Data Science Course in Bangalore

• Basic Terminology: Events, Sample Space, Experiment, types of events 
• Probability 
• Conditional Probability 
• Multiplication theorem 
• Bayes' theorem 
• Partition of sample space, Theorem of total probability 
• Random variables and probability distributions 
• Continuous and Discrete distributions 
• inomial Distribution 
• Normal and Standard Normal Distributions 

• Measures of central tendency 
• Measures of dispersion 
• Visualizations 
• Outlier Detection 
• Covariance and correlation 

• Central Limit Theorem 
• Confidence interval and confidence level 
• Hypothesis testing: Null and Alternate hypothesis 
• P value, Significance level 
• Statistical tests 
• NumPy for mathematical computing 
• Data manipulation using Pandas 
• Data visualization with Matplotlib and Seaborn 
• Data Pre-Processing 
• Web Scraping Using Beautifulsoup 

• Linear Regression 
• Multiple and Polynomial regression 
• Regularization, Ridge and Lasso regression 

• Logistic Regression 
• K Nearest Neighbours 
• Decision Trees 
• Naïve Bayes 
• Support Vector Machine 
• Ensemble Techniques: Random Forests, Gradient boosting machines 

• Clustering 
• Principal Component Analysis 

• Introduction to Deep Learning and Neural Networks 
• Introduction to Linear Algebra 
• Artificial Neural Networks 
• Neurons, Layers and Activation Functions 
• Convolutional neural networks - Computer Vision 
• Convolution Operation, Pooling, Padding and Strides 
• Transfer Learning (LeNet 5, Alex Net, VGG 19 & 16, ResNet, Inception V3) 
• Recurrent neural networks - Natural Language processing 
• Computer Vision & Natural Language processing using CNNs & RNNs 

• Installation 
• Introduction to PowerBI 
• Power Query 
• Dax 
• Functions 
• Visualizations 
• Reports 
• Dashboards 

• Installation 
• Introduction to Tableau 
• Tableau UI 
• Connecting to Excel files 
• Tableau Charts and graphs 
• Functions 
• Reports 
• Dashboards 

1. COVID-19 detection - Healthcare 
2. Real-time face detection - Cyber Security 
3. Sentiment analysis - Ecommerce

Introduction to Excel

Introduction to Excel Interface

• Excel ribbon, workbook, worksheet, rows, columns, cells
• Basic navigation and shortcuts
• Entering Data & Formatting Cells

Entering and editing data (text, numbers, dates)

Formatting (font, alignment, borders, number formats)

Basic Formulas

• Basic arithmetic operations (+, -, , /)
• Using AutoSum, AutoFill
• Introduction to cell references (relative, absolute)

Working with Data    

Sorting & Filtering Data

• Sort data (ascending/descending)
• Filtering using AutoFilter

Data Validation & Drop-down Lists

• Creating data validation rules
• Setting up drop-down lists

Find and Replace, Go To Special

• Using Find & Replace for large datasets
• Go To Special for selecting specific cell types (blanks, errors)

Text Functions

CONCATENATE, LEFT, RIGHT, MID, TRIM, UPPER, LOWER

Formulas and Functions    

Mathematical Functions

SUM, AVERAGE, MIN, MAX, COUNT

Logical Functions

• IF, AND, OR, NOT functions
• Nested IF statements
• Lookup Functions
• VLOOKUP, HLOOKUP
• XLOOKUP (if available)
• INDEX and MATCH

Charts and Data Visualization

Creating Basic Charts

Column, Line, Pie charts

Chart elements (title, axis labels, legends)

Advanced Charting

Combo charts, secondary axis

Scatter plots, histograms

Formatting Charts

Customizing chart colors, styles, layouts

Adding trendlines, error bars

Sparklines and Conditional Formatting

Mini-charts within cells

Applying conditional formatting (color scales, icon sets)

Data Analysis with PivotTables & PivotCharts

Introduction to PivotTables

Creating a basic PivotTable

Dragging fields into rows, columns, values, and filters

Customizing PivotTables

Grouping data, formatting values

Sorting and filtering within PivotTables

PivotCharts

Creating charts based on PivotTables

Customizing PivotCharts

Slicers and Timelines

Using slicers for dynamic filtering

Creating timelines for date-based data

Advanced Data Functions

What-If Analysis Tools

Goal Seek

Data Tables

Scenario Manager

Advanced Lookup Techniques

Dynamic arrays (FILTER, UNIQUE)

Using INDIRECT and OFFSET functions for dynamic referencing

Working with Large Datasets & Collaboration

Working with Multiple Sheets and Workbooks

Linking cells across sheets and workbooks

Consolidating data from multiple worksheets

Protecting Sheets & Workbooks

Locking cells, password protection

Protecting workbooks from unwanted changes

Collaboration Tools

Sharing workbooks, tracking changes, comments

Excel online collaboration (if applicable)

Data Import/Export

Importing data from CSV, TXT, and other sources

Exporting data to CSV, PDF

An Introduction to PYTHON

Introductory Remark about Python

• A Brief History of Python
• How Python is differ from other languages
• Python Version

Getting Help

• How To execute Python program
• Writing your first program google colab, vs code and anaconda

Python Basics

• Python keywords,variables and Identifiers
• Python statements
• Comments in python
• Command line arguments
• Getting user input
• Exercise

Decision making & Loops 

• Introduction
• Control flow and syntax
• The if statement
• Python operators
• The while Loop
• Break and continue
• The for Loop
• Pass statement

Data Stuctures

• Introduction
• List
• Tuples
• String
• Dictionary
• Set
• Exercise

Functions in Python

• Introduction
• Calling a function
• Function arguments
• Built in function
• Scope of variables
• Decorators
• Passing function to a function
• Lambda
• Closures
• Exercise

Modules and Packages

• Modules
• Importing Modules
• Standard Modules- sys
• Standard Modules- OS
• The dir Function
• Packages
• Exercise

Exception Handling

• Errors
• Run Time Errors
• Handling IO Exception
• Try….except statement
• Raise
• Assert

Files and Directories

• Introduction
• Writing Data to a file
• Reading data from a file
• Additional file methods
• Working with files
• Working with Directories
• The pickle Module

Classes & Objects

• Introduction classes and objects
• Creating classes
• Instance methods
• Special class method
• Inheritance
• Method overriding
• Data hiding
• Exercise

Web Scrapping using Beautiful Soup

• Introduction to BeautifulSoup
• Understanding how BeautifulSoup works.
• Initializing a BeautifulSoup object with HTML content.
• Navigating the HTML Tree Structure
• Accessing specific elements by tag (find, find_all).
• Working with attributes (class, id).
• Extracting Content
• Extracting text from elements.
• Extracting links, images, and other attributes.
• Dealing with Nested HTML Structures
• Extracting content from nested tags.
• Navigating through parents, children, and sibling elements.

Request Module in Python

Understanding HTTP Protocol

What is HTTP?

Key components of an HTTP request: URL, headers, body, method (GET, POST, etc.).

HTTP status codes (200 OK, 404 Not Found, etc.).

Overview of Python's requests Module

Introduction to the requests library.

Comparison with other modules (urllib vs. requests).

GET Request

Syntax: requests.get().

Passing query parameters (params).

Inspecting response objects (response.status_code, response.content, response.text).

POST Request

Syntax: requests.post().

Sending form data using data or json parameters.

Other HTTP Methods (Overview)

PUT, DELETE, HEAD, OPTIONS (with examples).
    
Inspecting Response Data

Accessing response body (response.text vs response.content vs response.json()).

Checking status codes and their meaning (response.status_code)

Extracting response headers (response.headers).

JSON Responses

Working with JSON data (response.json()).

Handling non-JSON responses.

Sending Query Parameters in a GET Request

Using the params parameter to include query strings.

Example: requests.get(url, params={'key1': 'value1', 'key2': 'value2'}).

Sending Data in a POST Request

Sending form data (data vs. json).

• Overview of Data Analysis
• Applications of Data Analysis in Real-world Scenarios
• Data Types and Data Structures in Data Analysis

• Introduction to NumPy
• Working with Arrays
• Array Operations and Broadcasting
• Mathematical and Statistical Functions in NumPy

• Introduction to Pandas Library for Data Manipulation
• Loading and Inspecting Datasets
• Filtering, Sorting, and Selecting Data , indexing , renaming
• Handling Missing Data
• Grouping and Aggregating Data

• Handling Duplicates and Invalid Data
• Data Transformation Techniques
• Data Imputation Techniques
• Data Consistency and Integrity Checks

• Introduction to EDA
• Descriptive Statistics
• Detecting and Treating Outliers
• Data Distribution Visualization
• Correlation and Covariance Analysis

• Introduction to Hypothesis Testing (30 min)
• Types of Hypotheses (Null and Alternative) (45 min)
• t-Tests and ANOVA (1 Hour)
• Chi-Square Test (45 min)
• Interpreting p-values (45 min)

• Introduction to Matplotlib for Plotting
• Creating Basic Line, Bar, and Scatter Plots
• Customizing Plot Elements
• Multi-plot Figures and Subplots

• Introduction to Seaborn Library
• Creating Heatmaps, Boxplots, and Violin Plots
• Pairplots and Jointplots for Multivariate Analysis
• Customizing Seaborn Visualizations

• Introduction to Time Series Data
• Working with DateTime in Pandas
• Plotting Time Series Data
• Rolling, Shifting, and Resampling Time Series Data

• Creating Dashboards with Matplotlib and Seaborn
• Storytelling with Data
• Case Study: Building a Dashboard for Business Insights

• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• Applications of ML in the Real World

• Introduction to Regression
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Regularization: Ridge, Lasso

• Introduction to Classification
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)
• Naive Bayes Classifier

• Confusion Matrix, Precision, Recall, F1-Score
• ROC Curve and AUC
• Cross-Validation
• Grid Search and Random Search

• Introduction to Decision Trees
• Decision Tree Algorithm: Classification and Regression
• Random Forests and Bagging

• Introduction to Clustering
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN Algorithm

• Introduction to Dimensionality Reduction
• Principal Component Analysis (PCA)

• Introduction to Ensemble Methods
• Bagging: Random Forests
• Boosting: AdaBoost, Gradient Boosting, XGBoost

• Introduction to Time Series
• Time Series Forecasting with ARIMA
• Exponential Smoothing and Moving Averages

• Introduction to Neural Networks
• Perceptron, Activation Functions

• Case Study 1: Regression
• Case Study 2: Classification
• Case Study 3: Clustering
• Final Project: Comprehensive ML Model

• What is Deep Learning?
• Neural Networks Overview
• Key Concepts: Neurons, Weights, Activation Functions (45 min)

• Architecture of Feedforward Neural Networks
• Forward and Backpropagation
• Gradient Descent and Optimization

• Introduction to TensorFlow and Keras
• Building a Basic Neural Network with Keras
• Training and Evaluating the Model

• Introduction to Convolutional Layers
• Pooling Layers and Fully Connected Layers
• Building a CNN for Image Classification

• Image Rescaling, Normalization, and Augmentation (45 min)
• Data Augmentation Techniques
• Preparing Datasets for Training

• Concept of Transfer Learning
• Using Pre-trained Models (VGG, ResNet, etc.)
• Fine-Tuning Pre-trained Models

• Introduction to Object Detection
• YOLO (You Only Look Once) and SSD (Single Shot Multibox Detector)
• Applications of Object Detection

• Case Study 1: Image Classification with CNN
• Case Study 2: Object Detection using YOLO
• Final Project: Building a Deep Learning Model for Computer Vision

• What is a Database?
• Introduction to SQL
• Creating and Managing Databases

• Describe table ,
• UNIQUE, NULLL , NOT NULL
• Check and Deault
• Primary Key , Foreign Keys
• Composite Keys ,Candidate Key ,Super Key
• Alter command

• SELECT Queries
• Filtering Data with WHERE Clause
• ORDER BY and LIMIT
• DISTINCT and COUNT Functions
• Basic Arithmetic Operations and Aliases

• Inserting Data
• Updating and Deleting Data
• Transaction Control
• Handling NULL Values

• Introduction to Relationships and Joins
• INNER JOIN
• LEFT, RIGHT, and FULL OUTER JOIN
• CROSS JOIN and SELF JOIN
• UNION and UNION ALL

• GROUP BY Clause
• HAVING Clause
• ROLLUP and CUBE
• CASE Statements

• Introduction to Subqueries
• Correlated Subqueries
• EXISTS and NOT EXISTS
• IN and NOT IN Subqueries
• Common Table Expressions (CTEs)

• Window Functions
• LEAD and LAG Functions
• PIVOT and UNPIVOT
• Recursive Queries
• Materialized Views and Indexing