IBM - AI Workflow: Data Analysis and Hypothesis Testing
- Offered byCoursera
AI Workflow: Data Analysis and Hypothesis Testing at Coursera Overview
Duration | 11 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
AI Workflow: Data Analysis and Hypothesis Testing at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
AI Workflow: Data Analysis and Hypothesis Testing at Coursera Course details
- 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.
AI Workflow: Data Analysis and Hypothesis Testing at Coursera Curriculum
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
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionAI Workflow: Data Analysis and Hypothesis Testing