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Data Science Course

Join our industry-aligned Advanced Data Science course designed to make beginners and professionals job ready in Data Science. With hands-on engaging small group projects, expert mentorship support, and advanced tools such as Gen-AI and LLMs, we prepare you for high-growth, specified roles in our AI world today.

NASSCOM FutureSkills Prime Certification
Hands‑On, Real‑World Training
Industry‑Led Expert Mentors
Flexible Learning Formats
100% Placement Assistance
Ready for the Future of AI

Batches

Free Internships

Hiring Partners

Career Transitions

Programming Languages and Tools

Job Titles for Data Science Course

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Data Scientist

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Data Analyst

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Machine Learning Engineer

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Business Intelligence (BI) Analyst

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Business Analyst (Data-driven)

Data Science Course Curriculum (Syllabus)

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Module 1: Python Core and Advanced

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

Module 2: Exploratory Data Analysis using Python

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

Module 3: Advanced Statistics

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

Module 4. SQL for Data Analysis

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

Module 5: Machine Learning Supervised Learning

INTRODUCTION

What Is Machine Learning?
Supervised Versus Unsupervised Learning
Regression Versus Classification Problems Assessing Model Accuracy

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

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 on Linear, Multiple Linear Regression, Polynomial, Regression using Python

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

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 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.

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Module 6: Machine Learning Unsupervised Learning

Why Unsupervised Learning
How it Different from Supervised Learning
The Challenges of Unsupervised Learning

Principal Components Analysis

Introduction to Dimensionality Reduction and its necessity
What Are Principal Components?
Demonstration of 2D PCA and 3D PCA
Eigen Values, EigenVectors, and Orthogonality
Transforming Eigen values into a new data set
Proportion of variance explained in PCA

Case Study: A Case Study on PCA using Python

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: A Case Study on clusterings using Python

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

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

Module 7: Artificial Intelligence and Deep Learning

Introduction to Neural Networks

Introduction to Perceptron & History of Neural networks
Activation functions
- a)Sigmoid b)Relu c)Softmax d)Leaky Relu e)Tanh
Gradient Descent
Learning Rate and tuning
Optimization functions
Introduction to Tensorflow
Introduction to Keras
Backpropagation and chain rule
Fully connected layer
Cross entropy
Weight Initialization
Regularization

TensorFlow 2.0

Introducing Google Colab
Tensorflow basic syntax
Tensorflow Graphs
Tensorboard

Artificial Neural Network with Tensorflow

Neural Network for Regression
Neural Network for Classification
Evaluating the ANN
Improving and tuning the ANN
Saving and Restoring Graphs

Module 8: Computer Vision (CV)

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

Module 9: Natural Language Processing (NLP)

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

Module 10: Power BI

Introduction To Power Bi

What is Business Intelligence?

Power BI Introduction
Quadrant report
Comparison with other BI tools
Power BI Desktop overview
Power BI workflow
Installation query addressalData Import And Visualizations
Data import options in Power BI
Import from Web (hands-on)
Why Visualization?
Visualization typesData Visualization (Contd.)
Categorical data visualization
Visuals for Filtering
Slicer details and use
Formatting visuals
KPI visuals
Tables and Matix
Power Queries
Power Query Introduction
Data Transformation – its benefits
Queries panel
M Language briefing
Power BI Datatypes
Changing Datatypes of columnsPower Queries (Cond.)
Filtering
Inbuilt column Transformations
Inbuilt row Transformations
Combine Queries
Merge QueriesPower Pivot And Introduction To Dax
Power Pivot
Intro to Data Modelling
Relationship and Cardinality
Relationship view
Calculated Columns vs Measures
DAX Introduction and Syntax

Frequently Asked Questions (FAQs)

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1. Who can enroll in this Data Science program?

This program is perfect for graduates, working professionals, and students from B.Tech, B.Sc, or any other history who are looking to switch into Data Science or upskill.

2. What is the length of the course?

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

3. Do you offer classroom training and/or online training?

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

4. Are you providing student placement assistance?

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

5. What is the eligibility criteria to learn Data Science course?

Anyone with a bachelor’s degree, a keen interest in Data Science, and a basic understanding of the subject is eligible to enroll in the course.

6. What are the career opportunities in Data Science Technology?

As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.

Here are some of the roles you can find in Data Science

Research Analyst
Data Scientist
Data Analyst
Big Data Analytics Specialist
Business Analyst Consultant / Manager
Data analyst

7. What is the average salary of a Data Scientist?

In India, Data Scientist salaries vary widely, typically ranging from ₹3 LPA to ₹20 LPA, depending on skills and experience. Here’s a quick breakdown:

Data Analyst: ₹3–7 LPA

Junior Data Scientist: ₹6–9 LPA

Data Scientist: ₹10–15 LPA