Data Wrangling, Analysis and AB Testing with SQL
- 3.4
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Course Summary
Learn how to collect, clean, and analyze data to make data-driven decisions with this comprehensive course in data wrangling and analysis.Key Learning Points
- Gain practical skills in data collection, cleaning, and analysis using Python
- Learn how to design and conduct A/B tests to make data-driven decisions
- Understand the importance of data visualization and exploratory data analysis
Related Topics for further study
Learning Outcomes
- Ability to collect and clean data using Python
- Understanding of how to design and conduct A/B tests
- Proficiency in data visualization and exploratory data analysis
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge in Python
- Familiarity with statistical concepts
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
- Video lectures
- Assignments and quizzes
Similar Courses
- Data Analysis and Interpretation
- Applied Data Science with Python
- Data Science Methodology
Related Education Paths
Related Books
Description
This course allows you to apply the SQL skills taught in “SQL for Data Science” to four increasingly complex and authentic data science inquiry case studies. We'll learn how to convert timestamps of all types to common formats and perform date/time calculations. We'll select and perform the optimal JOIN for a data science inquiry and clean data within an analysis dataset by deduping, running quality checks, backfilling, and handling nulls. We'll learn how to segment and analyze data per segment using windowing functions and use case statements to execute conditional logic to address a data science inquiry. We'll also describe how to convert a query into a scheduled job and how to insert data into a date partition. Finally, given a predictive analysis need, we'll engineer a feature from raw data using the tools and skills we've built over the course. The real-world application of these skills will give you the framework for performing the analysis of an AB test.
Knowledge
- Validate and clean a dataset
- Assess and create datasets to answer your questions
- Solve problems using SQL
- Build a simple testing framework to touch on AB Testing
Outline
- Data of Unknown Quality
- Course Introduction
- Introduction of Data of Unknown Quality
- Introduction to the Course Dataset
- Error Codes (Solution)
- Flexible Data Formats
- Flexible Data Formats (Solution)
- Identifying Unreliable Data + Nulls
- Unreliable Data - Part 1 (Solution)
- Unreliable Data -Part 2 (Solution)
- Unreliable Data - Part 3 (Solution)
- Answering Ambiguous Questions
- Users Table (Solution)
- A Note From UC Davis
- Connecting to Mode Analytics
- Not Dumb Questions
- Are You Connected to Mode Analytics?
- Module 1 Quiz
- Creating Clean Datasets
- Creating Clean Datasets Introduction
- Tools of the Trade: Coding Guide with Sublime Text (Optional)
- Data Types
- What is a Dependency?
- Create a View-Item Table
- Turn a Clean Query Into a Table (Activity/Solution)
- Hierarchy of Data
- Create User Info Table
- Create a User Snapshot Table (Activity/Solution)
- Partitions in Hive (Optional)
- Data Types (Quiz)
- Dependencies (Quiz)
- Turn a Clean Query Into a Table (Quiz)
- Module 2 Quiz
- SQL Problem Solving
- Introduction to SQL Problem Solving
- Map Out Your Joins
- Test Queries vs Final Queries
- Example: Create an Aggregate Table with a Rolling Date Period
- Rolling Orders (Solution)
- Find Each User's Most Recently Viewed Page for an Email Campaign
- Review Windowing Functions
- Promo Email (Solution)
- Product Analysis
- Product Analysis (Solution)
- Coding with Style
- Reorder and Connect Tables
- Module 3 Quiz
- Case Study: AB Testing
- How is AB Testing Used?
- Statistics Refresher (Optional)
- Test Assignments
- Test Assignments (Solution)
- Create a New Metric
- Creating a New Metric (Solution)
- Analyze Results
- Analyzing Results (Solution)
- Peer Review Overview
- Course Summary
- Additional Thoughts for the Final Project
- Additional Practice and Reading (Optional)
- Prepared for the Final Project?
Summary of User Reviews
Discover the art of data wrangling, analysis and A/B testing with this online course from Coursera. Users have given this course high praise for its practicality and relevance to real-world scenarios.Key Aspect Users Liked About This Course
The practicality of the course material has been highly praised by many users.Pros from User Reviews
- The course material is highly relevant to real-world scenarios.
- The instructors are knowledgeable and experienced in the field.
- The course is structured in a way that is easy to follow and understand.
- The hands-on approach to learning is very effective.
- The course provides a solid foundation for further learning in the field.
Cons from User Reviews
- Some users have reported technical issues with the course platform.
- The pace of the course may be too fast for some learners.
- The course may not be suitable for those with no prior experience in data analysis.
- The course may not cover advanced topics in enough detail.
- Some users have reported that the course assessments are too difficult.
Katrina Glaeser
3.4 Raiting
Data Wrangling, Analysis and AB Testing with SQL
- 3.4
-
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This course allows you to apply the SQL skills taught in “SQL for Data Science” to four increasingly complex and authentic data science inquiry case studies....
R Data Science Capstone Project
- 0.0
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Apply various data analysis and visualization skills and techniques you have learned by taking on the role of a data scientist working with real-world data sets....
