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AI Workflow: Data Analysis and Hypothesis Testing

Approx. 11 hours to complete

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Course Summary

This course provides an introduction to data analysis and hypothesis testing, as well as an overview of the IBM AI workflow. Students will learn how to use Python and Jupyter notebooks to perform data analysis and hypothesis testing.

Key Learning Points

Gain an understanding of the IBM AI workflow and how it relates to data analysis and hypothesis testing
Learn how to use Python and Jupyter notebooks for data analysis
Understand the basics of hypothesis testing and how to apply it to your data

Job Positions & Salaries of people who have taken this course might have

- USA: $60,000 - $120,000
- India: ₹400,000 - ₹1,200,000
- Spain: €25,000 - €45,000
- USA: $60,000 - $120,000
- India: ₹400,000 - ₹1,200,000
- Spain: €25,000 - €45,000
- USA: $80,000 - $150,000
- India: ₹600,000 - ₹2,200,000
- Spain: €35,000 - €60,000
- USA: $60,000 - $120,000
- India: ₹400,000 - ₹1,200,000
- Spain: €25,000 - €45,000
- USA: $80,000 - $150,000
- India: ₹600,000 - ₹2,200,000
- Spain: €35,000 - €60,000
- USA: $100,000 - $180,000
- India: ₹800,000 - ₹3,000,000
- Spain: €45,000 - €80,000

Learning Outcomes

Ability to use Python and Jupyter notebooks for data analysis
Understanding of hypothesis testing and its applications
Familiarity with the IBM AI workflow

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python
Familiarity with Jupyter notebooks

Course Difficulty Level

Beginner

Course Format

Online
Self-paced

Similar Courses

IBM AI Engineering
Data Science Essentials

Related Education Paths

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Description

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.

Outline

Data Analysis
EDA Overview
Introduction to Data Visualizations
Data Visualizations
Introduction to Missing Values
Missing Values
Case Study Introduction
Why is Exploratory Data Analysis Necessary?
Data Visualization: Through the Eyes of Our Working Example
Getting Started / Unit Materials
Data Visualization in Python
Missing Data: Introduction
Strategies for Missing Data
Categories of Missing Data
Simple Imputation
Bayesian Imputation
Case Study: Getting started
Summary/Review
Check for Understanding: EDA
Check for Understanding: Data Visualization
Check for Understanding: Missing Data
Data Analysis Module Quiz

Data Investigation
Introduction to hypothesis testing
Hypothesis Testing
Case Study Introduction
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)
p-value Limitations
Multiple Testing
Explain Methods for Dealing with Multiple Testing
Getting Started
Import the Data
Data Processing (Includes Assessment)
Summary/Review
Check for Understanding: Hypothesis Testing
Check for Understanding: Hypothesis Testing Limitations
Data Investigation Module Quiz

Summary of User Reviews

Discover IBM AI Workflow: Data Analysis and Hypothesis Testing course on Coursera. Students give it high ratings for its practical approach and comprehensive content. Many users appreciated the course for its real-world examples and hands-on exercises.

Key Aspect Users Liked About This Course

Real-world examples and hands-on exercises

Pros from User Reviews

The course covers a wide range of topics related to Data Analysis and Hypothesis Testing
The course content is well-organized and easy to follow
The instructors provide practical examples and real-world use cases that help students understand the concepts better

Cons from User Reviews

Some users found the pace of the course to be too slow
A few users reported technical issues with the course platform
The course does not cover advanced topics in Data Analysis and Hypothesis Testing

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