Data Science: Create Real World Projects

Course Curriculum

Chapter 1: Welcome to the Course: Start with Introduction

Lecture 1: Introduction

Chapter 2: Data Science Environment Setup

Lecture 1: Install anaconda on your machine

Lecture 2: Set up environment and Download Machine Learning Libraries

Lecture 3: Introduction to Jupyter Notebook

Chapter 3: Data Science Lifecycle/Methodology

Lecture 1: Data Science Methodologies

Lecture 2: CRISP-DM model

Lecture 3: Phases of CRISP-DM

Lecture 4: Phases of CRISP-DM part 2

Lecture 5: Phases of CRISP-DM part 3

Chapter 4: Introduction to Data Cleanup/Munging

Lecture 1: Why to clean the data?

Lecture 2: Data Quality

Lecture 3: Check if data is valid or not?

Lecture 4: Check if data is accurate or not?

Lecture 5: Completeness of the data

Lecture 6: Consistency of the data

Lecture 7: Uniformity of the data

Lecture 8: How to ensure data quality

Lecture 9: Inspect the data

Lecture 10: Cleaning the data

Lecture 11: Goal of data munging

Lecture 12: Understand your data

Lecture 13: Introduction to Outliers

Lecture 14: Finalize Data Munging

Chapter 5: Cleaning data (Coding session) : Feature Engineering

Lecture 1: Handle data type mismatch

Lecture 2: Remove Duplicate data

Lecture 3: Handling missing data

Lecture 4: Feature Importance

Lecture 5: Plot feature importance plot

Chapter 6: Introduction to Feature Transformation

Lecture 1: Introduction to Feature Importance

Lecture 2: Data Normalization

Lecture 3: Data Standardization

Lecture 4: Normalization in practice

Lecture 5: Standardization in practice

Lecture 6: Introduction to One Hot Encoding

Lecture 7: One Hot Encoding in practice

Chapter 7: Introduction to Machine Learning

Lecture 1: Types of data in Machine Learning

Lecture 2: Structured format for datasets

Lecture 3: Introduction to pandas library

Lecture 4: Train Test split Concept

Chapter 8: Introduction to Decision Tree

Lecture 1: Decision Tree part 1

Lecture 2: Decision Tree part 2

Lecture 3: Code: Decision Tree classifier

Lecture 4: Decision Tree: GINI index

Chapter 9: Introduction to Linear Regression

Lecture 1: Introduction to Linear Regression

Lecture 2: Learn about OLS [Ordinary Least Squares] algorithm

Lecture 3: Introduction to working of Linear Regression

Lecture 4: Lecture: Introduction to MSE, MAE, RMSE

Lecture 5: Introduction to R squared

Lecture 6: Implement Simple Linear Regression

Chapter 10: Introduction to Logistic Regression

Lecture 1: Learn about Logistic Regression

Lecture 2: Learn about Gradient Descent

Lecture 3: Implement Logistic Regression part 1

Lecture 4: Implement Logistic Regression part 2

Chapter 11: Project 1: Hotel Booking Prediction System (Learn Classification problem)

Lecture 1: Introduction to data and data dictionary

Lecture 2: Setup project and import libraries

Lecture 3: Import data to the project

Lecture 4: Clean NA values

Lecture 5: Clean your data

Lecture 6: Analysis 1: Where do the guest come from?

Lecture 7: Analysis 2: How much do guests pay for room per night?

Lecture 8: Analysis 3: How does the price vary?

Lecture 9: Sorting

Lecture 10: Analysis 4: Which months are busy months?

Lecture 11: Analysis 5: How long do people stay at the hotels?

Lecture 12: Feature selection using coorelation

Lecture 13: Refine Numerical attributes

Lecture 14: Refine Categorical attributes

Lecture 15: Augment the data

Lecture 16: Mean Encoding for Categorical attributes

Lecture 17: Preparing our data

Lecture 18: Feature Importance

Lecture 19: Splitting data and Building models

Chapter 12: Project 2: Natural Language Processing

Lecture 1: Loading the data to the project

Lecture 2: Introduction to Corpus and Term Document Matrix

Lecture 3: Storing data into the data frame

Lecture 4: Cleaning the data

Lecture 5: Creating Document Term Matrix

Lecture 6: Analyzing most commonly spoken words

Lecture 7: Creating wordcloud

Lecture 8: Profanity

Lecture 9: Sentimental Analysis

Lecture 10: Sentiment Label

Lecture 11: Plotting Polarity and Subjectivity

Lecture 12: Topic Modeling

Lecture 13: Topic Modeling: Part Of Speech Tagging

Lecture 14: Text Generation

Lecture 15: Text Generation Part 2

Chapter 13: Project 3: Artificial Intelligence: Neural Network