Data is meaningful only when information is extracted from it. That information can tell a story, and the best data analysts are magnificent storytellers. But no matter how accomplished a data analyst, a story can't be told compellingly without visualizing what the data says and a key part of a data analyst's role is in reporting on what the data is saying. In this course, you will explore data vi…
Data visualization prepares data for presentation to make it easier for non-data analysts to understand what the data is saying. Often, non-data analysts can't see relationships in data without visual aids, but effective data presentation allows them to change parameters and understand and view the data in a similar manner to a data scientist. In this course, explore the purpose and key considerat…
In today's data-driven world, mastering data visualization is essential for driving digital transformation and making well-informed decisions. In this course, you'll gain the skills needed to create clear, accurate, and impactful visual representations of data. In this course, you'll learn about the importance and types of data visualization and how it supports informed decision-making. You'll als…
Proficiency Levels
Novice
- Describes the basic components of an effective data presentation, including clarity and visual appeal.
- Identifies key data visualization tools and reporting platforms used within the organization.
- Lists common data visualization formats (charts, graphs, dashboards) and their typical use cases.
- Explains the importance of data quality and integrity when preparing presentation materials.
Intermediate ★ Required Level
- Uses standard visualization tools to create charts and graphs that summarize key trends.
- Follows data governance policies when sharing data visualizations with stakeholders.
- Assists in preparing simple data presentations using approved templates for routine business reports.
- Performs basic analytical findings to immediate team members, ensuring clarity and accuracy.
- Incorporates feedback from supervisors to improve the clarity and impact of routine presentations.
Advanced
- Monitors the effectiveness of different visualization methods for communicating complex insights.
- Advises on the alignment of presentation content with strategic objectives such as AI innovation or sustainability.
- Designs presentations of nuanced analytical findings for cross functional teams, adapting style for diverse audiences.
- Oversees the interpretation of complex data sets and distillation of key messages for executive level presentations.
- Evaluates audience feedback to refine presentation techniques and improve stakeholder understanding.
- Trains others to critique existing dashboards and reports, recommending improvements for greater impact.
Expert
- Leads cross functional workshops to teach best practices in data visualization and communication.
- Designs and delivers interactive dashboards that enable stakeholders to explore data independently.
- Establishes guidelines for consistent, high impact data presentations across the organization.
- Develops new presentation frameworks that integrate advanced analytics with business storytelling.
- Demonstrates innovative ways to visualize emerging data types (e.g., IoT, digital health) for business leaders.
- Creates tailored presentations that influence strategic decisions in areas like AI, sustainability, or market expansion.