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NICF-Data Science Essentials(SF) LITHAN

Explore data visualization and exploration concepts with experts from MIT and Microsoft, and get an introduction to machine learning.

Enrollment in this course is by invitation only

About This Course

This course is part of the Microsoft Professional Program Certificate in Data Science.

Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft.

In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or with R, and Python on Azure stack.

What you'll learn

  • Explore the data science process
  • Probability and statistics in data science
  • Data exploration and visualization
  • Data ingestion, cleansing, and transformation
  • Introduction to machine learning
  • The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning

Meet the instructors

Course Staff Image #1

Dr. Steve Elston

Steve is a big data geek and data scientist, with over two decades of experience using R and S/SPLUS for predictive analytics and machine learning. He holds a PhD degree in Geophysics from Princeton University, and has led multi-national data science teams across various companies

Course Staff Image #2

Cynthia Rudin

Cynthia leads the Prediction Analysis Lab at MIT, and is associated with the Computer Science and Artificial Intelligence Laboratory and the Sloan School of Management. She holds a PhD in applied and computational mathematics from Princeton University, and was previously, an associate research scientist at the Center for Computational Learning Systems at Columbia U.

  1. Course Number

  2. Classes Start

    Jan 03, 2021
  3. Classes End

    Feb 02, 2021
  4. Estimated Effort

    3-4 hours per week