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

Requirements
Students are expected to have some basic knowledge of – Any programming language, Databases, SQL
Or atleast programming concepts – data types, variables, lists, conditional loops, functions, etc
School level maths, statistics, probability and algebra
Note – This course will still cover all the basics in programming, analytics and maths required for building machine learning models

Description
This is a Beginner’s course that covers basic Machine Learning and Data Analytics conceptsThe Objective of this course is to teach students how to do an End-2-End data science projectFrom Problem definition, data sourcing, wrangling and modellingTo analyzing, visualizing and deploying & maintaining the modelsIt will cover the main principles/tools that are required for data scienceThis course is for anyone interested in learning data science – analyst, programmer, non-technical professional, student, etcHaving seen available data science courses and books, we feel there is a lack of an End 2 End approachQuite often you learn the different algorithms but don’t get a holistic view, especially around the process and deploymentAlso, either too much or limited mathematical details are provided for different algorithmsThe course will cover all the basics in programming, maths, statistics and probability required for building machine learning modelsThroughout the course detailed lectures covering the maths and logic of the algorithms, python code examples and online resources are provided to support the learning processStudents 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 SagemakerMore details are available on our website – datawisdomxCourse material including python code and data is available in github repository – datawisdomx, DataScienceCourse

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

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