Data Science – End 2 End Beginners Course Part 1

This is a Beginner’s course that covers basic Machine Learning and Data Analytics concepts

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

Having seen available data science courses and books, we feel there is a lack of an End 2 End approach

Quite often you learn the different algorithms but don’t get a holistic view, especially around the process and deployment

Also, either too much or limited mathematical details are provided for different algorithms

The course will cover all the basics in programming, maths, statistics and probability required for building machine learning models

Throughout the course detailed lectures covering the maths and logic of the algorithms, python code examples and online resources are provided to support the learning process

Students will learn how to build and deploy machine learning models using tools and libraries like anaconda, spyder, python, pandas, numpy, scikit–learn, xgboost, matplotlib, seaborn, joblib, flask, AWS Cloud S3, Elastic Beanstalk and Sagemaker