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Data Wrangling, Analysis and AB Testing with SQL

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.

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What's inside

Syllabus

Data of Unknown Quality

In this module, you will be able to create trustworthy analysis from a new set of data. You will be able to coalesce some nulls and identify unreliable data and discover reasons why data might be missing. You will also be able to answer ambiguous questions by defining new metrics.

Creating Clean Datasets

In this module, you will be able to name the main the categories of data types. You will be able to explain how the unfiltered data can be manipulated into a table where you can conduct data analysis. You will be able to discuss why a data warehouse is separate from a production database, and you will be able to use the tools you learned to create your own trustworthy tables.

SQL Problem Solving

In this module, you will be able to map out your joins and be able to highlight the level of detail needed for different kinds of questions. You will be able to practice answering data questions, which should help you feel ready to get asked a whole slough of questions, vague questions, ambiguous questions, or even poorly worded questions. Finally, you will develop a strategy for answering all those questions using data.

Case Study: AB Testing

In this module, you will be able to use your SQL skills to set up a basic AB testing system. You will be able to apply hypothesis testing to prove or disprove a hypothesis about how user behavior changed. You will be able to test and interpret the results using a metric or metrics that are tied directly to some business metrics. You will be able to test your SQL skills and give you the base experience you need to learn anything more complicated in terms of AB testing in the future.

Good to know

Know what's good

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, and possible dealbreakers

Provides you with practical knowledge of SQL to address data science questions using case studies

Helps you develop proficiency in using SQL for data analysis and problem-solving

Suitable for learners with prior experience in SQL and data analysis

Focuses on practical application of SQL skills, which can be valuable for career advancement

Covers essential concepts such as data cleaning, data manipulation, and analysis

Provides a strong foundation for further exploration in data science

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Reviews summary

Data wrangling, analysis and ab testing with sql

This course, part of the SQL for Data Science Specialization, teaches users how to wrangle and analyze data, and perform A/B testing using SQL. The course focuses on practical application, with students practicing on a real-world dataset. Students learn to apply SQL skills to solve business problems, and gain an understanding of how to design and conduct A/B tests. Overall, students find the course beneficial for improving their SQL skills and gaining practical experience in data analysis and A/B testing.

The course includes a module on data wrangling, which teaches students how to clean and prepare data for analysis. This involves removing errors, dealing with missing values, and transforming data into a format that is suitable for analysis.

"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."

"The course was average. The course was mostly theory with little practical application."

"The didactics are not really good on this course."

"Course content is interesting. However, the relation of different datasets is very very very ambiguous, it's difficult to finish the case study."

The course also covers data analysis, teaching students how to use SQL to explore data, identify patterns, and draw conclusions. This involves using SQL to perform aggregations, grouping, and filtering.

"In this module, you will be able to create trustworthy analysis from a new set of data."

"The course lectures and reading materials were excellent; some of the assignments were a bit unclear in terms of the directions."

"The course was fine to refresh my SQL knowledge."

"This course has very poor instructions, exercises are vague and all solutions don't always align with course."

"The course helped to practice the implementation of complicated SQL queries."

"This course is nice, but there are many points that make tasks extremely frustrating: vague legend about an on-line shop, unclear tasks in the testing part, uncomfortable Mode Workspaces, which are obligatory to use, and no ER schemes (at the start you will have to guess which tables are connected)."

The course concludes with a module on A/B testing, which teaches students how to use SQL to design and conduct A/B tests. This involves learning how to define hypotheses, create test groups, and measure the results of the test.

"The course provides very good knowledge and I think it's worth it."

"The course is very interesting and with a lot of examples and hands-on exercises."

"The best module helped us understand how to improve the analysis and testing tools to create the better final queries."

"The course is really beneficial. However, I could't comprehensively understand some points of it."

"It was a great course. It was challenging but I learned a lot."

The course emphasizes hands-on practice, with students completing numerous exercises and assignments throughout the course. This allows students to apply their learning to real-world data and to gain practical experience in data analysis and A/B testing.

"Overall a really great course to get the analytical side of the brain going! Some of the weeks were a little too open-ended, though."

"This course is good but can be better."

"It may seem that it's exercises and assignments are not clear at all, you have to spend too much time to understand what the instructor intends to ask."

The course is taught by Katrina Glaeser, a data scientist with over 10 years of experience. Glaeser is an expert in SQL and data analysis, and she brings her real-world experience to the course.

"A good course for learning and apply sql to conduct analysis on ab testing results."

"This course is difficult to follow and the exercises are not well explained."

The course is rated as intermediate level, and it is recommended that students have a basic understanding of SQL before taking the course. The course material is challenging, but it is accessible to students with a strong foundation in SQL.

"This course is not on par with the first one on this certification."

"This course has some room to improve the lecture video quality."

"The course did prepare me for the final project at the end, it was very confusing."

"This course was not well received due to its poor pacing, misleading questions, and overall disorganization."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Wrangling, Analysis and AB Testing with SQL with these activities:

Organize and Review Course Materials

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This activity helps you organize and consolidate your learning materials, making them easier to access and review, which can improve your retention and understanding.

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Gather all course materials, including lecture notes, assignments, quizzes, and exams.
Organize the materials into a logical structure.
Review the materials regularly.

Execute SQL Queries on Sample Datasets

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This activity allows you to practice executing SQL queries on sample datasets, which will reinforce your understanding of the SQL concepts covered in the course and help you develop proficiency in writing efficient and accurate SQL queries.

Browse courses on SQL Joins

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Obtain sample datasets from the course materials or other sources.
Write SQL queries to perform data cleaning, transformations, and aggregations on the sample datasets.
Execute the queries and verify the results against expected outputs.
Identify and correct any errors or inefficiencies in your queries.

Create a Visual Guide to SQL Concepts

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This activity encourages you to synthesize your understanding of SQL concepts and express them in a visual format, which can strengthen your comprehension and make it easier to share your knowledge with others.

Browse courses on Data Visualization

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Identify the key SQL concepts you want to visualize.
Choose a visual format, such as a flowchart, diagram, or infographic.
Create the visual guide, using clear and concise language and visuals.
Share your visual guide with others for feedback and discussion.

Show all three activities

Career center

Learners who complete Data Wrangling, Analysis and AB Testing with SQL will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst takes large amounts of raw data and turns it into actionable insights. Using SQL, a Data Analyst would be able to help a business improve through data-driven decision making. This course can help students gain the foundational skills necessary for Data Analysts to perform in their role. Particularly relevant to this role, the course covers topics such as data cleansing, data analysis, and A/B testing.

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist uses mathematics, statistics, and computing to solve problems. They use SQL to clean, transform, and analyze data in various formats. This course can help learners develop the necessary skills to work in this role by teaching them how to wrangle, analyze, and test data using SQL, which will be useful for performing data-driven analyses and extracting insights from data.

See salaries and explore the career path for Data Scientist

Business Analyst

A Business Analyst uses data analysis to identify and solve business problems. This course can help learners develop the SQL skills necessary for Business Analysts to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to use SQL to analyze data, make informed decisions, and improve business outcomes.

See salaries and explore the career path for Business Analyst

Statistician

A Statistician collects, analyzes, and interprets data to help organizations make informed decisions. This course can help learners develop the SQL skills that Statisticians need to perform their jobs, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to analyze and interpret data, draw conclusions, and make predictions.

See salaries and explore the career path for Statistician

Data Architect

A Data Architect designs and implements data management solutions. This course can help learners develop the SQL skills necessary for Data Architects to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to design and implement data architectures, manage data quality, and ensure data security.

See salaries and explore the career path for Data Architect

Database Administrator

A Database Administrator is responsible for the maintenance and performance of databases. This course can help learners develop the SQL skills necessary for Database Administrators to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to design and implement database systems, optimize database performance, and troubleshoot database issues.

See salaries and explore the career path for Database Administrator

Product Manager

A Product Manager is responsible for the development and launch of a product. This course can help learners develop the SQL skills necessary for Product Managers to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to analyze user data, identify product requirements, and make data-driven decisions.

See salaries and explore the career path for Product Manager

Data Engineer

A Data Engineer designs, builds, and maintains systems to manage and process data. This course can help learners develop the foundational skills necessary for Data Engineers to perform their role, such as data cleansing, data analysis, and A/B testing. By taking this course, learners can gain the skills to build scalable and reliable data pipelines, enabling them to support data-driven decision-making within an organization.

See salaries and explore the career path for Data Engineer

Data Visualization Specialist

A Data Visualization Specialist creates visual representations of data. This course can help learners develop the SQL skills necessary for Data Visualization Specialists to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to create clear and concise data visualizations that communicate insights effectively.

See salaries and explore the career path for Data Visualization Specialist

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze and predict financial data. This course can help learners develop the SQL skills necessary for Quantitative Analysts to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to analyze and interpret financial data, develop trading strategies, and make investment decisions.

See salaries and explore the career path for Quantitative Analyst

Data Science Manager

A Data Science Manager leads and manages data science teams. This course can help learners develop the SQL skills necessary for Data Science Managers to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to manage data science projects, communicate data analysis results, and lead teams of data scientists.

See salaries and explore the career path for Data Science Manager

User Experience Researcher

A User Experience Researcher studies how users interact with products and services. This course can help learners develop the SQL skills necessary for User Experience Researchers to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to collect and analyze user data, design user experiences, and evaluate the effectiveness of user interfaces.

See salaries and explore the career path for User Experience Researcher

Cloud Data Engineer

A Cloud Data Engineer designs, builds, and manages data pipelines in the cloud. This course can help learners develop the SQL skills necessary for Cloud Data Engineers to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to build and maintain scalable and reliable data pipelines in the cloud.

See salaries and explore the career path for Cloud Data Engineer

Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models. This course can help learners develop the SQL skills necessary for Machine Learning Engineers to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to prepare data for machine learning models, evaluate model performance, and deploy models into production.

See salaries and explore the career path for Machine Learning Engineer

Chief Data Officer

A Chief Data Officer is responsible for the overall data strategy and governance of an organization. This course may be useful for learners who aspire to become Chief Data Officers, as it can help them develop the SQL skills necessary to perform their daily tasks, such as data cleaning, data analysis, and A/B testing. By taking this course, learners can gain the skills to lead data initiatives, manage data resources, and ensure data compliance.

See salaries and explore the career path for Chief Data Officer

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Wrangling, Analysis and AB Testing with SQL.

SQL Queries for Mere Mortals

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This commonly used introductory textbook for SQL. It explains the syntax and structure of SQL in a clear and concise way, making it a good resource for beginners.

Data Wrangling, Analysis and AB Testing with SQL

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Data wrangling, analysis and ab testing with sql

Activities

Career center

Reading list

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