The Objective of this course is to teach students how to do an End-2-End data science project
- From Problem definition, data sourcing, wrangling and modelling
- To analyzing, visualizing and deploying & maintaining the models
- It will cover the main principles/tools that are required for data science
This course is for anyone interested in learning data science – analyst, programmer, non-technical professional, student, etc
The End 2 End Data science course will be divided into 4 parts
- Part 1 is a Beginner’s course that covers basic Machine Learning and Data Analytics
- Part 2 will cover Intermediate and Advanced machine learning techniques – Deep Learning and NLP (Natural Language Processing)
- Part 3 will cover Advanced machine learning techniques - Reinforcement Learning and Computer Vision
- Part 4 will cover Data Engineering – Databases and Big Data tools (Hadoop, Spark)
Parts 2,3,4 of the course will be published later
Throughout the course detailed lectures covering the maths/logic of the algorithms, python code examples and online resources are provided to support the learning process
- More details are available on our website - https://datawisdomx.com/
- Course material including python code and data is available at - https://github.com/datawisdomx/DataScienceCourse
- It has python code examples for each algorithm and a full model building and deployment example
Repository for the Data Science course. Each part of the course will have a separate folder with sub folders containing lectures, code and other files.