INTRODUCTION

• What is Python?
• Why does Data Science require Python?
• Installation of Anaconda
• Understanding Jupyter Notebook
• Basic commands in Jupyter Notebook
• Understanding Python Syntax



Data Types and Data Structures

• Variables and Strings
• Lists, Sets, Tuples, and Dictionaries



Control Flow and Conditional Statements

• Conditional Operators, Arithmetic Operators, and Logical Operators
• If, Elif and Else Statements
• While Loops
• For Loops
• Nested Loops and List and Dictionary Comprehensions



Functions

• What is function and types of functions
• Code optimization and argument functions
• Scope
• Lambda Functions
• Map, Filter, and Reduce



File Handling

• Create, Read, Write files and Operations in File Handling
• Errors and Exception Handling



Class and Objects

• Create a class
• Create an object
• The __init__()
• Modifying Objects
• Object Methods
• Self
• Modify the Object Properties
• Delete Object
• Pass Statements

Numpy – NUMERICAL PYTHON

2. Data Manipulation with Pandas

DATA VISUALIZATION

Data Visualization using Matplotlib and Pandas

Case Study on Exploratory Data Analysis (EDA) and Visualizations

UNSTRUCTURED DATA PROCESSING

Regular Expressions

PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS

Data Types and Data Structures

Data Gathering Techniques

Descriptive Statistics

Probability Distribution

Inferential Statistics

Introduction to Databases

• DML, DDL, DCL, and Data Types
• Common SQL commands using SELECT, FROM, and WHERE
• Logical Operators in SQL

• INNER and OUTER joins to combine data from multiple tables
• RIGHT, LEFT joins to combine data from multiple tables

• Advanced filtering using IN, OR, and NOT
• Sorting with GROUP BY and ORDER BY

• Common Aggregations including COUNT, SUM, MIN, and MAX
• CASE and DATE functions as well as work with NULL values

• Subqueries to run multiple queries together
• Temp tables to access a table with more than one query

• Perform Data Cleaning using SQL

INTRODUCTION

REGRESSION TECHNIQUES

Linear Regression

Multiple Linear Regression

Evaluating the Metrics of Regression Techniques

Polynomial Regression

Regularization Techniques

CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Regression Techniques.

CLASSIFICATION TECHNIQUES

Logistic regression

Naive Bayes

• Principle of Naive Bayes Classifier
• Bayes Theorem
• Terminology in Naive Bayes
• Posterior probability
• Prior probability of class
• Likelihood
• Types of Naive Bayes Classifier
• Multinomial Naive Bayes
• Bernoulli Naive Bayes and Gaussian Naive Bayes

TREE BASED MODULES

Decision Trees

• Decision Trees (Rule-Based Learning):
• Basic Terminology in Decision Tree
• Root Node and Terminal Node
• Regression Trees and Classification Trees
• Trees Versus Linear Models
• Advantages and Disadvantages of Trees
• Gini Index
• Overfitting and Pruning
• Stopping Criteria
• Accuracy Estimation using Decision Trees

Case Study: A Case Study on Decision Tree using Python

• Resampling Methods:
• Cross-Validation
• The Validation Set Approach Leave-One-Out Cross-Validation
• K-Fold Cross-Validation
• Bias-Variance Trade-O for K-Fold Cross-Validation

Ensemble Methods in Tree-Based Models

• What is Ensemble Learning?
• What is Bootstrap Aggregation Classifiers and how does it work?

Random Forest

• What is it and how does it work?
• Variable selection using Random Forest

Boosting: AdaBoost, Gradient Boosting

• What is it and how does it work?
• Hyper parameter and Pro’s and Con’s

Case Study: Ensemble Methods – Random Forest Techniques using Python

DISTANCE BASED MODULES

K Nearest Neighbors

• K-Nearest Neighbor Algorithm
• Eager Vs Lazy learners
• How does the KNN algorithm work?
• How do you decide the number of neighbors in KNN?
• Curse of Dimensionality
• Pros and Cons of KNN
• How to improve KNN performance

Case Study: A Case Study on KNN using Python

Support Vector Machines

• The Maximal Margin Classifier
• HyperPlane
• Support Vector Classifiers and Support Vector Machines
• Hard and Soft Margin Classification
• Classification with Non-linear Decision Boundaries
• Kernel Trick
• Polynomial and Radial
• Tuning Hyper parameters for SVM
• Gamma, Cost, and Epsilon
• SVMs with More than Two Classes

Case Study: A Case Study on SVM using Python

CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Classification Techniques.

Principal Components Analysis

Case Study: A Case Study on PCA using Python

K-Means Clustering

Hierarchical Clustering

Case Study: A Case Study on clusterings using Python

Association Rules

Apriori

Case Study: A Case Study on association rules using Python

CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and Unsupervised algorithms.

RECOMMENDATION SYSTEMS

Introduction to Neural Networks

TensorFlow 2.0

Artificial Neural Network with Tensorflow

Working with images & CNN Building Blocks

• Working with Images_Introduction
• Working with Images – Reshaping understanding, size of image understanding pixels Digitization,
• Sampling, and Quantization
• Working with images – Filtering
• Hands-on Python Demo: Working with images
• Introduction to Convolutions
• 2D convolutions for Images
• Convolution – Backward
• Transposed Convolution and Fully Connected Layer as a Convolution
• Pooling: Max Pooling and Other pooling options

CNN Architectures and Transfer Learning

• CNN Architectures and LeNet Case Study
• Case Study: AlexNet
• Case Study: ZFNet and VGGNet
• Case Study: GoogleNet
• Case Study: ResNet
• GPU vs CPU
• Transfer Learning Principles and Practice
• Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
• Transfer learning Visualization (run package, occlusion experiment)
• Hands-on demo T-SNE

Object Detection

• CNN’s at Work – Object Detection with region proposals
• CNN’s at Work – Object Detection with Yolo and SSD
• Hands-on demo- Bounding box regressor
• #Need to do a semantic segmentation project

CNN’s at Work – Semantic Segmentation

• CNNs at Work – Semantic Segmentation
• Semantic Segmentation process
• U-Net Architecture for Semantic Segmentation
• Hands-on demo – Semantic Segmentation using U-Net
• Other variants of Convolutions
• Inception and MobileNet models

CNN’s at work- Siamese Network for Metric Learning

• Metric Learning
• Siamese Network as metric learning
• How to train a Neural Network in Siamese way
• Hands-on demo – Siamese Network

Introduction to Statistical NLP Techniques

• Introduction to NLP
• Preprocessing, NLP Tokenization, stop words, normalization, Stemming and lemmatization
• Preprocessing in NLP Bag of words, TF-IDF as features
• Language model probabilistic models, n-gram model, and channel model
• Hands-on NLTK

Word Embedding

• Word2vec
• Golve
• POS Tagger
• Named Entity Recognition(NER)
• POS with NLTK
• TF-IDF with NLTK

Sequential Models

• Introduction to sequential models
• Introduction to RNN
• Introduction to LSTM
• LSTM forward pass
• LSTM backdrop through time
• Hands-on Keras LSTM

Applications

• Sentiment Analysis
• Sentence generation
• Machine translation
• Advanced LSTM structures
• Keras – machine translation
• ChatBot

Tableau for Data Science

• Install Tableau for Desktop 10
• Tableau to Analyze Data
• Connect Tableau to a variety of dataset
• Analyze, Blend, Join and Calculate Data
• Tableau to Visualize Data
• Visualize Data In the form of Various Charts, Plots, and Maps
• Data Hierarchies
• Work with Data Blending in Tableau
• Work with Parameters
• Create Calculated Fields
• Adding Filters and Quick Filters
• Create Interactive Dashboards
• Adding Actions to Dashboards