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Rav Ahuja

A majority of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language for communicating with and extracting data from databases. A working knowledge of databases and the SQL language is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and to help you learn and apply foundational knowledge of the SQL and R languages. It is also intended to get you started with performing SQL access in a data science environment.

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A majority of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language for communicating with and extracting data from databases. A working knowledge of databases and the SQL language is a must if you want to become a data scientist. The purpose of this course is to introduce relational database concepts and to help you learn and apply foundational knowledge of the SQL and R languages. It is also intended to get you started with performing SQL access in a data science environment.

The emphasis in this course is on hands-on and practical learning, which means you will work with real databases, real data science tools, and real-world datasets. You will also create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. And you will learn how to access databases from Jupyter notebooks using SQL and R.

No prior knowledge of databases, SQL, R, or programming is required.

What you'll learn

  • Create and access a database instance on cloud

  • Write basic SQL statements: CREATE, DROP, SELECT, INSERT, UPDATE, DELETE

  • Filter, sort, group results, use built-in functions, compose nested queries, access multiple tables

  • Access databases from Jupyter using R and SQL to query real-world datasets

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Suitable for learners of all skill levels, from beginners new to data science to seasoned professionals looking to build relational database concepts
Helps you learn SQL and R, which are languages essential to becoming a data scientist
Utilises real-world datasets and databases, providing practical experience in working with real-world data
Instructors are Rav Ahuja

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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 SQL for Data Science with R with these activities:
Review PostgreSQL concepts
Reviewing PostgreSQL concepts will help you refresh your knowledge and ensure you have a solid foundation for the course.
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Show steps
  • Reread your notes from a previous course on PostgreSQL.
  • Watch online tutorials on PostgreSQL basics.
  • Complete practice exercises on PostgreSQL queries.
Complete Coursera Tutorial on SQL
Supplement course material with an introductory tutorial on SQL.
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  • Create a Coursera account.
  • Enroll in the "SQL for Data Science" tutorial.
  • Complete all modules and quizzes.
Review Basic R Programming
Ensure proficiency in R programming before starting the course.
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  • Go over R syntax and data structures.
  • Practice writing basic R functions.
  • Complete a small R programming project.
Eight other activities
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Complete SQL and R tutorials
Completing SQL and R tutorials will help you develop the skills necessary to work with databases and data sets.
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  • Find online tutorials on SQL and R for beginners.
  • Follow the tutorials step-by-step and complete the exercises.
  • Ask questions and seek help if you encounter any difficulties.
Read "Database Systems"
Review concepts in database management to prepare for the course.
Show steps
  • Purchase the book or access it online.
  • Read at least 3 chapters.
  • Summarize the key points of each chapter.
Practice SQL queries
Regular practice with SQL queries will improve your proficiency and confidence in working with databases.
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Show steps
  • Find online platforms or resources that offer SQL practice exercises.
  • Solve a variety of SQL queries of varying difficulty levels.
  • Review your answers and identify areas for improvement.
SQL Practice Problems
Practice SQL queries to improve understanding and confidence.
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  • Find a set of practice problems online.
  • Attempt to solve the problems without looking at the solutions.
  • Check your solutions against the provided answers.
Join a SQL Study Group
Enhance understanding through collaborative learning with peers.
Show steps
  • Find or create a study group with other students taking the course.
  • Meet regularly to discuss concepts, work on practice problems, and help each other with assignments.
  • Take turns leading discussions and presenting material.
Create a Database Project Proposal
Apply theoretical knowledge by proposing a database project.
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  • Identify a problem or opportunity that can be solved using a database.
  • Design the database schema.
  • Write a proposal outlining your project plan, including objectives, methodology, and expected outcomes.
Volunteer at a Local Data Science Organization
Gain practical experience and expand networking within the data science field.
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Show steps
  • Identify data science organizations in your area.
  • Contact the organizations to inquire about volunteer opportunities.
  • Participate in projects or events related to data science.
  • Network with professionals in the field.
Contribute to Open-Source SQL Projects
Deepen understanding of SQL by contributing to real-world projects.
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  • Find open-source SQL projects on platforms like GitHub.
  • Identify areas where you can contribute based on your skill level.
  • Submit pull requests with your code contributions.

Career center

Learners who complete SQL for Data Science with R will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use programming and analytical skills to solve complex problems and extract meaningful insights from data. Industries across the board are seeking skilled Data Scientists to aid in operational efficiency and problem-solving. This SQL for Data Science with R course can help you gain experience with using SQL and R to perform complex queries and analyze real-world datasets. Some future employers may require a Master's or PhD in Data Science or related field.
Data Analyst
Data Analysts help companies turn raw information into usable insights. As a fast-growing field, it's expected to see a 20% job growth between 2019 and 2029. This course in SQL for Data Science with R can help you build a foundation in data analysis, as you'll learn how to communicate with and extract data from databases. Some future employers may even require a Master's degree in a related field.
Database Administrator
Database Administrators ensure database systems operate smoothly, efficiently, and securely. As the amount of data companies collect continues to grow, the need for skilled Database Administrators will continue to rise. This SQL for Data Science with R course can help you build a foundation in database management, as you'll learn how to create and access a database instance on cloud, write basic SQL statements, and more.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in a variety of industries, including manufacturing, transportation, and healthcare. This SQL for Data Science with R course may be helpful for those considering a career as an Operations Research Analyst, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Statistician
Statisticians collect, analyze, interpret, and present data. They use their skills to help businesses and organizations make informed decisions. This SQL for Data Science with R course may be helpful for those considering a career in Statistics, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Data Architect
Data Architects design and manage the architecture of data systems. They work with stakeholders to understand business requirements and translate those requirements into technical specifications. This SQL for Data Science with R course may be helpful for those considering a career in Data Architecture, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Software Engineer
Software Engineers design, develop, and maintain software applications. With the increasing importance of data in software development, Software Engineers with skills in data analysis and database management are in high demand. This SQL for Data Science with R course may be useful for those considering a career in Software Engineering, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They work on a variety of projects, such as developing self-driving cars, improving medical diagnosis, and personalizing online experiences. This SQL for Data Science with R course may be helpful for those considering a career in Machine Learning Engineering, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Market Researcher
Market Researchers gather and analyze data about markets, customers, and competitors. They use this information to help businesses make informed decisions about product development, marketing campaigns, and other business strategies. This SQL for Data Science with R course may be helpful for those considering a career in Market Research, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Information Security Analyst
Information Security Analysts protect computer networks and systems from unauthorized access, use, disclosure, disruption, modification, or destruction. With the increasing amount of data being stored and processed electronically, Information Security Analysts are in high demand. This SQL for Data Science with R course may be helpful for those considering a career in Information Security, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Risk Analyst
Risk Analysts identify, assess, and manage risks. They work in a variety of industries, including finance, insurance, and healthcare. This SQL for Data Science with R course may be helpful for those considering a career as a Risk Analyst, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work on the infrastructure that supports data analysis, machine learning, and other data-driven initiatives. This SQL for Data Science with R course may be useful, as it will help you to learn about database concepts and how to communicate with and extract data from databases.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions about financial markets. They play a vital role in helping investment firms make informed investment decisions. This SQL for Data Science with R course may be helpful for those considering a career in Quantitative Finance, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This SQL for Data Science with R course may be helpful for those considering a career as an Actuary, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.
Business Analyst
Business Analysts help organizations understand their business needs and develop solutions to improve their operations. With a growing emphasis on data-driven decision-making, Business Analysts are in high demand. This SQL for Data Science with R course may be useful for getting started on your path to becoming a Business Analyst, as you'll learn how to write basic SQL statements and use SQL and R to access databases and analyze real-world datasets.

Reading list

We've selected nine 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 SQL for Data Science with R.
Comprehensive guide to R for data science. It covers all the fundamentals of R, including data types, data structures, and functions. It also discusses more advanced topics such as data visualization and machine learning.
Classic textbook on statistical learning. It covers all the fundamentals of statistical learning, including supervised learning, unsupervised learning, and ensemble methods. It great resource for both beginners and experienced statistical learning practitioners.
Collection of over 500 SQL recipes. It provides solutions to common SQL problems, such as data manipulation, data aggregation, and data filtering. It valuable resource for both beginners and experienced SQL users.
Comprehensive guide to data science for business. It covers all the fundamentals of data science, including data collection, data cleaning, and data analysis. It also discusses more advanced topics such as machine learning and artificial intelligence.
Comprehensive guide to data science from scratch. It covers all the fundamentals of data science, including data collection, data cleaning, and data analysis. It also discusses more advanced topics such as machine learning and artificial intelligence.
Quick and easy introduction to R. It covers all the basics of R, including data types, data structures, and functions. It great resource for beginners who want to learn R quickly.
Quick and easy introduction to SQL. It covers all the basics of SQL, including data types, tables, queries, and joins. It great resource for beginners who want to learn SQL quickly.
Quick and easy introduction to machine learning. It covers all the basics of machine learning, including data preprocessing, model building, and model evaluation. It great resource for beginners who want to learn machine learning quickly.

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