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Statistics for Data Science with Python
  • Level Foundation
  • Duration 14 hours
  • Course by IBM
  • Offered by

About

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts. At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided. After completing this course, a learner will be able to: ✔Calculate and apply measures of central tendency and measures of dispersion to grouped and ungrouped data. ✔Summarize, present, and visualize data in a way that is clear, concise, and provides a practical insight for non-statisticians needing the results. ✔Identify appropriate hypothesis tests to use for common data sets. ✔Conduct hypothesis tests, correlation tests, and regression analysis. ✔Demonstrate proficiency in statistical analysis using Python and Jupyter Notebooks.

Modules

Course Introduction and Python Basics

    • 2 Videos
    • 2 Readings
    • 1 ExternalTool
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1 External Tool

  • (Optional) Python Review

2 Videos

  • Welcome from your Instructors!
  • Python Packages for Data Science

2 Readings

  • Course Overview
  • (Optional) Basics of Jupyter Notebooks

Understanding the basics of Descriptive Statistics

    • 4 Videos
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4 Videos

  • Welcome to Statistics!
  • Types of Data
  • Measure of Central Tendency
  • Measure of Dispersion

Introduction and Descriptive Statistics labs and assessments

    • 2 Quiz
    • 1 ExternalTool
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1 External Tool

  • Lab: Descriptive Statistics

2 Quiz

  • Practice Quiz - Introduction to Descriptive Statistics
  • Introduction and Descriptive Statistics

Charts and graphs in statistics

    • 4 Videos
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4 Videos

  • Visualization Fundamentals
  • Statistics by Groups
  • Statistical Charts
  • Introducing the teacher's rating data

Data Visualization labs and assessments

    • 2 Quiz
    • 1 ExternalTool
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1 External Tool

  • Lab: Visualizing Data

2 Quiz

  • Practice Quiz - Data Visualization
  • Data Visualization

Probability Distributions

    • 5 Videos
    • 2 Readings
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5 Videos

  • Random Numbers and Probability Distributions
  • State your hypothesis
  • Normal Distribution
  • T distribution
  • Probability of Getting a High or Low Teaching Evaluation

2 Readings

  • Alpha (α) and P-value
  • Standard Normal Table

Introduction to Probability Distributions labs and assessments

    • 2 Quiz
    • 1 ExternalTool
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1 External Tool

  • Lab: Introduction to Probability Distributions

2 Quiz

  • Practice Quiz - Introduction to Probability Distribution
  • Introduction to Probability Distribution

Testing for mean differences and relationships

    • 5 Videos
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5 Videos

  • z-test or t-test
  • Dealing with tails and rejections
  • Equal vs unequal variances
  • ANOVA
  • Correlation tests

Hypothesis testing labs and assessments

    • 1 ExternalTool
    • 2 Assignment
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2 Assignment

  • Practice Quiz - Hypothesis Testing
  • Hypothesis Testing

1 External Tool

  • Lab: Hypothesis Testing

Regression in place of hypothesis testing

    • 4 Videos
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4 Videos

  • Regression - the workhorse of statistical analysis
  • Regression in place of t - test
  • Regression in place of ANOVA
  • Regression in place of Correlation

Regression Analysis labs and assessments

    • 1 ExternalTool
    • 2 Assignment
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2 Assignment

  • Practice Quiz - Regression analysis
  • Regression Analysis

1 External Tool

  • Lab: Regression Analysis

Instructions for Projects

    • 8 Readings
    • 1 PeerReview
    • 2 ExternalTool
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2 External Tool

  • Peer Graded Assignment
  • (Optional):Obtain an IBM Cloud Feature Code

1 Peer Review

  • Create and Share your Jupyter Notebook

8 Readings

  • Project Case Scenario
  • Overview of Project Tasks
  • Task 1: Become familiar with the dataset
  • Task 2: Generate Descriptive Statistics and Visualizations
  • Task 3: Use the appropriate tests to answer the questions provided.
  • (Optional):Task 2: Create or Login into IBM cloud to use Watson Studio.
  • (Optional): Load in the Dataset in your Jupyter Notebook
  • (Optional): Share your Jupyter Notebook.

Final Exam

    • 1 Assignment
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1 Assignment

  • Final Exam

Cheat sheet for Statistical Analysis in Python

    • 1 Plugin
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IBM Digital Badge

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Opt-in to receive your badge!

1 Readings

  • IBM Digital Badge

Auto Summary

Embark on a foundational journey into Statistics for Data Science with Python, led by expert instructors on Coursera. This comprehensive course covers essential statistical methods and data analysis techniques, including data gathering, visualization, hypothesis testing, ANOVA, regression, and correlation analysis, all using Python and Jupyter Notebooks. Ideal for aspiring Data Scientists, Analysts, Business Analysts, and Researchers, the course spans 840 minutes and offers both Starter and Professional subscription options. No prior statistics or computer science knowledge is required, though familiarity with Python is recommended.

Murtaza Haider
Instructors

Murtaza Haider

Aije Egwaikhide
Instructors

Aije Egwaikhide