Data Science – End 2 End Beginners Course Part 1

Machine Learning & Data Analytics- Python, Pandas, Maths, Statistics, Probability, Regression, Classification,Clustering

What you'll learn
Part 1 is a Beginner’s course that covers Machine Learning and Data Analytics
Objective is to teach students how to do an End-2-End data science project
From problem definition, data sourcing, wrangling, modelling, analyzing and visualizing to deploying and maintaining
Part 1 will cover all the basics required for building machine learning models - programming, analytics, maths, process, algorithms and deployment
It will provide full maths and logic details for all algorithms
Programming (python) and Data analytics (pandas)
Maths, Statistics and Probability basics required for understanding the different algorithms
Data Science Process – Problem, Wrangling, Algorithm Selection, Model Building , Visualization, Deployment
Data Wrangling
Build Machine Learning models - Supervised & Unsupervised algorithms using Regression, Classification & Clustering
How to Visualize and Evaluate models
Model Persistence and Deployment using joblib and flask, Deploying on AWS Cloud using S3 and Elastic Beanstalk, Using AWS Sagemaker
End 2 End Project – Building a RoboAdvisor - multi-asset portfolio using global assets and macroeconomic data
Detailed python code and data is provided to explain all concepts and algorithms
Use popular libraries like scikit-learn, xgboost, numpy, matplotlib, seaborn, joblib, flask, etc

Description
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

More details are available on our website - datawisdomx

Course material including python code and data is available in github repository - datawisdomx, DataScienceCourse

Who this course is for:
This course is for anyone interested in learning data science
From beginners to intermediate level users
Analyst, programmer, non-technical professional, student, etc
Data Analysts, Machine Learning engineers, Data Engineers, Business Analysts who want to become Data Scientists