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Lisa Dierker

Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly. By the end of the course, you will be able to use powerful data analysis tools – either SAS or Python – to manage and visualize your data, including how to deal with missing data, variable groups, and graphs. Throughout the course, you will share your progress with others to gain valuable feedback, while also learning how your peers use data to answer their own questions.

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What's inside

Syllabus

Selecting a research question
We would like to welcome you to Wesleyan University's Data Analysis and Interpretation Specialization. In this session, we will discuss the basics of data analysis. Your task will be to select a data set that you would like to work with and to review available code books that help you develop your own research question. You will also set up a Tumblr blog that will allow you to reflect on these experiences, submit assignments and share your work with others throughout the course. First, you may want to check out the welcome video
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Helps learners with data analysis questions they may not have thought of otherwise
Teaches data analysis for real-world use
Develops data analysis skills and knowledge for career success
Covers a range of data analysis methods and tools
Includes hands-on practice and exercises
Taught by data analysis experts

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Reviews summary

Data management and visualization course insights

According to learners, this course provides a solid introduction to data management and visualization, offering the unique flexibility of choosing to learn either SAS or Python. Many students highlight the hands-on assignments, finding them highly practical and valuable for applying concepts directly to their own data and research questions. The modules covering data munging and creating visualizations are frequently praised for their usefulness in handling and presenting real-world data. While the course aims to be accessible to beginners, some reviewers found the pace challenging, particularly in the programming sections, suggesting a desire for more detailed explanations or slower progression.
Covers cleaning and preparing messy data.
"The session on data management/munging was especially useful for tackling real-world datasets that aren't perfectly clean."
"Learned practical techniques for dealing with common data issues like missing values effectively."
"The concepts on data cleaning were presented clearly and directly applicable to my work."
Learn to create effective graphs.
"Learning to visualize data using code was a very practical skill I gained from this course."
"The module on creating graphs helped me understand how to effectively present data findings."
"Covered the basics of data visualization well, showing how to create standard plots."
Hands-on tasks aid learning application.
"The assignments were the strongest part; applying concepts to my chosen dataset made everything click."
"Working on my own research question throughout the course was incredibly motivating and practical."
"Found the assignments relevant and they really reinforced the data management skills taught."
Good introduction for newcomers to data.
"As someone completely new to data analysis, I found the early modules and concepts very understandable and easy to follow."
"The course truly starts from scratch, which is exactly what I needed with no prior programming knowledge."
"While the start is great, it felt a bit fast-paced for me as a complete beginner once we introduced programming."
Offers choice between two major data tools.
"I appreciated having the option to learn either SAS or Python, catering to different needs and career paths."
"Choosing between SAS and Python was great for flexibility, but sometimes I felt the course material was slightly less cohesive because of it."
"Successfully got me started with Python programming for basic data handling tasks."
Some found the pace challenging or depth lacking.
"Felt like the course moved quite quickly, especially once we got into writing more complex code."
"I think more time or depth could have been spent on advanced data manipulation techniques."
"While a good intro, it only scratches the surface on visualization tools and options available."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Management and Visualization with these activities:
Review statistics concepts
Ensure a strong foundation for the course by reviewing key statistical concepts.
Browse courses on Statistics
Show steps
  • Identify areas of statistics that need reinforcement
  • Review notes, textbooks, or online resources
  • Complete practice problems
Read "Data Science for Business"
Enhance your understanding of the fundamentals of data science by reading a foundational book on the subject.
Show steps
  • Purchase or borrow a copy of the book
  • Read the book and take notes
  • Complete the exercises and assignments in the book
Attend a data analysis workshop
Gain practical experience and insights by attending a workshop focused on data analysis techniques or tools.
Browse courses on Data Analysis
Show steps
  • Identify a workshop that aligns with your interests and learning goals
  • Register for the workshop
  • Attend the workshop and actively participate
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group with other students
Enhance your learning by engaging in discussions, sharing insights, and working together on data analysis projects with other students.
Browse courses on Data Analysis
Show steps
  • Find a study group or create your own
  • Meet regularly to discuss course material, work on assignments, and prepare for exams
  • Take turns presenting your findings and leading discussions
Practice writing SAS or Python code
Complete plenty of practice drills to strengthen your grasp of SAS or Python syntax and data analysis techniques.
Browse courses on SAS
Show steps
  • Choose a data set that you're interested in and load it into SAS or Python
  • Write code to generate summary statistics for the data
  • Plot the data in a variety of ways to explore the relationships between the variables
  • Practice manipulating the data using SAS or Python functions
Explore SAS or Python online tutorials
Extend your knowledge by exploring online tutorials that provide step-by-step instructions for specific data analysis tasks.
Browse courses on SAS
Show steps
  • Identify an aspect of data analysis that you'd like to learn more about
  • Search for an online tutorial that covers the topic
  • Follow the tutorial instructions and complete the exercises
Develop a data analysis project
Deepen your understanding of data analysis by undertaking a project that involves collecting, cleaning, analyzing, and visualizing data.
Browse courses on Data Analysis
Show steps
  • Identify a question or problem that you want to solve using data
  • Collect data from relevant sources
  • Clean and prepare the data for analysis
  • Analyze the data using SAS or Python techniques
  • Visualize the results in a clear and informative way
  • Write a report or presentation to communicate your findings
Create a data visualization dashboard
Strengthen your data visualization skills by creating a dashboard that presents key insights from a data set.
Browse courses on Data Visualization
Show steps
  • Identify the data set you want to use
  • Choose a data visualization tool
  • Design and create the dashboard
  • Share your dashboard with others

Career center

Learners who complete Data Management and Visualization will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts work on the frontlines of business, using data to find and solve problems that improve their organizations. This course, Data Management and Visualization, will help you build a foundation in data analysis. You will learn how to manage and visualize data, two essential skills for any Data Analyst. This course may also be helpful for professionals who want to pivot into a career in data analysis.
Data Scientist
Data Scientists use their skills in mathematics, statistics, and computer science to extract knowledge and insights from data. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Data Scientist. This course may also be helpful for professionals who want to pivot into a career in data science.
Market Researcher
Market Researchers use data to understand consumer behavior and trends. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Market Researcher. This course may also be helpful for professionals who want to pivot into a career in market research.
Business Analyst
Business Analysts use data to help businesses make better decisions. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Business Analyst. This course may also be helpful for professionals who want to pivot into a career in business analysis.
Financial Analyst
Financial Analysts use data to make investment recommendations. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Financial Analyst. This course may also be helpful for professionals who want to pivot into a career in financial analysis.
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of business operations. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Operations Research Analyst. This course may also be helpful for professionals who want to pivot into a career in operations research.
Statistician
Statisticians use data to analyze trends and make predictions. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Statistician. This course may also be helpful for professionals who want to pivot into a career in statistics.
Data Engineer
Data Engineers build and maintain the infrastructure that stores and processes data. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Data Engineer. This course may also be helpful for professionals who want to pivot into a career in data engineering.
Database Administrator
Database Administrators manage and maintain databases. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Database Administrator. This course may also be helpful for professionals who want to pivot into a career in database administration.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Software Engineer. This course may also be helpful for professionals who want to pivot into a career in software engineering.
Computer Scientist
Computer Scientists research and develop new computing technologies. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Computer Scientist. This course may also be helpful for professionals who want to pivot into a career in computer science.
Information Systems Manager
Information Systems Managers plan and implement information systems for organizations. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Information Systems Manager. This course may also be helpful for professionals who want to pivot into a career in information systems management.
Chief Data Officer
Chief Data Officers are responsible for overseeing the use of data within an organization. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Chief Data Officer.
Data Governance Specialist
Data Governance Specialists develop and implement policies and procedures for managing data within an organization. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Data Governance Specialist.
Privacy Analyst
Privacy Analysts develop and implement policies and procedures for protecting the privacy of data. This course, Data Management and Visualization, can help you build a foundation in data management and visualization. These are both essential skills for any Privacy Analyst.

Reading list

We've selected 28 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 Management and Visualization.
Comprehensive overview of reinforcement learning. It covers topics such as Markov decision processes, dynamic programming, and deep reinforcement learning.
Comprehensive guide to data science techniques and tools. It covers everything from data collection and cleaning to data analysis and visualization.
Comprehensive introduction to statistical learning. It covers topics such as supervised learning, unsupervised learning, and model selection.
Provides a comprehensive overview of deep learning for data science. It covers a wide range of topics, from neural networks to deep learning architectures.
Great resource for learning how to use Python for data analysis. It covers all the basics, from data cleaning and manipulation to data visualization and statistical modeling.
Provides a hands-on introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, from supervised learning to unsupervised learning.
Is an easy-to-follow guide on data visualization. It teaches readers how to display data in a way that is easy to understand and visually appealing.
Provides a comprehensive overview of the ethical issues surrounding data collection, analysis, and use. It covers topics such as data privacy, data security, and algorithmic bias.
Comprehensive guide to using R for data science. It covers everything from data cleaning and manipulation to data visualization and statistical modeling.
Teaches readers how to use SAS for data analysis. It covers topics like data cleaning, data manipulation, and data visualization. It's a good choice for beginners who want to learn how to use SAS for data analysis.
Provides a comprehensive overview of deep learning for natural language processing. It covers topics like natural language processing tasks, deep learning models for natural language processing, and deep learning applications for natural language processing. It's a good choice for readers who want to learn more about deep learning for natural language processing.
Provides a practical overview of data science techniques for business applications. It covers topics such as data mining, machine learning, and predictive analytics.
Provides a clear and concise introduction to data visualization. It covers the basics of data visualization, as well as more advanced techniques.
Provides a comprehensive overview of big data for data science. It covers a wide range of topics, from data storage to data analysis.
Provides a comprehensive overview of computer vision for data science. It covers a wide range of topics, from image processing to object recognition.
Provides a comprehensive overview of time series analysis with Python. It covers a wide range of topics, from time series forecasting to time series clustering.
Provides a comprehensive overview of data science concepts and techniques. It good starting point for learners who are new to the field.
Introduces readers to data management, covering key concepts like data storage, data quality, and data security. It's a good resource for beginners who want to learn more about managing data.
Provides a user-centered approach to data visualization. It covers topics like data visualization principles, data visualization techniques, and data visualization tools. It's a good choice for readers who want to learn more about data visualization.
Provides a practical guide to data science. It covers topics like data collection, data analysis, and data visualization. It's a good choice for readers who want to learn more about data science.
Provides a comprehensive overview of data mining techniques. It covers topics like data preprocessing, data mining algorithms, and data mining applications. It's a good choice for readers who want to learn more about data mining.
Good introduction to machine learning concepts and techniques. It covers a wide range of topics, from supervised learning to unsupervised learning.
Provides a comprehensive overview of statistics for data science. It covers a wide range of topics, from descriptive statistics to machine learning.

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