Data Analysis: Statistical Modeling and Computation in Applications from edX

If you have specific questions about this course, please contact us at [email protected].

Data science requires multi-disciplinary skills ranging from mathematics, statistics, machine learning, problem solving to programming, visualization, and communication skills. In this course, learners will combine these foundational and practical skills with domain knowledge to ask and answer questions using real data.

This course will start with a review of common statistical and computational tools such as hypothesis testing, regression, and gradient descent methods. Then, learners will study common models and methods to analyze specific types of data in four different domain areas:

Epigenetic Codes and Data Visualization
Criminal Networks and Network Analysis
Prices, Economics and Time Series
Environmental Data and Spatial Statistics

Learners will be guided to analyze a real data set from each of these areas of focus, and present their findings in written reports. They will also discuss relevant and practical issues with peers.

This course is part of the MITx MicroMasters Program in Statistics and Data Science. It is at a similar pace and level of rigor as an on-campus course at MIT. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

What you'll learn

Model, form hypotheses, perform statistical analysis on real data
Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data
Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks
Model time series using moving average, autoregressive and other stationary models for forecasting with financial data
Use Gaussian processes to model environmental data and make predictions
Communicate analysis results effectively

What's inside

Learning objectives

Model, form hypotheses, perform statistical analysis on real data
Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data
Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks

Model time series using moving average, autoregressive and other stationary models for forecasting with financial data
Use gaussian processes to model environmental data and make predictions
Communicate analysis results effectively

Model, form hypotheses, perform statistical analysis on real data
Use dimension reduction techniques such as principal component analysis to visualize high-dimensional data and apply this to genomics data
Analyze networks (e.g. social networks) and use centrality measures to describe the importance of nodes, and apply this to criminal networks
Model time series using moving average, autoregressive and other stationary models for forecasting with financial data
Use gaussian processes to model environmental data and make predictions
Communicate analysis results effectively

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Karene Chu, Stefanie Jegelka, and Caroline Uhler, who are recognized for their work in the field of data science

Provides practical skills and knowledge in data science, including mathematics, statistics, machine learning, and programming

Covers advanced topics in data science, such as network analysis, time series analysis, and spatial statistics

Requires a strong foundation in mathematics and statistics

May require additional resources or software, such as programming software and statistical packages

Involves working with real-world datasets and presenting findings in written reports

Reviews summary

Well-received data analysis course

Learners say this data analysis course is well-received and appropriate for both beginners and experts. The course gives a thorough understanding of data analysis and equips students with statistical modeling skills. It covers a wide range of topics, from basic graph analysis to time series analysis and Gaussian and spatial statistics. Despite being challenging, learners agree that the course is worth taking because of the amount of material learned.

Course is accessible to beginners.

"I recommend this course its really nice for both beginners and experts"

Course covers a range of data analysis topics.

"It cover a broad of topic, such as the basic of graph analysis, time series analysis and the last module is about Gaussian and spatial statistics."

Be prepared for a challenging course.

"This is an amazing mooc, to be honest it's a difficult one with a lot of material."

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 Analysis: Statistical Modeling and Computation in Applications with these activities:

Review Applied Predictive Modeling

Show steps

Review this foundational text in predictive modeling to solidify fundamental concepts and prepare for the upcoming coursework.

View Tidy Modeling with R: A Framework for Modeling... on Amazon

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Read Chapters 1-3 to review modeling basics.
Complete the exercises and quizzes in each chapter.
Summarize key concepts in your own words.

Build a Resource Guide for Data Science

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Compile a comprehensive resource guide that includes valuable materials, tools, and references for data science. This will provide a centralized resource for you to utilize throughout the course and beyond, facilitating your learning journey.

Browse courses on Data Science Tools

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Identify and gather relevant resources, articles, tutorials, and datasets.
Categorize and organize the resources based on topic and purpose.
Document the resources in a structured and accessible format.

Explore Network Analysis with Gephi

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Develop proficiency in network analysis techniques by following guided tutorials on Gephi. This practical experience will enable you to analyze and visualize complex networks, extracting valuable insights from data relationships.

Browse courses on Network Analysis

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Familiarize yourself with the Gephi interface and basic functionality.
Load and explore network data within Gephi.
Apply network analysis algorithms and metrics to identify patterns and relationships.
Create visualizations to communicate the insights gained from network analysis.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Practice Hypothesis Testing with Online Tools

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Sharpen your hypothesis testing skills by utilizing online tools and resources for practice. This will enhance your proficiency and confidence in applying this technique in data analysis.

Browse courses on Hypothesis Testing

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Find online resources and tools for hypothesis testing.
Practice conducting hypothesis tests using different scenarios.
Analyze the results and interpret the statistical significance.

Create a GitHub Portfolio of Data Projects

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Develop a portfolio of data projects on GitHub to demonstrate your skills and practice project management. This will be a valuable asset for showcasing your abilities and securing future opportunities.

Browse courses on GitHub

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Create a GitHub account and set up a profile.
Identify data sources and gather data.
Develop and implement data analysis and modeling algorithms.
Visualize results and communicate insights.
Write clear and concise documentation for each project.

Develop a Data Science Portfolio Website

Show steps

Showcase your data science skills and projects by creating a professional portfolio website. This will serve as a valuable tool for demonstrating your expertise and capabilities to potential employers or collaborators.

Show steps

Choose a web development platform and design your website.
Develop individual project pages to highlight your data science work.
Include a section for your resume, LinkedIn profile, and contact information.
Publish your website and optimize it for search engines.

Develop an Interactive Data Dashboard

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Enhance your data visualization and communication skills by creating an interactive data dashboard. This project will allow you to present complex data in a visually appealing and accessible manner.

Browse courses on Data Visualization

Show steps

Identify a dataset and define the key insights you want to communicate.
Choose a data visualization tool and explore its capabilities.
Design and develop interactive visualizations to effectively convey your insights.
Publish your dashboard and share it with peers for feedback.

Career center

Learners who complete Data Analysis: Statistical Modeling and Computation in Applications will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models. This course may be useful for a Machine Learning Engineer as it provides a foundation in statistical modeling and machine learning. This course may also be useful in a supporting role for a Machine Learning Engineer, such as data collection or data preparation.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers build and maintain the infrastructure that supports data-driven applications. This course may be useful for a Data Engineer as it provides a foundation in data modeling, statistical analysis, and programming. This course may also be useful in a supporting role for a Data Engineer, such as data collection or data preparation.

See salaries and explore the career path for Data Engineer

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve business problems. This course may be useful for an Operations Research Analyst as it provides a foundation in statistical modeling and optimization. This course may also be useful in a supporting role for an Operations Research Analyst, such as data collection or data visualization.

See salaries and explore the career path for Operations Research Analyst

Econometrician

Econometricians use statistical techniques to analyze economic data. This course may be useful for an Econometrician as it provides a foundation in statistical modeling and data analysis. This course may also be useful in a supporting role for an Econometrician, such as data collection or data visualization.

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Business Analyst

Business Analysts use data to identify and solve business problems. This course may be useful for a Business Analyst as it provides a foundation in data modeling and statistical analysis. This course may also be useful in a supporting role for a Business Analyst, such as data collection or data visualization.

See salaries and explore the career path for Business Analyst

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course may be useful for a Data Analyst as it provides a foundation in data modeling and statistical analysis. This course may also be useful in a supporting role for a Data Analyst, such as data collection or data visualization.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to model and analyze financial data. This course may be useful for a Quantitative Analyst as it provides a foundation in statistical modeling and data analysis. This course may also be useful in a supporting role for a Quantitative Analyst, such as data collection or data preparation.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be useful for an Actuary as it provides a foundation in statistical modeling and data analysis. This course may also be useful in a supporting role for an Actuary, such as data collection or data visualization.

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Epidemiologist

Epidemiologists investigate the causes and spread of disease. This course may be useful for an Epidemiologist as it provides a foundation in statistical modeling and data analysis. This course may also be useful in a supporting role for an Epidemiologist, such as data collection or data visualization.

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Biostatistician

Biostatisticians use statistical techniques to analyze biological data. This course may be useful for a Biostatistician as it provides a foundation in statistical modeling and data analysis. This course may also be useful in a supporting role for a Biostatistician, such as data collection or data visualization.

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Information Architect

Information Architects design and build information systems that are easy to use and understand. This course may be useful for an Information Architect as it provides a foundation in data modeling and user experience design. This course may also be useful in a supporting role for an Information Architect, such as data collection or data visualization.

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Statistician

Statisticians use mathematical and statistical techniques to collect, analyze, interpret, and present data. This course may be useful for a Statistician as it provides a foundation in statistical modeling and data analysis. This course may also be useful in a supporting role for a Statistician, such as data collection or data preparation.

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User Experience Designer

User Experience Designers design and build user interfaces that are easy to use and understand. This course may be useful for a User Experience Designer as it provides a foundation in data visualization and human-computer interaction. This course may also be useful in a supporting role for a User Experience Designer, such as data collection or data analysis.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course may be useful for a Software Engineer as it provides a foundation in programming and data structures. This course may also be useful in a supporting role for a Software Engineer, such as data collection or data visualization.

See salaries and explore the career path for Software Engineer

Data Scientist

Data Scientists work in artificial intelligence, machine learning, and large data infrastructure. They may also work in service roles such as data architecture, maintenance, and support. This course may be useful for a Data Scientist as it provides a foundation in data modeling, statistical analysis, information visualization, and programming. This course may also be useful in a supporting role for a Data Scientist, such as training and maintaining data infrastructure.

See salaries and explore the career path for Data Scientist