• This course is intended for learners who have a basic knowledge of programming in any language (Java, C, C++, Pascal, Fortran, JavaScript, PHP, python, etc.). You could have learned these basic programming skills on your own or taken a course in programming in high school or college.

• Basic process of data science.

• Python and Jupyter notebooks.

• An applied understanding of how to manipulate and analyze uncurated datasets.

• Basic statistical analysis and machine learning methods.

• How to effectively visualize results.

By the end of the course, you should be able to find a dataset, formulate a research question, use the tools and techniques of this course to explore the answer to that question, and share your findings.

Module 1: Introduction to Data Science

• What is Data Science?

• What is Machine Learning?

• What is Deep Learning?

• What is AI?

• Data Analytics & it’s types

Module 2: Introduction to Python

• What is Python?

• Why Python?

• Installing Python

• Python IDEs

• Jupyter Notebook Overview

Module 3: Python Basics

• Python Basic Data types

• Lists

• Slicing

• IF statements

• Loops

• Dictionaries

• Tuples

• Functions

• Array

• Selection by position & Labels

Module 4: Python Packages

• Pandas

• Numpy

• Sci-kit Learn

• Mat-plot library

Module 5: Importing data

• Reading CSV files

• Saving in Python data

• Loading Python data objects

• Writing data to csv file

Module 6: Manipulating Data

• Selecting rows/observations

• Rounding Number

• Selecting columns/fields

• Merging data

• Data aggregation

• Data munging techniques

Module 7: Statistics Basics

• Central Tendency

• Mean

• Median

• Mode

• Skewness

• Normal Distribution

• Probability Basics

• What does mean by probability?

• Types of Probability

• ODDS Ratio?

• Standard Deviation

• Data deviation & distribution

• Variance

• Bias variance Trade off

• Underfitting

• Overfitting

• Distance metrics

• Euclidean Distance

• Manhattan Distance

• Outlier analysis

• What is an Outlier?

• Inter Quartile Range

• Box & whisker plot

• Upper Whisker

• Lower Whisker

• catter plot

• Cook’s Distance

• Missing Value treatments

• What is a NA?

• Central Imputation

• KNN imputation

• Dummification

• Correlation

• Pearson correlation

• Positive & Negative correlation

• Error Metrics

• Classification

• Confusion Matrix

• Precision

• Recall

• Specificity

• F1 Score

• Regression

• MSE

• RMSE

• MAPE

Module 8: Machine Learning

Module 9: Supervised Learning

• Linear Regression

• Linear Equation

• Slope<

• Intercept

• R square value

• Logistic regression

• ODDS ratio

• Probability of success

• Probability of failure

• ROC curve

• Bias Variance Tradeoff

Module 10: Unsupervised Learning

• K-Means

• K-Means ++

• Hierarchical Clustering

Module 11: Other Machine Learning algorithms

• K – Nearest Neighbour

• Naïve Bayes Classifier

• Decision Tree – CART

• Decision Tree – C50

• Random Forest