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Professional Certificate in Advanced Analytics

Advance your analytics career with cutting-edge tools and techniques

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Course Date

STARTS ON

February 28, 2023

Course Duration

DURATION

6 months, online
15–20 hours per week

Course Fee

PROGRAM FEE

US$7,450 US$6,705 or get US$745 off with a referral

Course Information Flexible payment available
Course Fee

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Develop Future-Ready Skills Today

Emeritus is collaborating with MIT xPRO to help you build future-ready skills. Enroll before and get up to 10% tuition assistance to set yourself up for professional success.​

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Prerequisites: Familiarity with Excel datasets, data visualization, and basic knowledge of Github and Python coding are recommended.

Drive Decision-Making With Data

With the advent of big data, executives are turning to data scientists and analysts for actionable insights that help chart a path to success. Data professionals have become information champions, leveraging their expertise to unearth hidden opportunities for higher profit and long-term growth.

Therefore, it’s easy to understand why data science holds the number three spot among best professions in the U.S. and why the U.S. Bureau of Labor Statistics projects an average of 13,500 job openings in the field every year through 2031.

If you are ready to level up your data science skills for modern-day decision-making, the Professional Certificate in Advanced Analytics from MIT xPRO will give you the expertise you need to navigate a future shaped by data and help you advance in an in-demand field.

23%

increase in the number of job openings for analysts between 2021 and 2031

Source: U.S. Bureau of Labor Statistics

$120,000

Median base salary for data scientists in the U.S.

Source: Glassdoor
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Who Is This Program For?

  • Data professionals in engineering, finance, insurance, IT, or operations with coding experience who wish to develop a suite of advanced data analysis skills they can apply in their day-to-day work to make sound business decisions
  • Business professionals in sales, marketing, IT, or operations looking to sharpen their decision-making by learning to model, execute, and analyze data and use their expertise to advance their career
  • Recent graduates with a STEM background who wish to build practical experience in data science to prepare for a role as an analyst and start down a career path in the field
Prerequisites: Familiarity with Excel datasets, data visualization, and basic knowledge of Github and Python coding are recommended.

Key Takeaways

  • Leverage data in order to optimize or improve decision-making within an organization
  • Use Python and Google Colab to train, organize, run, and analyze datasets and models that yield meaningful results
  • Analyze and translate technical results into actionable insights for executives
  • Develop a portfolio that showcases hands-on application of data science skills for model optimization and strategic decision-making for business

Program Highlights

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Earn an MIT xPRO certificate and 36 continuing education units (CEUs)

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Gain insights from MIT faculty — experts in the field who bring industry experience to the classroom

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Learn from real-world case studies solved by faculty using applications of data science and applied analytics

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Demonstrate your knowledge in a final capstone project by developing a portfolio of application-based assignments completed throughout the program

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Receive one-on-one career support from Emeritus and introductions to hiring partners for eligible participants

Program Topics

    Survey the fundamentals of data science and understand its potential. Explore how data science can be used to learn more about your customers and master key analytical frameworks.

    • Module 1: Introduction to Data Science
    • Module 2: Thinking about Risk and Uncertainty through Probability and Distributions
    • Module 3: Correlation
    • Module 4: Clustering
    • Module 5: Linear Regression Part 1
    • Module 6: Linear Regression Part 2
    • Module 7: Logistic Regression


    Examine the role of optimization and how it can be used for the good of humanity. Dive deeper into model design and learn how to build, interpret, and assess models.

    • Module 8: Collaborative Filtering
    • Module 9: Optimization Part 1
    • Module 10: Optimization Part 2
    • Module 11: Optimization Part 3
    • Module 12: Optimization Part 4
    • Module 13: Optimization Part 5


    Take an in-depth look at regression and classification, ensemble learning, and bias in model-based, data-driven decision-making.

    • Module 14: Regression and Classification
    • Module 15: Ensemble Models
    • Module 16: Fairness and Bias Issues in Data-Driven Predictions


    Explore advanced applications of data science including deep learning, neural networks, and natural language processing.

    • Module 17: Neural Networks Part 1
    • Module 18: Neural Networks Part 2
    • Module 19: Neural Networks Part 3
    • Module 20: Natural Language Processing (NLP) Part 1
    • Module 21: Natural Language Processing (NLP) Part 2
    • Module 22: Interpretability and Causality in Models


    Discover real-world applications of AI/ML and new applications of digital transformation.

    • Module 23: Data, Models, and Decisions
    • Module 24: Leading Digital Transformations


Career Preparation and Guidance

Both hard and soft skills are needed to succeed as a data analytics professional. This program offers you guidance for navigating your career path, from crafting your elevator pitch to perfecting your interview skills. These services are provided by Emeritus, our learning collaborator for this program. The program support team includes program leaders who help you reach your learning goals. The primary goal is to give you the skills necessary to be prepared for a job in this field; however, job placement is not guaranteed.

Career guidance with this program includes:

  • Navigating your job search
  • Preparing for interviews
  • Negotiating salary
  • Crafting your elevator pitch
  • Optimizing your LinkedIn profile
  • Writing resumes and cover letters

Exercises specific to building your data science career include:

  • Building your personal brand and promoting your skills
  • Understanding the roles and workflow of data science
  • Job searching and interviewing for positions in data science
  • Communicating data science concepts through a capstone project
  • Reflecting on your skills to determine how to solve problems and become an efficient leader

Case Studies

Throughout the program, each faculty member will present case studies they have personally helped solve using the principles and tools of data science, including:

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Retail Management

Analyze how a Boston MA retailer handled their response to the Covid-19 pandemic using the systems approach.

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Filatoi Riuniti

Solve an optimization problem by developing a model and making recommendations on how this Italian yarn manufacturer should outsource production in order to maximize profits.

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Facial Analysis Algorithms

Examine the ramifications of poorly designed models, and discover how to detect, diagnose, and mitigate biases that can arise in model-based, data-driven decision-making.

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BlueBike

See how BlueBike used regression to predict demand. Then build your own models with regression and use R2 to pick the optimal model.

Program Faculty

Profile picture of program faculty, Vivek Farias

Vivek Farias

Patrick J. McGovern (1959) Professor, Professor of Operations Management, MIT Sloan School of Management

Vivek is affiliated with the Operations Management group and the Operations Research Center (ORC) at MIT Sloan School of Management... More info

Profile picture of program faculty, Robert Freund

Robert Freund

Theresa Seley Professor in Management Science, Professor of Operations Research, MIT Sloan School of Management

Robert’s primary research interests are in convex optimization, computational complexity and related computational science, convex geometry, large-scale nonlinear optimization, and related mathematical systems... More info

Profile image of the program faculty, Retsef Levi

Retsef Levi

J. Spencer Standish (1945) Professor of Operations Management, Faculty Leader, MIT Leaders for Global Operations (LGO), MIT Sloan School of Management

Retsef’s research focuses on the design of analytical data-driven decision support models and tools addressing complex business and system design decisions under uncertainty in areas such as health and healthcare management, supply chain, procurement and inventory management, revenue management, pricing optimization, and logistics... More info

Profile image of the program faculty, Rama Ramakrishnan

Rama Ramakrishnan

Professor of the Practice, MIT Sloan School of Management

Rama’s research and teaching interests focus on the application of data science and machine learning techniques to problems in industry and the creation of intelligent products and services created by the algorithmic use of data... More info

Profile picture of program faculty, Vivek Farias

Vivek Farias

Patrick J. McGovern (1959) Professor, Professor of Operations Management, MIT Sloan School of Management

Vivek is affiliated with the Operations Management group and the Operations Research Center (ORC) at MIT Sloan School of Management. His research focuses on the development of new methodologies for large-scale dynamic optimization under uncertainty, and the application of these methodologies to the design of practical revenue management strategies across various industries, from airlines and retail to online advertising. He serves on the editorial boards of Management Science as a co-editor of data science, Operations Research, and the Informs Journal on Optimization.

Vivek received his PhD in electrical engineering from Stanford University. During his time in industry, he served as co-founder/CTO at Celect, a predictive analytics start-up acquired by Nike. Vivek also serves on several technology start-up advisory boards and has experience in quantitative finance and private equity.

Profile picture of program faculty, Robert Freund

Robert Freund

Theresa Seley Professor in Management Science, Professor of Operations Research, MIT Sloan School of Management

Robert’s primary research interests are in convex optimization, computational complexity and related computational science, convex geometry, large-scale nonlinear optimization, and related mathematical systems.

Robert has served as co-editor of Mathematical Programming and as associate editor of several optimization and operations research journals. He received the Longuet-Higgins Prize in computer vision (2007) and numerous MIT teaching and education awards in conjunction with the MBA core course and textbook Data, Models, and Decisions: The Fundamentals of Management Science, co-authored with Dimitris Bertsimas.

Robert received his BA in Mathematics from Princeton University and MS and PhD in Operations Research from Stanford University.

Profile image of the program faculty, Retsef Levi

Retsef Levi

J. Spencer Standish (1945) Professor of Operations Management, Faculty Leader, MIT Leaders for Global Operations (LGO), MIT Sloan School of Management

Retsef’s research focuses on the design of analytical data-driven decision support models and tools addressing complex business and system design decisions under uncertainty in areas such as health and healthcare management, supply chain, procurement and inventory management, revenue management, pricing optimization, and logistics.

Retsef is the recipient of the NSF Faculty Early Career Development award, the 2008 INFORMS Optimization Prize for Young Researchers, the 2013 Daniel H. Wagner Prize, and the 2016 Harold W. Kuhn Award.

Retsef received a bachelor's degree in mathematics from Tel-Aviv University (Israel) in 2001 and a PhD in operations research from Cornell University in 2005.

Profile image of the program faculty, Rama Ramakrishnan

Rama Ramakrishnan

Professor of the Practice, MIT Sloan School of Management

Rama’s research and teaching interests focus on the application of data science and machine learning techniques to problems in industry and the creation of intelligent products and services created by the algorithmic use of data.

Rama has over two decades of experience as a data science entrepreneur and tech executive. He has founded or served as a senior executive in four software companies that have been acquired by technology giants such as Oracle, Salesforce, and Demandware. He remains an active member of the start-up space as an advisor, angel investor, and board member.

Rama holds a BTech degree from the Indian Institute of Technology, Chennai and MS and PhD degrees from MIT.

Certificate

Example image of certificate that will be awarded after successful completion of this program

Certificate

Get recognized! Upon successful completion of this program, MIT xPRO grants a certificate of completion to participants and 36 Continuing Education Units (CEUs). This program is graded pass or fail; participants must receive 75% to pass and obtain the certificate of completion.

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