Data Engineering Foundations

Whether you want to become a Data Scientist, Data Analyst, or Data Engineer, knowing how to work with SQL (Structured Query Language) is an essential skill. SQL is a powerful language used for communicating with and extracting data from databases. In this hands-on course, you’ll learn how to retrieve, organize, and manage data using SQL, starting with the fundamentals and moving to more advanced techniques. You’ll also explore how to combine SQL with Python to work with databases and analyze data. By the end of this course, you’ll be able to: - Write SQL queries using SELECT, INSERT, UPDATE, and DELETE to retrieve and manage data. - Filter and organize data using WHERE, COUNT, DISTINCT, LIMIT, ORDER BY, GROUP BY, and built-in database functions. - Create and manage database tables using CREATE, ALTER, and DROP, and understand the differences between DDL and DML. - Query complex datasets using subqueries, string patterns, ranges, and INNER and OUTER JOINs to work with data across multiple tables. - Connect SQL with Python using Jupyter notebooks to access databases and integrate SQL into your data science workflow. - Explore advanced database concepts such as Stored Procedures, Views, and ACID transactions. Throughout the course, you will complete hands-on labs, work with real databases on the Cloud, and gain experience with real data science tools. In the final project, you’ll analyze multiple real-world datasets to demonstrate your ability to query and work with data. Ready to build your SQL skills? Join over 670,000 learners who have enrolled in this course. Earn a shareable certificate to showcase your data science skills on LinkedIn, your resume, or your CV.

Go from zero to working with real data in Python. No programming experience required. Data skills are some of the most in-demand skills in the job market. The U.S. Bureau of Labor Statistics projects data scientist employment to grow 34% from 2024 to 2034, much faster than average, with a median annual wage of $112,590.¹ Python is the language behind that work, and this course is where you start. This IBM course takes you from your first line of code to the skills data teams use every day. You'll start with Python basics, then build up through data structures, logic, functions, error handling, and object-oriented programming. From there, you'll work with real data using NumPy and Pandas, the industry-standard libraries for data analysis and AI, and learn to collect it from the web using REST APIs and web scraping with BeautifulSoup. You'll learn by doing. Across five modules, you'll complete more than 25 hands-on labs and practice projects in Jupyter Notebooks, the same environment professional data scientists use, with nothing to install or set up. By the end, you'll be able to write programs, clean and analyze datasets, pull data from live sources, and automate repetitive tasks, and you'll have practical work to point to in interviews. Whether you're aiming for a career in data science, data analytics, software development, data engineering, AI, or DevOps, or you want to add Python to the skills you already have, this course gives you the foundation to build on. Skills you'll gain include: - Writing Python programs using variables, data structures, loops, functions, and classes - Reading, writing, and processing data in CSV, JSON, and text formats - Analyzing and manipulating data with Pandas and NumPy - Collecting data from websites and services using REST APIs and web scraping - Working in Jupyter Notebooks to develop and document your code - Automating real-world tasks with Python scripts Join 1.5 million+ learners (4.6 stars from 43,000+ reviews, and 95% of learners liked this course). Finish with a shareable certificate you can add to your LinkedIn profile, resume, or CV. ¹ U.S. Bureau of Labor Statistics, Occupational Outlook Handbook: Data Scientists. Median wage as of May 2024; projected growth 2024 to 2034. This course builds foundational Python skills and is a first step toward data roles, not a guarantee of employment or salary.

Start your journey in one of the fastest growing professions today with this beginner-friendly Data Engineering course! You will be introduced to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in the ecosystem. You will begin this course by understanding what is data engineering as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in this exciting field. Next you will learn about the data engineering ecosystem, the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks. You will become familiar with the components of a data platform and gain an understanding of several different types of data repositories such as Relational (RDBMS) and NoSQL databases, Data Warehouses, Data Marts, Data Lakes and Data Lakehouses. You’ll then learn about Big Data processing tools like Apache Hadoop and Spark. You will also become familiar with ETL, ELT, Data Pipelines and Data Integration. This course provides you with an understanding of a typical Data Engineering lifecycle which includes architecting data platforms, designing data stores, and gathering, importing, wrangling, querying, and analyzing data. You will also learn about security, governance, and compliance. You will learn about career opportunities in the field of Data Engineering and the different paths that you can take for getting skilled as a Data Engineer. You will hear from several experienced Data Engineers, sharing their insights and advice. By the end of this course, you will also have completed several hands-on labs and worked with a relational database, loaded data into the database, and performed some basic querying operations.

Are you ready to dive into the world of data engineering? In this beginner level course, you will gain a solid understanding of how data is stored, processed, and accessed in relational databases (RDBMSes). You will work with different types of databases that are appropriate for various data processing requirements. You will begin this course by being introduced to relational database concepts, as well as several industry standard relational databases, including IBM DB2, MySQL, and PostgreSQL. Next, you’ll utilize RDBMS tools used by professionals such as phpMyAdmin and pgAdmin for creating and maintaining relational databases. You will also use the command line and SQL statements to create and manage tables. This course incorporates hands-on, practical exercises to help you demonstrate your learning. You will work with real databases and explore real-world datasets. You will create database instances and populate them with tables and data. At the end of this course, you will complete a final assignment where you will apply your accumulated knowledge from this course and demonstrate that you have the skills to: design a database for a specific analytics requirement, normalize tables, create tables and views in the database, load and access data. No prior knowledge of databases or programming is required. Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.