Online Course – Certified Professional Internship in Statistical Learning for Data Science from Google and the University of Colorado Boulder

Advanced statistics for proficiency in data science. Master the knowledge and skills to effectively communicate model choices and interpretations.

Suggested by: Coursera (What is Coursera?)

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Unsupervised machine learning
  • Repeat sampling
  • Regression
  • Programming in R
  • Spleens

What you will learn in the course

Courses for which the course is suitable

  • Data Scientist
  • Data Analyst
  • Algorithm developer
  • Statistical analyst
  • Machine Learning Engineer
  • Artificial Intelligence Expert
  • Information Analyst
  • Statistical Modeler

Internship – a three-part course series

Learning statistics is an important specialization for those who want to build a career in data science or upgrade their skills in the field. The program builds on your basic knowledge of statistics and equips you with advanced model selection techniques, including:

  • Regression
  • Classification
  • Trees
  • SVM
  • Unsupervised learning
  • splines
  • Sampling methods

In addition, you will gain a deep understanding of coefficient estimation and interpretation, which will be valuable when you need to explain and justify your models to clients and companies. Through this specialization, you will develop theoretical knowledge and communication skills that will allow you to clarify the principles behind your model choices and coefficient interpretations.

Master of Science in Data Science (MS-DS) Program

This specialization is available for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) program offered on the Coursera platform. The MS-DS is an interdisciplinary program that brings together faculty from the following departments:

  • Applied mathematics
  • computer science
  • Information Sciences

With performance-based admissions and no application process, the MS-DS is suitable for individuals with a broad background in mathematics, statistics, computer science, or information science. For more information about the MS-DS program, visit the Coursera website.

Hands-on Learning Project

During the internship, learners will perform numerous programming tasks designed to help them master the concepts of learning statistics, including:

  • Regression
  • Classification
  • Trees
  • SVM
  • Unsupervised learning
  • splines
  • Sampling methods

Details of the courses that make up the specialization

Regulation and classification

Course 1

  • 34 hours
  • 3.9 (12 ratings)

Course Details

What you’ll learn:
  • Explain why statistical learning is important and how it can be used.
  • Identify the advantages, disadvantages, and criticisms of different models and select the most appropriate model for a given statistical problem.
  • Determine what type of data and problems require supervised versus unsupervised techniques.
Skills you will acquire:
  • Category: Modeling
  • Category: Data Science
  • Category: Machine Learning
  • Category: Statistical Analysis
  • Category: R Programming

Sampling, selection, and splines

Course 2

  • 15 hours

Course Details

What you’ll learn:
  • Apply sampling methods to obtain additional information about fitted models.
  • Optimize the matching procedure to improve prediction and interpretation accuracy.
  • Identify the advantages and approaches of nonlinear models.
Skills you will acquire:
  • Category: Statistics
  • Category: Data Science
  • Category: Choice
  • Category: Sample
  • Category: Splines

Trees, SVM and unsupervised learning

Course 3

  • 12 hours

Course Details

What you’ll learn:
  • Describe the advantages and disadvantages of trees, and how and when to use them.
  • Apply SVM for binary classification or for K > 2 classes.
  • Analyze the advantages and disadvantages of neural networks compared to other machine learning algorithms, such as SVM.
Skills you will acquire:
  • Category: Statistics
  • Category: Unsupervised Learning
  • Category: Regression
  • Category: Trees
  • Category: Support Vector Machine (SVM)