SQL for Data Science Capstone Project (Coursera)

SQL for Data Science Capstone Project (Coursera)

Data science is a dynamic and growing career field that demands knowledge and skills-based in SQL to be successful. This course is designed to provide you with a solid foundation in applying SQL skills to analyze data and solve real business problems. Whether you have successfully completed the other courses in the Learn SQL Basics for Data Science Specialization or are taking just this course, this project is your chance to apply the knowledge and skills you have acquired to practice important SQL querying and solve problems with data. You will participate in your own personal or professional journey to create a portfolio-worthy piece from start to finish.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

You will choose a dataset and develop a project proposal. You will explore your data and perform some initial statistics you have learned through this specialization. You will uncover analytics for qualitative data and consider new metrics that make sense from the patterns that surface in your analysis. You will put all of your work together in the form of a presentation where you will tell the story of your findings. Along the way, you will receive feedback through the peer-review process. This community of fellow learners will provide additional input to help you refine your approach to data analysis with SQL and present your findings to clients and management.
What You Will Learn

  • Develop a project proposal and select your data
  • Perform descriptive statistics as part of your exploratory analysis
  • Develop metrics and perform advanced techniques in SQL
  • Present your findings and make recommendations

Course 4 of 4 in the Learn SQL Basics for Data Science Specialization.

Syllabus

WEEK 1
Project Proposal and Data Selection/Preparation
In this first milestone, you will select your client and import your dataset. You will begin to explore your data to understand it and make assumptions about your data. You will draft a project proposal to act as a guide as you explore your data and prove or disprove your hypotheses.

WEEK 2
Descriptive Stats & Understanding Your Data
In this milestone, you will start to execute your project proposal. You will start looking at your data and perform initial statistic models to explore your data and determine what you have available to you.

WEEK 3
Beyond Descriptive Stats (Dive Deeper/Go Broader)
In this milestone, you will go beyond the descriptive statistics you completed in the last milestone. This milestone is really about diving deeper to analyze your data, beyond descriptive stats. Maybe you need to analyze qualitative data or textual data to get a full picture.

WEEK 4
Presenting Your Findings (Storytelling)
In this milestone, you will present your findings. You will identify your audience and create a presentation tailored to them. You will be able to tell the story of analyses and make recommendations.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Data Modeling and Applications (Coursera) Coursera
Illinois Tech

Data Modeling and Applications (Coursera)

This course introduces the student to the use of a database management system to provide the software and database necessary to upgrade a DBMS system. Database structures, applications, network, relational and hierarchical data models, applicate programs development, query systems, file security, and the role of the database administrator will be studied. A course fee may apply.

Nov 23rd 2026
5-12 Weeks
Advanced Linear Models for Data Science 1: Least Squares (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 1: Least Squares (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Nov 16th 2026
5-12 Weeks
Global Statistics - Composite Indices for International Comparisons (Coursera) Coursera
University of Geneva

Global Statistics - Composite Indices for International Comparisons (Coursera)

In this course on global statistics, offered by the University of Geneva jointly with the ETH Zürich KOF, you will learn the general approach of constructing composite indices and some of resulting problems. We will discuss the technical properties, the internal structure (like aggregation, weighting, stability of time series), the primary data used and the variable selection methods. These concepts will be illustrated using a sample of the most popular composite indices. We will try to address not only statistical questions but also focus on the distinction between policy-, media- and paradigm-driven indicators.

Nov 23rd 2026
5-12 Weeks
Population Health: Predictive Analytics (Coursera) Coursera
Leiden University

Population Health: Predictive Analytics (Coursera)

Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better. In this course, you will learn how to make accurate prediction tools, and how to assess their validity. First, we will discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. Then, we look at key concepts such as study design, sample size and overfitting.

Nov 23rd 2026
4 Weeks
Process Mining: Data science in Action (Coursera) Coursera
Eindhoven University of Technology

Process Mining: Data science in Action (Coursera)

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis.

Nov 16th 2026
5-12 Weeks
Understanding China, 1700-2000: A Data Analytic Approach, Part 1 (Coursera) Coursera
The Hong Kong University of Science and Technology - HKUST

Understanding China, 1700-2000: A Data Analytic Approach, Part 1 (Coursera)

The purpose of this course is to summarize new directions in Chinese history and social science produced by the creation and analysis of big historical datasets based on newly opened Chinese archival holdings, and to organize this knowledge in a framework that encourages learning about China in comparative perspective. Our course demonstrates how a new scholarship of discovery is redefining what is singular about modern China and modern Chinese history.

Nov 23rd 2026
5-12 Weeks
HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Administrative/IT Perspective) (Coursera) Coursera
Columbia University

HI-FIVE: Health Informatics For Innovation, Value & Enrichment (Administrative/IT Perspective) (Coursera)

HI-FIVE (Health Informatics For Innovation, Value & Enrichment) Training is an approximately 10-hour online course designed by Columbia University in 2016, with sponsorship from the Office of the National Coordinator for Health Information Technology (ONC). The training is role-based and uses case scenarios. No additional hardware or software are required for this course. Our nation’s healthcare system is changing at a rapid pace.

Nov 23rd 2026
4 Weeks
Data Science Ethics (Coursera) Coursera
University of Michigan

Data Science Ethics (Coursera)

What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches? This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values.

Nov 16th 2026
4 Weeks
Machine Translation (Coursera) Coursera
Karlsruhe Institute of Technology - KIT

Machine Translation (Coursera)

Welcome to the CLICS-Machine Translation MOOC. This MOOC explains the basic principles of machine translation. Machine translation is the task of translating from one natural language to another natural language. Therefore, these algorithms can help people communicate in different languages. Such algorithms are used in common applications, from Google Translate to apps on your mobile device.

Nov 23rd 2026
5-12 Weeks
Ferramentas para Ciência de Dados: Introdução ao R (Coursera) Coursera
FIA Business School

Ferramentas para Ciência de Dados: Introdução ao R (Coursera)

Nossas boas-vindas ao Curso Ferramentas para Ciência de Dados: Introdução ao R. Neste curso, você aprenderá que o mundo evoluiu muito quando o assunto é tomada de decisão baseada em dados e já não é possível comparar a quantidade de informações a que temos acesso atualmente com o que tínhamos disponíveis décadas atrás.

Nov 30th 2026
4 Weeks
Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera) Coursera
Johns Hopkins University

Advanced Linear Models for Data Science 2: Statistical Linear Models (Coursera)

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a basic understanding of statistics and regression models; at least a little familiarity with proof based mathematics; basic knowledge of the R programming language.

Nov 23rd 2026
4 Weeks