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AI Workflow: Data Analysis and Hypothesis Testing
  • Level Expert
  • Duration 11 hours
  • Course by IBM
  • Offered by

About

This is the second course in the IBM AI Enterprise Workflow Certification specialization.  You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.   In this course you will begin your work for a hypothetical streaming media company by doing exploratory data analysis (EDA).  Best practices for data visualization, handling missing data, and hypothesis testing will be introduced to you as part of your work.  You will learn techniques of estimation with probability distributions and extending these estimates to apply null hypothesis significance tests. You will apply what you learn through two hands on case studies: data visualization and multiple testing using a simple pipeline.   By the end of this course you should be able to: 1.  List several best practices concerning EDA and data visualization 2.  Create a simple dashboard in Watson Studio 3.  Describe strategies for dealing with missing data 4.  Explain the difference between imputation and multiple imputation 5.  Employ common distributions to answer questions about event probabilities 6.  Explain the investigative role of hypothesis testing in EDA 7.  Apply several methods for dealing with multiple testing   Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Course 1 of the IBM AI Enterprise Workflow specialization and have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

Modules

Exploratory Data Analysis

    • 1 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding: EDA

1 Videos

  • EDA Overview

1 Readings

  • Why is Exploratory Data Analysis Necessary?

Data Visualization

    • 2 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding: Data Visualization

1 Labs

  • Case Study Answer Key Notebook

2 Videos

  • Introduction to Data Visualizations
  • Data Visualizations

3 Readings

  • Data Visualization: Through the Eyes of Our Working Example
  • Getting Started / Unit Materials
  • Data Visualization in Python

Missing Data

    • 2 Videos
    • 5 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding: Missing Data

2 Videos

  • Introduction to Missing Values
  • Missing Values

5 Readings

  • Missing Data: Introduction
  • Strategies for Missing Data
  • Categories of Missing Data
  • Simple Imputation
  • Bayesian Imputation

Case Study - Data Visualization

    • 1 Videos
    • 1 Readings
    • 2 PeerReview
Show info about module content

2 Peer Review

  • Visualization and Imputation
  • Build a Deliverable!

1 Videos

  • Case Study Introduction

1 Readings

  • Case Study: Getting started

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Analysis Module Quiz

1 Readings

  • Summary/Review

Estimation and hypothesis testing

    • 2 Videos
    • 7 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Check for Understanding: Hypothesis Testing

2 Videos

  • Introduction to hypothesis testing
  • Hypothesis Testing

7 Readings

  • TUTORIAL: IBM Watson Studio dashboard
  • Hypothesis Testing: Through the eyes of our Working Example
  • Overview
  • Statistical Inference
  • Business Scenarios and Probability
  • Variants on t-tests
  • One-way Analysis of Variance (ANOVA)

Hypothesis testing - limitations

    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Check for Understanding: Hypothesis Testing Limitations

2 Readings

  • p-value Limitations
  • Multiple Testing

Case Study - Multiple testing

    • 1 Videos
    • 4 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Case Study Answer Key Notebook

1 Videos

  • Case Study Introduction

4 Readings

  • Explain Methods for Dealing with Multiple Testing
  • Getting Started
  • Import the Data
  • Data Processing (Includes Assessment)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Investigation Module Quiz

1 Readings

  • Summary/Review

Auto Summary

"AI Workflow: Data Analysis and Hypothesis Testing" is the second course in the IBM AI Enterprise Workflow Certification specialization, offered by Coursera. It focuses on exploratory data analysis, data visualization best practices, handling missing data, and hypothesis testing, with hands-on case studies. Taught by IBM experts, the course spans approximately 660 minutes. Aimed at experienced data science practitioners, it builds on prior knowledge from Course 1, requiring skills in linear algebra, probability theory, statistics, Python, and IBM Watson Studio. Subscription options include Starter and Professional levels.

Mark J Grover
Instructors

Mark J Grover

Ray Lopez, Ph.D.
Instructors

Ray Lopez, Ph.D.