Data Analytics Using Google CoLab : A course for Beginners

Course Curriculum

Chapter 1: ***Data Analytics from basic to advance***

Lecture 1: Introduction to Data Analytics using Google CoLab: An overview

Lecture 2: The outcomes of the course and future prospects as data analyst …

Lecture 3: The detail course structure of section 1

Lecture 4: How to link a .csv file with Google Colab

Lecture 5: Basics of Python : Part1

Lecture 6: Basics of Python : Part2(Loops in python)

Lecture 7: Basics of Python : Part3(List)

Lecture 8: Basics of Python : Part4 (Dictionary and tuples)

Lecture 9: Basics of Python : Part5 (Functions in python)

Lecture 10: Concepts of Numpy in Google Colab

Lecture 11: Concepts of Pandas part1

Lecture 12: Concepts of Pandas part2

Lecture 13: Exploratory data analysis using test case : part1

Chapter 2: Data Wrangling : Clean, Transform, Merge and Reshape

Lecture 1: Data Wrangling and getting familiar with Data Frames

Lecture 2: Data Cleaning and model building using Test case part-2

Chapter 3: *** Probability and Probability Distributions***

Lecture 1: Probability Distribution Functions and Probability Mass Functions

Lecture 2: Python implementation of Different type of Distribution

Lecture 3: Cumulative Distribution Functions

Lecture 4: Questions on Probability Distribution

Lecture 5: Concepts of Bayes Theorem part1

Lecture 6: Concept of Bayes Theorem an Naïve Bayes Classifier

Lecture 7: Naïve Bayes theorem Part 3

Lecture 8: Practical Example of Bayes Theorem

Lecture 9: Building a Naïve Bayes Classifier from scratch

Chapter 4: ***Sampling Theory and Sampling Distribution***

Lecture 1: Overview of Section and its outcomes

Lecture 2: Concept of Sampling Theory and Distribution

Lecture 3: Confidence Interval Estimation

Chapter 5: ***Hypothesis Testing in business statistics***

Lecture 1: Concepts of Hypothesis Testing

Chapter 6: ***Linear Regression and Logistic Regression using Python***

Lecture 1: Concept of Linear and Logistic Regression

Lecture 2: Implementation of linear regression from scratch

Lecture 3: An example of Ordinary Least Square

Lecture 4: Multivariate Linear Regression

Lecture 5: Significance of independent vs dependent variable

Lecture 6: Multivariate Linear regression implementation in Python

Lecture 7: Maximum Likelihood estimation in Statistics

Lecture 8: Concepts of Logistic Regression in Business Statistics

Lecture 9: Implementation of Logistic Regression in python

Lecture 10: Loan Prediction algorithm implementation using Logistic Regression

Chapter 7: ***Feature Engineering In Business Statistics***

Lecture 1: Feature engineering on house price prediction

Lecture 2: Feature Engineering on Categorical Variable : House Prediction

Lecture 3: Heart Disease Prediction using Feature Engineering

Chapter 8: ***Singular Value Decomposition***

Lecture 1: Implementation of SVD using Housing Data

Chapter 9: ***Decision Trees using python in Google Colab***

Lecture 1: Overview of Decision Tree and its implementation

Lecture 2: Introduction to Decision tree and ID3

Lecture 3: Introduction to ID3 and its concepts

Lecture 4: Concept of Entropy, Overfitting and Information Gain

Lecture 5: Practical Implementation of ID3and their limitations

Lecture 6: C4.5 Decision and its advancement over ID3 Decision tree

Lecture 7: Building a Decision tree with data using python from scratch

Lecture 8: Practical Implementation of CART Algorithm using Python

Lecture 9: CHAID Algorithm and its importance in Data Analytics

Lecture 10: Implementation of CHAID using Python

Lecture 11: Random Forest Regressor and its comparison with Linear Regression model -part3

Chapter 10: ***Time Series and Exploratory Data Analysis***

Lecture 1: Test Case1 : Tractor Sales Data

Lecture 2: Test Case 2: Air Passengers Forecasting

Lecture 3: EDA of Covid Data of India

Chapter 11: Clustering Algorithms in Data Analytics

Lecture 1: Introduction to the concepts of Unsupervised Learning and clustering

Lecture 2: The Mathematics of K-Means Clustering

Lecture 3: The math of k means part 2

Lecture 4: Practical Implementation of K-Means Clustering

Lecture 5: Concept of Silhouette Coefficient to measure quality of Clusters

Lecture 6: Fuzzy C Means Algorithm : Practical Implementation

Lecture 7: Fuzzy C Means Algorithm-FAANY

Lecture 8: Mean Shift Clustering

Lecture 9: DBSCAN- Density Based Spatial Clustering

Chapter 12: Recommendation System

Lecture 1: Introduction to Recommendation System (RecSys)

Lecture 2: Memory Based Collaborative Filtering

Lecture 3: Matrix Based Collaborative Filtering

Lecture 4: Item Based RecomendationSystem

Lecture 5: Movie Recommendation System using Collaborative Filtering

Lecture 6: Collaborative Filtering Based Movie Recommendation System

Lecture 7: Hybrid Collaborative Filtering

Lecture 8: Implementation of Clustering in Recommendation System

Chapter 13: Churn Prediction Algorithm

Lecture 1: Comparative analysis of Churn Prediction Algorithms

Chapter 14: BUILDING A CAR SELLING PREDICTION MODEL

Lecture 1: Car Prediction Model Using Linear Regression