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SQL Server Intermediate-Advanced Course with Business Applications

This course is designed for individuals who want to learn advanced techniques in SQL queries. What you will learn:

  • Deepen your understanding of JOINs and how to use them correctly and advanced with their particularities.
  • How to understand the concept of data grouping and use aggregation functions in combination with the HAVING filters and the OVER() clause.
  • How to apply advanced filters with a combination of logical operators in the WHERE clause and the use of Case When to transform data value by value.
  • Proper handling of date and text data types, the correct way to perform conversions following patterns and a wide range of functions for this purpose.
  • Use of the Merge clause to merge tables, Pivot and Unpivot to convert values into columns and vice versa, and handling of temporary tables.
  • Creation and correct usage of indexes to optimize performance, as well as creating and using various types of functions. How to create and when to use views.
  • How to create a server in the cloud and communicate with it from anywhere in the world, including virtual environments like Google Colab.
  • Creation and correct usage of stored procedures to have structured and shared code, cursors to iterate over tables and other advanced functionalities.
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Preprocessing Data with Python: From Basic to Advanced

This course is aimed at people who want to learn how to use Python for efficient data transformation and preprocessing. What you will learn:

  • Basic concepts of Python for those who are taking their first steps in this language
  • NumPy library: how to understand the foundation behind all multidimensional arrays, indexing, filtering, replacement and other useful functions for creating and manipulating data.
  • Pandas library: one of the most used in the industry with many functionalities, many of them explained and applied with real use cases for data treatment. It contains from the most basic transformations to the most advanced and elegant.
  • Time series handling: this type of datasets have special care and a different treatment from conventional ones, in the course it is explained how to process them adequately for TSA applications.
  • Introduction to Apache Spark: a very important tool when it comes to parallelizing workloads and scaling to larger datasets and infrastructures.
  • Preprocessing with Scikit-Learn: a very powerful library designed to process features in Machine Learning, including explanations on categorical handling, data imputation, grouping, and dimensionality reduction.
  • Feature engineering: how to apply all the steps with the learned bases to manipulate the data and leave it as clean and representative as possible for the type of application.