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Statistical Thinking for Data Science and Analytics

edX, Online
Length
5 weeks
Next course start
Start anytime See details
Course delivery
Self-Paced Online
Length
5 weeks
Next course start
Start anytime See details
Course delivery
Self-Paced Online
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Course description

Statistical Thinking for Data Science and Analytics

This statistics and data analysis course will pave the statistical foundation for our discussion on data science.

You will learn how data scientists exercise statistical thinking in designing data collection, derive insights from visualizing data, obtain supporting evidence for data-based decisions and construct models for predicting future trends from data.

Upcoming start dates

1 start date available

Start anytime

  • Self-Paced Online
  • Online
  • English

Suitability - Who should attend?

Prerequisites

High School Math. Some exposure to computer programming.

Outcome / Qualification etc.

What you'll learn

  • Data collection, analysis and inference
  • Data classification to identify key traits and customers
  • Conditional Probability-How to judge the probability of an event, based on certain conditions
  • How to use Bayesian modeling and inference for forecasting and studying public opinion
  • Basics of Linear Regression
  • Data Visualization: How to create use data to create compelling graphics

Training Course Content

Introduction to Data Science

Statistical Thinking

  • Examples of Statistical Thinking
  • Numerical Data, Summary Statistics
  • From Population to Sampled Data
  • Different Types of Biases
  • Introduction to Probability
  • Introduction to Statistical Inference

Statistical Thinking 2

  • Association and Dependence
  • Association and Causation
  • Conditional Probability and Bayes Rule
  • Simpsons Paradox, Confounding
  • Introduction to Linear Regression
  • Special Regression Models

Exploratory Data Analysis and Visualization

  • Goals of statistical graphics and data visualization
  • Graphs of Data
  • Graphs of Fitted Models
  • Graphs to Check Fitted Models
  • What makes a good graph?
  • Principles of graphics

Introduction to Bayesian Modeling

  • Bayesian inference: combining models and data in a forecasting problem
  • Bayesian hierarchical modeling for studying public opinion
  • Bayesian modeling for Big Data

Course delivery details

This course is offered through Colgate University, a partner institute of EdX.

7-10 hours per week

Expenses

  • Verified Track -$99
  • Audit Track - Free
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