Become an SQL Data Engineer/Data Analyst
Mastering Data: Unleashing the Power of SQL for Future Data Analysts and Engineers
What you will learn
Understand the roles and responsibilities of data analysts and data engineers.
Identify and use different types of SQL databases including MS SQL, MySQL, PostgreSQL, and Oracle SQL.
Write basic to complex SQL queries using various SQL syntax, operators, and functions.
Understand and implement data cleaning, , backup, and restoration in SQL.
Perform data analysis tasks using SQL, such as computing descriptive statistics and utilizing various functions and techniques for manipulating data.
Understand the principles of data engineering using SQL, including designing databases, handling ETL processes, and managing large datasets.
Implement SQL-like queries in NoSQL databases.
Understand the basics of Big Data technologies and how SQL interfaces with these tools.
Use SQL in conjunction with popular data visualization tools such as Tableau and PowerBI.
Apply SQL best practices and performance optimization strategies in real-world situations.
Gain a strong foundation in SQL, setting the stage for further learning and specialization in the fields of data analysis and data engineering.
Why take this course?
The SQL Data Engineer/Data Analyst course is a comprehensive learning experience that equips students with the skills to leverage SQL's powerful features in real-world data engineering and data analysis scenarios. This course offers an in-depth exploration of SQL, extending from the basics to advanced concepts, and including essential topics like NoSQL, Big Data technologies, and data visualization.
This course begins by introducing students to the roles and responsibilities of data analysts and data engineers, emphasizing the significance of SQL in these professions. It familiarizes students with a variety of SQL databases such as MS SQL, MySQL, PostgreSQL, and Oracle SQL. Gradually, we delve into the fundamentals of SQL, including its syntax, data types, operators, and expressions, and the common SQL statements used to manipulate data in databases.
We then advance to more complex SQL concepts like functions, joins, subqueries, views, indexes, and constraints. Students will have an opportunity to master the art of writing sophisticated SQL queries, and manage databases effectively. This includes learning essential data cleaning techniques and understanding the import and export of data, as well as backup and restoration of databases.
The course also places a special focus on using SQL for data analysis. It covers topics like descriptive statistics, group by, having and order by clauses, window functions, and other advanced SQL techniques used in data analysis. Simultaneously, it sheds light on using SQL for data engineering tasks, such as designing databases, handling ETL processes, and managing large datasets.
Moreover, the curriculum explores SQL-like queries in NoSQL databases, helping students gain a broader understanding of the data ecosystem. It provides an introduction to Big Data technologies like Hadoop and Spark and shows how SQL interfaces with these tools. As visualization is crucial in data analysis, the course outlines how to use SQL with popular data visualization tools like Tableau and PowerBI.
Finally, students learn SQL best practices and performance optimization strategies, ensuring that they not only write functional SQL queries but also write efficient and secure ones.
The culmination of the course is a capstone project, which allows students to apply their acquired knowledge and skills to a real-world data problem, demonstrating their proficiency in using SQL for data engineering and data analysis.
This course is designed for anyone looking to upskill in the field of data analysis and data engineering. With a blend of theoretical lessons, practical exercises, and quizzes, students will gain hands-on experience and in-depth knowledge of SQL, enabling them to succeed in their professional careers.
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