Module 1 – SQL

Working with Databases

• SQL Schema
• Inserting Comments in SQL
• Creating Databases
• Creating SQL Tables
• Types Of SQL KEYS
• Primary Key
• Foreign Key
• Composite Key
• Super Key
• Alternate Key
• CREATE Table
• INSERT Data

Introduction To SQL for Data Science

• Types of Databases
• What is a Query?
• What is SQL?
• SQL or SEQUEL?
• SQL Installation
• Extra Help in Installing SQL
• Overview of SQL workbench
• Introduction To SQL Commands

SQL Databases

• Creating Exiting Databases
• Overview Of Existing Databases
• The SELECT Statement in Details
• The ORDER BY Clause
• The WHERE Clause
• Operation with SELECT statement
• Aliasing in SQL
• The DISTINCT Keyword
• WHERE Clause with SQL Comparison operators
• The AND, OR and NOT Operators
• The IN Operator
• The BETWEEN Operator
• The LIKE Operator
• The REGEXP Operator
• IS NULL & IS NOT NULL Operator
• The ORDER BY Clause in Details
• The LIMIT Clause

SQL Joins

• Introduction To SQL JOINS
• Joining Across Multiple Databases
• Joining Table to Itself
• Joining Across Multiple SQL Tables
• LEFT and RIGHT JOIN

Working with Existing SQL Table

• INSERTING Into Existing Table
• INSERTING Multiple Data Into Existing Table
• Creating A Copy of a Table
• Updating Existing Table
• Updating Multiple Records In Existing Table

SQL Functions

• COUNT () Function
• SUM() Function
• AVG() Function
• SQL Combined Functions
• Count Function in Details
• The HAVING() Function
• LENGTH() Function
• CONCAT() Function
• INSERT() Function
• LOCATE() Function
• UCASE() & LCASE() Function

Sub-query

• Basic Subquery
• Subquery with Aggregate Functions
• Subquery with Conditional Logic
• Subquery with Joins

Case Statements & Common Table Expression

• Understanding of Case Statements
• Case Statements with Aggregate Functions
• Understanding of Common Table Expression
• Common Table Expression with Joins

Module 2 – Python

Introduction to Python for Data Science

• Install and Write Your First Python Code
• Introduction to Jupyter Notebook And Jupyter Lab
• Working with Code Vs Markdown
• Introduction To Google Collab

Python Basics

• Keywords And Identifiers
• Python Comments
• Python Docstring
• Python Variables
• Rules and Naming Conventions for Python
• Variables
• Python Output() Function
• Input() Function In Python
• Import() Function In Python

Python Operators

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Special Operators
• Membership Operators

Python Flow Controls

• If Statement
• IfElse Statement
• ELif Statement
• For loop
• While loop
• Break Statement
• Continue Statement

String Objects

• Basic data structure in python
• String object basics
• String Inbuilt methods
• Splitting and Joining strings
• String format functions

List

• List object basics
• List methods
• List as stack and queues
• List comprehensions

Tuples, Set, Dictionaries and Functions

• Tuples, Sets, Dictionary object
• Dictionary Object methods
• Dictionary Comprehensions
• Dictionary View objects
• Function basics, Parameter passing, Iterators
• Generator functions
• Lambda functions
• Map, Reduce, Filter functions

OOP’s Concept

• OOP’s basic concepts
• Creating classes
• Pilers of OOP’s
• Inheritance
• Polymorphism, Encapsulation, and Abstraction
• Decorator
• Class methods and Static Methods
• Special (Magic/Dunder) Methods
• Property decorators – Getters, Setters, and Deleters

Files

• Working with files
• Reading and writing files
• Buffered read and write
• Other file methods
• Logging, debugger
• Modules and statements

Exception Handling

• Difference between exceptions and error
• Exceptions Handling with try-except
• Custom exception handling
• List of general use exceptions
• Best practice exception handling

Flask

• Flask Introduction
• Flask application
• Open-link Flask
• App Routing Flask
• URL Building Flask
• HTTP Methods Flask
• Flask project – Food app, postman, Swagger

NumPy

• Ndarray Object
• Data Types
• Array Attributes
• Array Creation Routines
• Array from Existing
• Data Array From Numerical Ranges
• Indexing & Slicing
• Advanced Indexing
• Broadcasting
• Iterating Over Array
• Array Manipulation
• Binary Operators
• String Functions
• Mathematical Functions
• Arithmetic Operations
• Statistical Functions
• Sort, search and counting functions
• Byte Swapping
• Copies &Views
• Matrix Library
• Linear Algebra

Pandas

• Pandas- Series
• Loc & iLoc
• Data Frame Introduction
• Operations On Pandas Data Frame
• Selection And Indexing On Pandas Data Frame
• Reading A Dataset Into Pandas Data Frame
• Adding A Column To Pandas Data Frame
• How To Drop Columns And Rows In Pandas Data Frame
• How To Reset Index In Pandas Data Frame
• How To Rename A Column In Pandas Data Frame
• Tail(), Column and Index
• How To Check For Missing Values or Null Values(isnull() Vs Isna())
• Pandas Describe Function
• Conditional Selection With Pandas
• How To Deal With Null Values
• How To Sort Values In Pandas
• Pandas Groupby
• Count() & Value_Count()
• Concatenate Function
• Join & Merge(Creating Dataset)
• Pandas-Join
• Visualisation

Visualisation

• Matplotlib
• Seaborn
• Cufflinks
• Plotly
• Bokeh

Python Projects

• Weeding Scripts Image resizing
• Jupyter Notebook merging, reading etc
• Sending emails
• Weather app
• Memes Generator
• Food Log App
• Web Scrapping
• Web crawlers for image data sentiment
• Analysis and product review sentiment analysis

Module 3 – Statistics

• Introduction to basic Statistics terms
• Types of Statistics
• Types of data
• Levels of measurement
• Measures of Central Tendency
• Measure of dispersion
• Random Variables
• Set
• Skewness
• Covariance and Correlation

Probability distribution function

• Probability density/distribution function
• Types of the probability distribution
• Binomial Distribution
• Poisson distribution
• Normal Distribution (Gaussian Distribution)
• Probability Density Function and Mass Function
• Cumulative Density Function
• Examples of Normal Distribution
• Bernoulli Distribution
• Uniform Distribution
• Z Stats
• Central Limit Theorem
• Estimation

Hypothesis Testing

• Hypothesis Testing’s Mechanism
• P-Value
• T-Stats
• Student T distribution
• T-Stats vs Z-Stats: Overview
• When to use a t-tests vs z-tests
• Type 1 & Type 2 Error
• Bayes Statistics (Bayes Theorem)
• Confidence Interval(CI)
• Confidence Intervals and the Margin of Error
• Interpreting confidence levels and confidence intervals
• Chi-Square Test
• Chi-Square Distribution using Python
• Chi-Square for Goodness of Fit Test
• When to use which statistical distribution?
• Analysis Of Variance (ANOVA)
• Assumptions to use ANOVA
• Anova three types
• Partitioning of Variance in the ANOVA
• Calculating using Python
• F-Distribution
• F-Test (variance ratio test)
• Determining the Values of F
• F Distribution using Python

Linear Algebra

• Linear Algebra
• Vector
• Scaler
• Matrix
• Matrix Operations and Manipulations
• Dot product of two vectors
• Transpose of a Matrix
• Linear Independence of Vectors
• Rank of a Matrix
• Identity Matrix or Operator
• Determinant of a Matrix
• Inverse of a Matrix
• Norm of a Vector
• Eigenvalues and Eigenvectors
• Calculus
• Solving stats problem with python
• Stats problem implementation with scipy

Module 4 – GitHub for Data Science

• Introduction to GitHub for Data Science
• Setting up GitHub account for Data Science projects
• Create GitHub Profile for Data Science
• Create GitHub Project Description for Data Science

Module 5 – Machine Learning

• AI vs ML vs DL Vs DS
• Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
• Train, Test, Validation Split performance
• Overfitting, Underfitting
• Bias vs Variance

Feature engineering

• Handling Missing Data
• Handling imbalance, data up-sampling
• Down-Sampling
• Smote
• Data interpolation
• Handling Outliers
• Filter Method
• Wrapper method
• Embedded Methods Feature Scaling
• Standardization Mean Normalization
• Min-Max Scaling Unit Vector Feature Extraction
• PCA (Principle Component Analysis) Data Encoding
• Nominal Encoding One hot encoding
• One hot encoding with multiple categories Mean Encoding
• Ordinal Encoding Label Encoding
• Target guided ordinal encoding Covariance
• Correlation Check
• Pearson correlation coefficient Spearman’s Rank correlation VIF

Feature Selection

• Feature Selection
• Recursive Feature Elimination
• Backward Elimination
• Forward Elimination

Exploratory Data Analysis

• Feature Engineering and Selection
• Analyzing Bike Sharing Trends
• Analyzing Movie Reviews Sentiment
• Customer Segmentation and Effective Cross Selling
• Analyzing Wine Types and Quality
• Analyzing Music Trends and Recommendations
• Forecasting Stock and Commodity Prices

Regression Analysis

• Linear Regression
• Gradient Descent
• Multiple Linear Regression
• Polynomial Regression
• R square and Adjusted R square
• RMSE , MSE, MAE comparision
• Regularized Linear Models
• Ridge Regression
• Lasso Regression
• Elastic Net
• Complete end-to-end project with deployment

Logistics Regression

• Logistics Regression in-depth mathematical intuition
• In-depth geometrical intuition
• Hyper parameter tuning
• Grid search CV
• Randomize search CV
• Data Leakage
• Confusion Matrix
• Precision, recall, F1 score, ROC, AUC
• Best metric selection
• Multiclass classification in LR
• Complete end-to-end project with deployment

Decision tree

• Decision Tree Classifier
• In-depth mathematical intuition
• In-depth geometrical intuition
• Confusion Matrix
• Precision, Recall,F1 score, ROC, AUC
• Best metric selection
• Decision Tree Regressor
• In-depth mathematical intuition
• In-depth geometrical intuition
• Performance metrics
• Complete end-to-end project with deployment

Support Vector Machines

• Linear SVM Classification
• In-depth mathematical intuition
• In-depth geometrical intuition
• Soft Margin Classification
• Nonlinear SVM Classification
• Polynomial Kernel
• Gaussian, RBF Kernel
• Data Leakage
• Confusion Matrix
• presion,recall,F1 score, ROC, AUC
• Best metric selection
• SVM Regression
• In-depth mathematical intuition
• In-depth geometrical intuition
• Complete end-to-end project with deployment

Naïve Bayes

• Bayes Theorem
• Multinomial Naïve Bayes
• Gaussian Naïve Bayes
• Various Type of Bayes theorem and its intuition
• Confusion Matrix
• Precision, Recall, F1 score, ROC, AUC Best metric selection
• Complete end-to-end project with deployment

Ensemble Techniques and its types

• Definition of Ensemble techniques
• Bagging Technique
• Bootstrap Aggregation
• Random Forest (Bagging Technique)
• Random Forest Regressor
• Random Forest Classifier
• Complete end-to-end project with deployment

Boosting

• Boosting Technique ADA Boost Gradient Boost
• XG Boost
• Complete end-to-end project with deployment

Stacking techniques

• Stacking Technique
• Complete end-to-end project with deployment

KNN

• KNN Classifier
• KNN Regressor
• Variants of KNN
• Brute Force KNN
• K-Dimension Tree
• Ball Tree
• Complete end-to-end project with deployment

Dimensionality Reduction

• The curse of Dimensionality
• Dimensionality Reduction Technique
• PCA (Principle Component Analysis)
• Mathematics Behind PCA
• Scree Plots
• Eigen-decomposition Approach

Clustering

• Clustering and their types K-Means Clustering
• K-Means++
• Batch K-Means
• Hierarchical Clustering DBSCAN
• Evaluation of Clustering
• Homogeneity, completeness and V-measure Silhouette Coefficient
• Davies-Bouldin Index Contingency Matrix
• Pair Confusion Matrix
• Extrinsic Measure
• Intrinsic Measure
• Complete end-to-end project with deployment

Anomaly Detection

• Anomaly Detection Types
• Anomaly Detection Applications
• Isolation Forest Anomaly Detection Algorithm
• Density-Based Anomaly Detection (Local Outlier Factor) Algorithm
• Support Vector Machine Anomaly Detection Algorithm
• DBSCAN Algorithm for anomaly Detection
• Complete end-to-end project with deployment

Time Series

• What is a time series? Old Techniques
• ARIMA
• ACF and PACF
• Time-dependent seasonal components
• Auto regressive (AR), moving average (MA) and mixed ARMA-modeller
• The Random Walk Model
• Box-Jenkins methodology
• Forecasts with ARIMA and VAR models
• Dynamic models with time-shifted explanatory variables
• The Koyck transformation
• Partial adjustment and adaptive expectation models
• Granger’s causality tests
• Stationarity, unit roots and cointegration Time series model performance
• Various approach to solve time series problem Complete end-to-end project with deployment
• Prediction of NIFTY stock price and deployment

NLP Basic

• Tokenization
• POS Tags and Chunking Stop Words
• Stemming and Lemmatization
• Named Entity Recognition (NER)
• Word Vectorization (Word Embedding) TFIDF
• Complete end-to-end project with deployment

Model retraining approach

Auto ML

• H2O
• Pycaret
• Auto Sk-Learn
• Auto Time Series
• Auto ViML
• Auto Gluon
• Auto Viz TPOT
• Auto Neuro

Major projects

• Healthcare analytics prediction of medicines based on FITBIT band
• Revenue forecasting for start-ups
• Prediction of order cancellation at the time of ordering inventories
• Anomaly detection in inventory packaged material
• Fault detection in wafflers based on sensor data
• Demand forecasting for FMCG product
• Threat identification in security system
• Defect detection in vehicle engine
• Food price forecasting with Zomato dataset
• Fault detection in wafflers based on sensor data
• Cement Strength reg
• Credit Card Fraud
• Forest Cover Classification
• Fraud Detection
• Income Prediction
• Mushroom classifier
• Phishing Classifier
• Thyroid Detection
• Visibility climate

Module 6 – Deep Learning

Neural Network

• A Simple Perception
• Detail mathematical Explanation
• Neural Network overview and its use case
• Various Neural Network architect overview
• Use case of Neural Network in NLP and computer vision
• Activation function -All name Multilayer Network
• Loss Functions
• Optimizers
• Forward and Backward Propagation
• weight initialization technique vanishing gradient problem
• Exploding gradient problem Visualization of NN

TensorFlow Installation

• Environment setup for Deep learning Collab pro setup
• TensorFlow Installation 20
• TensorFlow Installation 16 with virtual environment
• TensorFlow 20 function
• TensorFlow 20 neural network creation
• TensorFlow 16 functions
• TensorFlow 16 neural network and its functions
• Keras Introduction
• Keras in-depth with neural network creation
• Mini project in TensorFlow

Pytorch

• Pytorch installation
• Pytorch functional overview
• Pytorch neural network creation

MXNet

• Mxnet installation
• Mxnet in depth Function overview
• Mxnet model creation and Training

KERAS Tuner

• Keras Tuner installation and overview
• Finding best parameter from Keras tuner
• Keras tuner application across various neural network

CNN overview

• CNN definition
• various CNN based architecture explanation end to end CNN network CNN Explainer
• Training CNN

Module 7 – Computer Vision Projects

• Traffic Surveillance System
• Object Identification
• Object Tracking
• Object Classification
• TensorFlow Object Detection
• Image to Text Processing
• Speech to Speech analysis
• Vision Based Attendance System

Module 8 – Natural language processing

• Computational Linguistic
• History of NLP
• Why NLP
• Use of NLP
• Natural Language Toolkit (NLTK)

Text processing

• Importing Text
• Web Scrapping
• Text Processing
• Understanding Regex
• Text Normalisation
• Word Count
• Frequency Distribution
• Text Annotation
• Use of Annotator
• String Tokenization
• Annotator Creation
• Sentence processing
• Lemmatization in text processing
• POS
• Named Entity Recognition
• Dependency Parsing in text
• Sentimental Analysis

Spacy

• Spacy Overview
• Spacy function
• Spacy function implementation in text processing
• POS tagging, challenges and accuracy
• Entities and named entry Recognition, Interpolation, Language models
• Text blob

RNN

• Recurrent Neural Networks
• Long Short Term Memory (LSTM) Bi LSTM
• stacked LSTM
• GRU implementation
• Building a Story writer using character level RNN

Word Embedding

• Word embeddings
• Co-occurrence vectors
• Word2vec
• Doc2vec

Attention Based model

• Seq2Seq
• Encoders and Decoders Attention Mechanism
• Attention Neural Networks Self Attention

NLP project

• Machine Translation
• Abstractive Text Summarization
• Keyword Spotting
• Language Modelling
• Document Summarization
• Text classification
• Detecting fake text
• Fake News Detector
• Python Plagiarism Checker type a message
• Text to speech
• Keyword Spotting
• Document Ranking
• Text Search (with Synonyms) Language Modelling
• Spam Detector
• Image Captioning

Module 9 – Data Analytics

Tableau (basics to advanced) projects

• Human Recourse
• Supply Cain
• Sale return
• E-commerce
• Project management
• Sales dashboard

Powerbi (basics to advanced) projects

• Cost insights
• Management insights
• Retail insights

Model 10 – To make you Job Ready

• Get guidance to master the concepts by participating in FREE hands-on projects
• Virtual Internships
• Resume Building
• Mock Interviews to be job ready