Generative AI Crash Course for Non-Tech Professionals. Register Now >

CDS Video Series | Sec 02 – Data Engineering and Databases


You will find this course added to your account after the purchase.

ADaSci Members receive 50% discount.


Data Engineering is one of the most important aspects in the field of data science. It is concerned with storage, accessing, processing and managing the data required for any modelling and analysis purpose. With various types of data available from different sources and required for different types of model building, having knowledge of data engineering is a must for any data science professional. Association of Data Scientists (ADaSci) is coming up with this opportunity to let data science aspirants dive deeply into data engineering. In this workshop, the attendees will get a complete understanding of the concept of data engineering and where and how it is used. Different methods of processing the data, feature engineering and munging will be discussed in this workshop with hands-on experiments. Along with this, they will also get familiarity with different types of databases and methods to handle these databases.

Learning Outcomes:

  • Complete understanding of Data Engineering and its importance in Data Science
  • Knowledge of different types of databases with their use and importance
  • In-depth understanding of data wrangling and feature engineering techniques
  • Hands-on exposure to different feature engineering techniques required before modelling purpose


  • Basic understanding of data analysis
  • Basic knowledge of Python programming language
  • Basic understanding of database systems
  • Familiarity with Jupyter/Colab Notebook


  • Jupyter Notebook / Google Colab
  • MySql installed
  • High-Speed Internet Connection


The workshop is pre-recorded video (more than 5 hours) and notebooks.

Our Expert Instructor:

Dr. Vaibhav Kumar is currently working as Senior Director with The Association of Data Scientists (ADaSci). He brings a lot of experience in the field of Deep Learning and Artificial Intelligence. With a diverse background in industry and academics, he has led many Research and Development (R&D) activities in the field of AI. Having a PhD in Deep Learning, he has published many research papers in reputed journals and conferences and guided many scholars in their research works.