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Data Analysis and Visualization

Peter Baumgartner and Brittany O'Dea

By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story.

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By the end of this course, learners are provided a high-level overview of data analysis and visualization tools, and are prepared to discuss best practices and develop an ensuing action plan that addresses key discoveries. It begins with common hurdles that obstruct adoption of a data-driven culture before introducing data analysis tools (R software, Minitab, MATLAB, and Python). Deeper examination is spent on statistical process control (SPC), which is a method for studying variation over time. The course also addresses do’s and don’ts of presenting data visually, visualization software (Tableau, Excel, Power BI), and creating a data story.

Material features online lectures, videos, demos, project work, readings and discussions. This course is ideal for individuals keen on developing a data-driven mindset that derives powerful insights useful for improving a company’s bottom line. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. It is the second course in the Data-Driven Decision Making (DDDM) specialization. To learn more about the specialization, check out a video overview at https://www.youtube.com/watch?v=Oi4mmeSWcVc&list=PLQvThJe-IglyYljMrdqwfsDzk56ncfoLx&index=11.

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What's inside

Syllabus

Data Analysis Software Tools
This module provides an overview of the tools needed for data analysis.
Statistical Process Control (SPC)
This module covers SPC, a way to analyze variation over time in your process using data. It is helpful in identifying current problems and can also be used to monitor the process for any deviations once the process is ‘in control'.
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Data Visualization and Translation
This module provides tools for leveraging data through visualization and translation.
Project: Data Analysis and Visualization
This module provides an opportunity to bridge theory and practice. Learners apply knowledge from this course to solve a business problem.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers data analysis tools (R software, Minitab, MATLAB, and Python), useful for extracting valuable insights
Provides a good overview of statistical process control (SPC), helpful for identifying problems and monitoring process stability
Explores visualization tools (Tableau, Excel, Power BI), enabling effective data storytelling and communication
Emphasizes developing a data-driven mindset, essential for making informed decisions
Suitable for individuals seeking to improve their ability in data analysis and visualization for informed decision-making
Ideal for learners with some familiarity with reading reports, handling data, and interpreting visualizations

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Reviews summary

Insightful data analysis & visualization

Learners say this data analysis and visualization course gives a broad vision and excellent techniques for analyzing and visualizing data. Great reading material is attached to each topic. However, some learners find assignments to be either difficult or unclear.
Includes great reading material with each topic.
"Great reading material attached with each topic"
Some learners find assignments difficult or unclear.
"Examples and better instructions on the assignment would improve this course."
"It was very good. just the last project was a bit confusing and not so clear."

Activities

Coming soon We're preparing activities for Data Analysis and Visualization. These are activities you can do either before, during, or after a course.

Career center

Learners who complete Data Analysis and Visualization will develop knowledge and skills that may be useful to these careers:
Data Analyst
The Data Analyst role is an information technology professional who converts raw data into insights and actionable information. Data Analysts, in short, organize data to increase efficiency. Given their heavy reliance on extracting key insights from data, it is paramount that they have a strong understanding of data analysis software tools, which this course provides an extensive overview of. This course may also be helpful for Data Analysts who work with statistical process control, or want to expand their horizons to excel in data presentation.
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, computer science, and business to extract insights from data. They develop and implement data analysis algorithms, and build models to predict future trends. This course will provide you with a strong foundation in the tools and techniques used by Data Scientists, including data analysis software tools and statistical process control.
Statistician
Statisticians are responsible for the collection, analysis, interpretation, and presentation of data. They use their skills in mathematics, statistics, and computing to solve problems in a wide range of industries. This course will provide you with a solid foundation in the tools and techniques used by Statisticians, including data analysis software tools and statistical process control.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to help organizations make better decisions. They use data to identify inefficiencies and develop solutions to improve operations. This course will provide you with a strong foundation in the tools and techniques used by Operations Research Analysts, including data analysis software tools and statistical process control.
Business Analyst
Business Analysts use data to identify and solve business problems. They work with stakeholders to understand their needs, and then develop and implement solutions. This course will provide you with a strong foundation in the tools and techniques used by Business Analysts, including data analysis software tools and statistical process control.
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior. They use this information to develop marketing strategies and campaigns. This course will provide you with a strong foundation in the tools and techniques used by Market Researchers, including data analysis software tools and statistical process control.
User Experience (UX) Designer
User Experience Designers create products and services that are easy and enjoyable to use. They use their knowledge of human behavior and design principles to create user interfaces that are intuitive and efficient. This course will provide you with a foundation in the tools and techniques used by UX Designers, including data analysis software tools and visualization software.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages, software development tools, and computer science to create software that meets the needs of users. This course will provide you with a foundation in the tools and techniques used by Software Engineers, including data analysis software tools.
Web Developer
Web Developers create and maintain websites. They use their knowledge of programming languages and web technologies to build websites that are functional and visually appealing. This course will provide you with a foundation in the tools and techniques used by Web Developers, including data analysis software tools and visualization software.
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. They use their knowledge of databases, programming languages, and cloud computing to ensure that data is accessible, reliable, and secure. This course will provide you with a foundation in the tools and techniques used by Data Engineers, including data analysis software tools.
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to make it easier for users to understand. They use their skills in design and technology to create clear and concise visualizations that communicate key insights. This course will provide you with a strong foundation in the tools and techniques used by Data Visualization Specialists, including data visualization software.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use this information to make investment decisions and develop trading strategies. This course will provide you with a strong foundation in the tools and techniques used by Quantitative Analysts, including data analysis software tools and statistical process control.
Actuary
Actuaries use mathematical and statistical models to assess risk. They use this information to develop insurance and pension plans, and to advise clients on financial matters. This course will provide you with a strong foundation in the tools and techniques used by Actuaries, including data analysis software tools and statistical process control.
Risk Analyst
Risk Analysts use data to identify and assess risks. They use this information to develop risk management plans and advise clients on risk mitigation strategies. This course will provide you with a strong foundation in the tools and techniques used by Risk Analysts, including data analysis software tools and statistical process control.
Financial Analyst
Financial Analysts use financial data to make investment decisions. They use this information to develop financial models and advise clients on investment strategies. This course will provide you with a strong foundation in the tools and techniques used by Financial Analysts, including data analysis software tools.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Analysis and Visualization.
Offers an advanced treatment of statistical learning methods, including supervised and unsupervised learning, regression, and classification. Primarily valuable as a reference for learners with a strong statistical background.
Provides a comprehensive guide to using R for data analysis, covering data import, cleaning, transformation, visualization, and modeling. Beneficial for learners who want to gain proficiency in R programming.
Offers an advanced treatment of Bayesian data analysis methods, including model fitting, inference, and prediction. Primarily valuable as a reference for learners with a strong statistical background.
Offers practical advice and insights into the real-world challenges of data science, from data collection to model deployment. Valuable for learners interested in the practical aspects of data analysis.
Provides a practical introduction to machine learning algorithms and techniques using popular Python libraries. Useful for learners interested in applying machine learning to data analysis.
Offers a comprehensive guide to using Python for data science, including data manipulation, analysis, and visualization. Valuable for learners interested in Python programming for data analysis.
Provides a practical guide to using Tableau for data visualization, covering data preparation, visualization creation, and best practices. Beneficial for learners who want to develop proficiency in Tableau.
Offers a practical guide to using Python libraries for data analysis, including data manipulation, aggregation, and visualization. Beneficial for learners who want to develop proficiency in Python programming for data analysis.
Provides a comprehensive introduction to probability and statistics, emphasizing applications in engineering and science. Beneficial for learners who want to strengthen their foundational knowledge in these areas.
Introduces the principles of data visualization and provides practical guidance on creating effective visualizations. Useful for learners who want to improve their data presentation skills.

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