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Data Science
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Data Science – End 2 End Beginners Course Part 1

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

     
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Data Science – End 2 End Beginners Course Part 1

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About 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 More details are available on our website - datawisdomx Course material including python code and data is available in github repository - datawisdomx, DataScienceCourse

What You Will 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 Show moreShow less.