Data Science with VBA Programming and SQL Database Management

The purpose of the this datascience course is to make it as practical as possible therefore the emphasis in this course is to teach Data Science through a case study with hands-on and practical learning. As such, the case study is based on financial GDP data and other data downloaded from the World bank website for numerous countries from 1960 through to 2020.

This is the course that will help you walk right into a workplace as a data scientist with sufficient knowledge of programming and working with databases.  This course has a very practical angle with the theoretical knowledge necessary to become a data scientist hitting the ground running.

The course starts with learning about the concepts, principles, and terminology of programming languages and how to develop program solving skills. It then covers what datascience is, its components and why and where it is used.

Visual Basic for Applications (VBA) is introduced. The Worldbank data will be imported into excel. You will learn how to program and write your first program using the very handy buttons supplied in the ActiveX tab of the Developer module.

To be a good Data Scientist, you should have some knowledge on databases specifically about SQL (Structured Query Language). Many interview questions of Data Science start with SQL queries. Therefore, SQL is essential for Data Science. Many beginners who are trying to get into Data Science but, worried about coding, start with SQL queries. You will also learn how to populate, retrieve and manipulate data using the relational database tool SQL .

Descriptive statistics are covered as well as correlation and regression. The what and why of visualizations are covered along with the different types. Practical examples are given throughout the course. Included in the course is comparative analyses of GDP (Gross Domestic Product) growth and GDP per capita for the BRICS countries (Brazil, Russia, India, China and South Africa). There are 266 countries that can be used for any comparative analyses with years ranging from 1960 to 2020. The countries are classified into 4 income groups and 7 regions. Top down analyses, regression, histograms and many more are included in this course.