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

• Introduction to Array
• Creation and Printing of an array
• Basic Operations in Numpy
• Indexing
• Mathematical Functions of Numpy

2. Data Manipulation with Pandas

• Series and DataFrames
• Data Importing and Exporting through Excel, CSV Files
• Data Understanding Operations
• Indexing and slicing and More filtering with Conditional Slicing
• Group by, Pivot table, and Cross Tab
• Concatenating and Merging Joining
• Descriptive Statistics
• Removing Duplicates
• String Manipulation
• Missing Data Handling

DATA VISUALIZATION

Data Visualization using Matplotlib and Pandas

• Introduction to Matplotlib
• Basic Plotting
• Properties of plotting
• About Subplots
• Line plots
• Pie chart and Bar Graph
• Histograms
• Box and Violin Plots
• Scatterplot

Case Study on Exploratory Data Analysis (EDA) and Visualizations

• What is EDA?
• Uni – Variate Analysis
• Bi-Variate Analysis
• More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.

UNSTRUCTURED DATA PROCESSING

Regular Expressions

• Structured Data and Unstructured Data
• Literals and Meta Characters
• How to Regular Expressions using Pandas?
• Inbuilt Methods
• Pattern Matching

PROJECT ON WEB SCRAPING: DATA MINING and EXPLORATORY DATA ANALYSIS

• Data Mining (WEB – SCRAPING)
  This project starts completely from scratch which involves the collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of the Data Science Life Cycle which involves.
  • Data Collection
  • Data Mining
  • Data Preprocessing
  • Data Visualization
    Ex: Text, CSV, TSV, Excel Files, Matrices, Images

Data Types and Data Structures

• Statistics in Data science:
• What is Statistics?
• How is Statistics used in Data Science?
• Population and Sample
• Parameter and Statistic
• Variable and its types

Data Gathering Techniques

• Data types
• Data Collection Techniques
• Sampling Techniques:
• Convenience Sampling, Simple Random Sampling
• Stratified Sampling, Systematic Sampling, and Cluster Sampling

Descriptive Statistics

• What is Univariate and Bi Variate Analysis?
• Measures of Central Tendencies
• Measures of Dispersion
• Skewness and Kurtosis
• Box Plots and Outliers detection
• Covariance and Correlation

Probability Distribution

• Probability and Limitations
• Discrete Probability Distributions
• Bernoulli, Binomial Distribution, Poisson Distribution
• Continuous Probability Distributions
• Normal Distribution, Standard Normal Distribution

Inferential Statistics

• Sampling variability and Central Limit Theorem
• Confidence Intervals
• Hypothesis Testing
• Z-test, T-test
• Chi-Square Test
• F-Test and ANOVA

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Introduction to Databases

• Basics of SQL
  • DML, DDL, DCL, and Data Types
  • Common SQL commands using SELECT, FROM, and WHERE
  • Logical Operators in SQL
• SQL Joins
  • INNER and OUTER joins to combine data from multiple tables
  • RIGHT, LEFT joins to combine data from multiple tables
• Filtering and Sorting
  • Advanced filtering using IN, OR, and NOT
  • Sorting with GROUP BY and ORDER BY
• SQL Aggregations
  • Common Aggregations including COUNT, SUM, MIN, and MAX
  • CASE and DATE functions as well as work with NULL values
• Subqueries and Temp Tables
  • Subqueries to run multiple queries together
  • Temp tables to access a table with more than one query
• SQL Data Cleaning
  • Perform Data Cleaning using SQL

INTRODUCTION TO MACHINE LEARNING

• What Is Machine Learning?

• Supervised Versus Unsupervised Learning

• Regression Versus Classification Problems

• Assessing Model Accuracy

• Why Unsupervised Learning?

• How it Differs from Supervised Learning

• The Challenges of Unsupervised Learning

SUPERVISED LEARNING

Regression Techniques

• Linear Regression

  • Simple Linear Regression:

    • Estimating the Coefficients

    • Assessing the Coefficient Estimates

    • R Squared and Adjusted R Squared

    • MSE and RMSE

  • Multiple Linear Regression:

    • Estimating the Regression Coefficients

    • OLS Assumptions

    • Multicollinearity

    • Feature Selection

    • Gradient Descent

    • Evaluating the Metrics of Regression Techniques

• Error Analysis & Model Validation

  • Homoscedasticity and Heteroscedasticity of error terms

  • Residual Analysis

  • Q-Q Plot

  • Cook’s Distance and Shapiro-Wilk Test

  • Identifying the line of best fit

• Other Considerations in the Regression Model

  • Qualitative Predictors

  • Interaction Terms

  • Non-linear Transformations of the Predictors

• Polynomial Regression

  • Why Polynomial Regression

  • Creating polynomial linear regression

  • Evaluating the metrics

• Regularization Techniques

  • Lasso Regularization

  • Ridge Regularization

  • ElasticNet Regularization

• Case Study: Linear, Multiple Linear Regression, Polynomial Regression using Python

Classification Techniques

• Logistic Regression

  • An Overview of Classification

  • Difference Between Regression and Classification Models

  • Why Not Linear Regression?

  • Logistic Regression:

    • The Logistic Model

    • Estimating the Regression Coefficients and Making Predictions

    • Logit and Sigmoid functions

    • Setting the threshold and understanding decision boundary

    • Logistic Regression for >2 Response Classes

  • Evaluation Metrics for Classification Models:

    • Confusion Matrix

    • Accuracy and Error Rate

    • TPR and FPR

    • Precision and Recall, F1 Score

    • AUC-ROC

    • Kappa Score

• 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

    • Gaussian Naive Bayes

• Tree-Based Models

  • 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: Decision Tree using Python

• Resampling Methods

  • Cross-Validation

  • The Validation Set Approach

  • Leave-One-Out Cross-Validation

  • K-Fold Cross-Validation

  • Bias-Variance Trade-Off for K-Fold Cross-Validation

• Ensemble Methods in Tree-Based Models

  • What is Ensemble Learning?

  • What is Bootstrap Aggregation (Bagging) 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?

    • Hyperparameters, Pros and Cons

  • Case Study: Ensemble Methods – Random Forest Techniques using Python

• Distance-Based Models

  • 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: 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 Hyperparameters for SVM:

      • Gamma, Cost, and Epsilon

    • SVMs with More than Two Classes

    • Case Study: SVM using Python

UNSUPERVISED LEARNING

Principal Components Analysis (PCA)

• Introduction to Dimensionality Reduction and its necessity

• What Are Principal Components?

• Demonstration of 2D PCA and 3D PCA

• Eigen Values, EigenVectors, and Orthogonality

• Transforming Eigenvalues into a new dataset

• Proportion of variance explained in PCA

• Case Study: PCA using Python

Clustering

• K-Means Clustering

  • Centroids and Medoids

  • Deciding the optimal value of ‘K’ using Elbow Method

  • Linkage Methods

• Hierarchical Clustering

  • Divisive and Agglomerative Clustering

  • Dendrograms and their interpretation

• Applications of Clustering

• Practical Issues in Clustering

• Case Study: Clustering using Python

RECOMMENDATION SYSTEMS

• What are recommendation engines?

• How does a recommendation engine work?

• Data Collection

• Data Storage

• Filtering the Data

• Content-based Filtering

• Collaborative Filtering

• Cold Start Problem

• Matrix Factorization

• Building a recommendation engine using matrix factorization

• Case Study: Recommendation Systems

CAPSTONE PROJECTS

• Regression Capstone Project: Use case covering Data Understanding, EDA, Data Processing, and Regression Techniques

• Classification Capstone Project: Use case covering Data Understanding, EDA, Data Processing, and Classification Techniques

• Unsupervised Learning Capstone Project: Use case covering Data Understanding, EDA, Data Processing, and Clustering/Dimensionality Reduction techniques

Our Data Science Course in Bengaluru is designed for:

• Beginners looking for a data science course for beginners in Bangalore
• Working professionals upgrading to AI & Data Science roles
• Students pursuing careers in Data Analytics & Data Science
• Job seekers wanting a data science certification with placement support

No prior coding skills are needed — just a passion for data and learning.

The course will take 6 months depending on how many hands-on projects, assessments, and placement prep are included.

Yes. We offer both classroom sessions (Hyderabad campus) and live online training.

Yes. We provide placement assistance, resume preparation, mock interviews, and introductions to hiring partners.