CURRICULUM

AG01.    Install Python and Jupyter environment, a powerful framework for data analysis

AG02.    Working with Python,  iPython

AG03.    Use the Jupyter notebook environment

AG04.    Programming concepts in Python

AG05.    Different Data structures in Python

AG06.    How to work with various data formats within python, including JSON, and                         MS-Excel Worksheets

AG07.    Learn Numpy - a common scientific computation library

AG08.    Getting familiar with Pandas for structural data processing

AG09.    Building informative, useful and beautiful visualizations dashboards using Matplotlib and Seaborn libraries in Python

AG10.    Getting familiar with clustering in data mining

AG11.    Python data science handbook essential tools for working with data

AG12.    Learn the common statistical data analysis techniques in python

AG13.    Predicting Data with time or time series forecasting

AG14.    Basic Statistics for data science course covering standard deviation, covariance,    correlation, autocorrelation

AG15.    Life-cycle of a machine learning project

AG16.    Life of a Data analyst, Data scientist

AG17.    Step-by-step execution for creating a Machine learning model

AG18.    Introduction to machine learning

AG19.    Classification and Overview of Different techniques in Machine learning

AG20.    Use scikit-Learn(sklearn) for machine learning tasks

AG21.    Understanding types of data and their uses

AG22.    Getting familiar with simple linear regression

AG23.    Generalizing prediction of the continuous variable with Multiple Linear Regression

AG24.    Cross-verification method for creating better models

AG25.    Model Evaluation using multiple techniques like RMSE

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