6 Months | Advanced Data Science Certification Course in Hyderabad

Data Science & Gen AI Certification Course with Placement Assistance

Job - Ready Course

Data Science & Gen AI Certification Course with Placement Assistance

Job - Ready Course

Launch your career as a Data Scientist with in-demand skills like Machine Learning, Gen AI, Tableau, and SQL. With 14,000+ jobs on LinkedIn India and 137K+ roles expected by 2025, now’s the time to get industry-ready with real-world projects.

Mode: Offline | Online | Live Recorded Sessions
180 Hours of learning
Guaranteed virtual internships with Accenture & PWC
Certificates from Nasscom and Microsoft
7+ Real Time projects
Guaranteed Interview Opportunities**

In Collaboration With

Key-Highlights

Hybrid Mode of learning (Online, Offline and access to live recorded sessions)

Guest Lectures with Industry leaders from Microsoft & IIM Faculty

10+ Capstone Projects + 1 Individual end-to-end Project

1:1 Interactions with mentors to track progress

Exclusive batches for beginners

Exclusive Community access

Corporate Readiness Program

Profile & Resume building sessions

Accreditations and Certification

Course Overview

In this Data Science Certification course, we will provide the skills you need to enhance your career in data science and prepare to become the best in the evolving field.
So by the end of the course, you will have gained industry-relevant skills in Python, SQL, Machine learning and NLP, Generative AI, Tableau, and Power BI.

What topics are included in the Data Science certification course?

The topics covered in this Data Science program and the modules you will learn are

Module 1 – Python Fundamentals and Oops Concept
Module 2 – Data Handling with Pandas APIs and Web Services
Module 3 – Complete Data Visualization and Advanced Data Analysis
Module 4 – Advanced Data Handling with Pandas and Web Scraping
Module 5 – Statistics with Probability Distributions and Inference
Module 6 – Advanced Machine Learning with Supervised /Unsupervised learning
Module 7 – Natural Language Processing (NLP) with Model Deployment and Productionization
Module 8 – Deep Learning Foundations with Training Techniques
Module 9 – Generative AI and Tableau
Module 10 – Advanced SQL with Power BI
Module 11 – Integration with Other Tools
Module 12 – Best Practices and Case Studies
Module 13 – Advanced Neural Network Architectures and Attention Mechanisms

Which skills will you gain by the end of this Data Science program?

Python For Data Science: Enhance skills like Advanced Python Programming and Control Flow, Data Structures, Variables and Data Types, and OOPs and Pandas and learn Web Scraping
Statistics: To become a great data scientist, you need to master the mathematics and statistics in Probability Distributions
MLOps: By the end of this Data Science program, you will you will have hands-on experience in machine learning with
Generative AI: Generative AI and learn to build models that create text, images, and music and build next-generation AI applications
Power BI: You can Master Power BI, Microsoft’s widely used data visualization tool. Learn to create interactive dashboards and management reports.
SQL: By the end of the course, you will learn to do querying databases and Data extraction and manipulation
Tableau: The leading data visualization tool that creates interactive dashboards

Why are Data Scientists in high demand across industries?

Data scientists are in high demand because industries can’t move forward without analyzing the data and extracting its insights. Since the rise of AI and machine learning, everything has been compiled into data. So, without data scientists, industries can’t move forward and make big decisions. That’s why data scientists are important in industries. They ensure that decisions are made properly by ensuring that we get the data and analyze it.

What does a day in the life of a Data Scientist look like?

Data Collection & Cleaning: Gather data from various sources, do the web scraping, and ensure data quality and integrity
Exploratory Data Analysis (EDA): Understand the data distribution with summary statistics and use visualizations to identify patterns and trends
Testing & Validation: Split data into training and test sets and apply statistical and prevent overfitting
Model Deployment: Train machine learning models and use APIs, cloud platforms, or local servers.
Data Visualization & Reporting: Create dashboards with tools like Tableau, Power BI
Code Review & Documentation: keep clean, well-commented code Document assumptions and findings.
Experimentation: Test different models with A/B testing

Tools Covered

Know Your Mentors

Swapnil

Sr. Data Analyst

20 Years Experience

Vishnu Murthy

Sr. Data Scientist

20 Years Experience

Satya Kumar

Sr. Data Scientist

12 Years Experience

Satish

Sr. Data Scientist

5 Years Experience

Program Highlights

Master data science from experts who make the concepts easy and fun to learn

Learn directly from the Industry experts
Interactive and engaging sessions helping you learn the real-world concepts
Simplified learning approach to make tough data concepts easy to grasp
Hands-on activities to help you apply what you learn instantly
Gain firsthand industry insights to make you job-ready

The WhiteScholars data science course helps you find real job opportunities through strong industry connections.

WhiteScholar's data science course is structured in collaboration with senior leading data scientists to match the real-time industry needs.

Designed with insights from top data science industry experts
Focuses more on a practical approach to learning
Covers Python, SQL, Machine learning, and Power BI
The Data Science course is aligned with the industry needs
Work on live projects, weekly assignments, 7+ industry-relevant projects, and individual projects
Get job-ready skills that companies actually look for.

Work on real-time projects, case studies, and relevant assignments, with the course making you industry-ready.

Developing personalized recommendation systems:
a. Understanding Netflix Recommendation Engine
b. Swiggy’s dish recommendations
Diagnosing diseases through MRI scan analysis of body organs.
Creating conversational AI bots to enhance customer interactions and satisfaction.
Predicting the likelihood of fraudulent insurance claims.
Classifying product reviews on e-commerce platforms.
Building AI based Smart Search applications

Masters Who Mentor

Mentoring from the Industry experts who have more than 20 years of experience in the data science field. Our mentors help you to gain knowledge from real-world industry experience.

Network of 25+ hiring managers

We have a dedicated network of hiring managers with whom we have built trust over time, and they are actively seeking top talent.

Tailored capstone projects

1.Developing personalized recommendation systems:

Netflix Recommendation Engine
Swiggy’s dish recommendations,

2.Diagnosing diseases through MRI scan analysis of body organs.

3.Creating a Conversational AI bot to enhance customer interactions and satisfaction.

4.Predicting the likelihood of fraudulent insurance claims.

⁠5.Classifying product reviews on e-commerce platforms.

⁠6.Building AI-based Smart Search applications

Curriculum driven by Industry needs

Our industry-ready curriculum is developed to meet the real-time job market. It helps the students to get hands-on experience and practical skills in today's competitive market.

Curriculum and modules

1. Introduction to Programming and Python

What is programming?
History of Python
Setting up the development environment
IDLE
Jupyter Notebook
VS Code
Writing and running your first Python program

Basic Syntax

Comments
Indentation
Printing to the console

Variables and Data Types

Numbers (integers, floats)
Strings
Booleans

Basic Operators

Numbers (integers, floats)
Strings
Booleans

Control Flow

Conditional statements (if, elif, else)
Loops (for loops, while loops)
Break, continue, and pass statements.

Functions

Defining and calling functions: Function arguments and return values
Scope and lifetime of variables in Lambda functions

Data Structures: Lists

Creating and accessing lists
List operations (indexing, slicing, adding, removing elements)
List methods
append
extend
insert
remove
pop
clear
index
count
sort
Reverse

Data Structures: Tuples and Sets

Creating and accessing tuples
Tuple methods
Creating and accessing sets
Set operations
union
intersection
Difference

Data Structures: Dictionaries

Creating, accessing, and modifying dictionaries: Dictionary methods.
keys
values
items
get
pop
update

Working with Strings

String operations
concatenation
slicing
formatting
String methods
find
replace
split
join
lower
upper
Strip

File Handling

Reading from files
Writing to files
Working with CSV files using the csv module

Modules and Packages

Importing modules
Standard library overview
Math
Datetime
Random
Installing and using third-party packages (pip)

Error Handling

Understanding exceptions
Try, except, else, finally blocks
Raising exceptions

Object-Oriented Programming (OOP)

Classes and objects
Attributes and methods
Inheritance
Polymorphism

Comprehensions

List comprehensions
Dictionary comprehensions
Set comprehensions

Decorators and Generators

Understanding and using decorators
Creating and using generators

Regular Expressions

Introduction to regular expressionss
Using the re module for pattern matching

Advanced Data Handling with Pandas

Introduction to Pandas
DataFrames and Series
Reading from and writing to different file formats
CSV
Excel
JSON
Data cleaning and manipulation

Data Visualization

Introduction to Matplotlib and Seaborn
Plotting graphs and charts
Customizing plots

Web Scraping

Introduction to web scraping
Using BeautifulSoup and requests
Handling web scraping challenges
Pagination
Dynamic content

APIs and Web Services

Understanding APIs
Making HTTP requests using requests
Parsing JSON data

Working with Databases

Introduction to SQL and databases
Using SQLite with Python
Performing a CRUD operation

Concurrency and Parallelism

Understanding concurrency vs. parallelism
Using the threading and multiprocessing modules

Testing and Debugging

Writing unit tests with unittest and pytest
Debugging techniques and tools

Advanced OOP Concepts

Abstract classes and interfaces
Design patterns
Metaclasses

Advanced Python Libraries

Introduction to NumPy for numerical computing
SciPy for scientific computing
Exploring additional libraries as per interest
TensorFlow for machine learning.

2. Data Visualization

2a. Introduction to Data Visualization OOP Concepts

Fundamentals of Data Visualization
Importance of data visualization
Key principles of effective data visualization
Overview of common visualization types
bar charts
line charts
scatter plots
Introduction to Tableau and Power BI
Introduction to Tableau: installation, interface overview
Introduction to Power BI: installation, interface overview

2b. Data Connection and Preparation

Tableau

Connecting to data sources (Excel, CSV, databases)
Data cleaning and preparation
Understanding Tableau data types and relationships

PowerBI

Connecting to data sources (Excel, CSV, database s)
Data cleaning and preparation
Understanding Power BI data types and relationships

2c. Basic Visualization Techniques

Tableau

Creating basic charts (bar, line, pie)
Customizing charts (colors, labels, tooltips)
Using filters and sorting data

PowerBI

Creating basic charts (bar, line, pie)
Customizing charts (colors, labels, tooltips)
Using filters and sorting data

2d. Advanced Visualization Techniques

Tableau

Creating advanced charts (heat maps, tree maps, bullet charts),
Using calculated fields
Parameters and input controls

PowerBI

Creating advanced charts (heat maps, tree maps, bullet charts), using DAX (Data Analysis Expressions) for calculations, Parameters, and input controls

2e. Dashboards and Interactive Reports

Tableau

Designing interactive dashboards,
Adding interactivity with actions (filters, highlights),
Creating stories for data presentation,

PowerBI

Designing interactive dashboards,
Adding interactivity with slicers and filters,
Creating reports for data presentation.

2f. Data Preparation and Cleaning Techniques

Tableau

Data blending,
Data joins and unions,
Handling null values and outliers,

PowerBI

Data transformation using Power Query,
Data joins and merges:
Handling null values and outliers.

2g. Advanced Data Analysis

Tableau

Time series analysis
Forecasting and trend analysis
Cohort analysis,

PowerBI

Time series analysis,
Forecasting and trend analysis,
Cohort analysis

2h. Geographic and Geospatial Analysis

Tableau

Creating maps
Spatial joins and distance calculations,
Advanced map visualizations,

PowerBI

Creating maps,
Spatial joins and distance calculations,
Advanced map visualizations.

2i. Integration with Other Tools

Tableau

Exporting Tableau visualizations,
Embedding Tableau in websites and applications,
Connecting Tableau to R and Python for advanced analytics,

PowerBI

Exporting Power BI visualizations,
Embedding Power BI in websites and applications,
Connecting Power BI to R and Python for advanced analytics

2j. Best Practices and Case Studies

Best Practices for Effective Data Visualization
Design principles
Avoiding common pitfalls
Case Studies and Real-world Applications
Reviewing industry-specific use cases
Hands-on case study analysis.

3. Statistics

3a. Introduction to Statistics

Descriptive Statistics
Measures of central tendency: mean, median, mode.
Measures of dispersion: range, variance, standard deviation, interquartile range.
Data Visualization
Histograms, bar charts, and pie charts
Box plots, scatter plots
Probability Basics
Probability theory and rules
Conditional probability and Bayes’ theorem
Probability distributions: discrete and continuous

3b. Probability Distributions

Discrete Distributions
Bernoulli
Binomial
Poisson distributions
Continuous Distributions
Uniform, Normal (Gaussian)
Exponential distributions
Central Limit Theorem
Multivariate Distributions
Multinomial distribution
Multivariate Normal distribution

3c. Statistical Inference

Sampling and Sampling Distributions
Point Estimation and Properties of Estimators
Bias
Variance
Mean Squared Error (MSE)
Interval Estimation
Confidence intervals for means and proportions
Hypothesis Testing
Null and alternative hypotheses
Type I and Type II errors
p-values and significance levels
t-tests
chi-square tests
ANOVA

3d. Bayesian Statistics

Bayesian vs Frequentist methods
Prior, likelihood, and posterior distributions
Bayesian inference and applications

4. Machine Learning

4a. Introduction to Machine Learning

Overview and history of machine learning
Types of machine learning: supervised, unsupervised, reinforcement learning
Key terminology
Features
Labels
Training set
Test set
Validation set

4b. Data Preprocessing

Data cleaning
Handling missing values
outliers
Feature scaling
normalization
standardization
Encoding categorical variables
one-hot encoding
label encoding
Feature engineering and selection techniques

4c. Supervised Learning

Linear Models
Linear regression
Logistic regression
Decision Trees
Construction and interpretation of decision trees
Pruning techniques to avoid overfitting
Ensemble Methods
Bagging (Bootstrap Aggregating)
Random Forests
Boosting (AdaBoost, Gradient Boosting)
Support Vector Machines (SVM)
Concept of hyperplane and support vectors
Kernel tricks: linear, polynomial, radial basis function (RBF), K-Nearest Neighbors (KNN)
Distance metrics: Euclidean, Manhattan
Choosing the value of K
Gradient Boosting Machines (GBMs)
XGBoost
LightGBM
CatBoost
Advanced SVM techniques
Advanced kernel techniques
Support Vector Regression(SVR)

4d. Unsupervised Learning

Clustering
K-means clustering
Hierarchical clustering: agglomerative and divisive
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Dimensionality Reduction
Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Linear Discriminant Analysis (LDA)
Association Rule Learning
Apriori algorithm

4e. Model Evaluation and Validation

Train-test split
Cross-validation
k-fold
stratified k-fold
Evaluation metrics for regression
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Mean Absolute Error (MAE)
R²
Evaluation metrics for classification
accuracy
precision
recall
F1 score
ROC-AUC

4f. Introduction to Neural Networks

Perceptron and multi-layer perceptron
Activation functions
sigmoid
tanh
ReLU
Backpropagation and gradient descent

4g. Introduction to Time Series Analysis

Definition and examples of time series data
Components of time series
trend
seasonality
cyclic patterns
irregular components
Time series vs. cross-sectional data

4h. Descriptive Analysis

Plotting time series data
Moving averages and smoothing techniques
Seasonal decomposition of time series
STL (Seasonal and Trend decomposition using Loess)

4i. Stationarity and Differencing

Definition of stationarity
Augmented Dickey-Fuller (ADF) test for stationarity
Differencing to achieve stationarity
Seasonality adjustment

4j. Autocorrelation and Partial Autocorrelation

Autocorrelation Function (ACF)
Partial Autocorrelation Function (PACF)
Identifying patterns using ACF and PACF plots

4k. Time Series Models

Autoregressive (AR) models
AR(p) process and Yule-Walker equations
Moving Average (MA) models
MA(q) process and invertibility
Autoregressive Moving Average (ARMA) models
ARMA(p,q) process and parameter estimation
Autoregressive Integrated Moving Average (ARIMA) models
ARIMA(p,d,q) process, and model selection
Seasonal ARIMA (SARIMA) models

4l. Model Evaluation and Validation

Splitting time series data
Training and testing sets
Walk-forward validation
Forecasting accuracy metrics
Mean Absolute Error (MAE)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Mean Absolute Percentage Error (MAPE)

5. Advanced Machine Learning Concepts

5a. Deep Learning

Convolutional Neural Networks (CNNs)
Convolution and pooling layers
Famous architectures: LeNet, AlexNet, VGG, ResNet
Recurrent Neural Networks (RNNs)
Vanishing gradient problem
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)

5b. Natural Language Processing (NLP)

Text Preprocessing
Tokenization
Stemming
Lemmatization
Stopwords removal
Word Embeddings
Word2Vec
GloVe
Sequence Models
RNNs
LSTMs
GRUs
Transformers
Attention mechanism
BERT
GPT models

5c. Model Deployment and Productionization

Model serving and APIs
Containerization with Docker
Model monitoring and management
Scaling machine learning models in production

6. Deep Learning Foundations

6a. Deep Learning Basics

Overview of deep learning and neural networks
Key concepts
neurons
layers
activation functions
Loss functions
Introduction to deep learning frameworks
TensorFlow
PyTorch

6b. Optimization and Training Techniques

Gradient descent and variants
SGD
Adam
RMSprop
Learning rate schedules and techniques to avoid overfitting
dropout
regularization
Batch normalization and its impact on training

6c. Advanced Neural Network Architectures

Convolutional Neural Networks (CNNs)
Review of CNNs
Advanced CNN architectures (ResNet, DenseNet, EfficientNet)
Techniques to improve CNN performance
Data augmentation
Transfer learning
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
Overview of RNNs and LSTMs
Advanced architectures: GRU, Bidirectional RNNs, and Attention mechanisms
Applications of RNNs in time series and sequence modeling

6d. Transformers and Attention Mechanisms

Introduction to Attention Mechanisms
The need for attention in deep learning
Types of attention mechanisms
self-attention
cross-attention
Applications of attention in various domains
Transformers: Theory and Architecture
The transformer architecture
encoder
decoder
multi-head attention
Positional encoding and its importance
Understanding the transformer model through the “Attention is All You Need” paper

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Learner's Journery

Stand out to recruiters with a powerful resume and portfolio that showcase your skills, projects, and potential.

Make your LinkedIn profile work for you and get noticed by the right people.

WhiteScholars offers exclusive programs to step into the corporate world with confidence

Boost your confidence with mock interviews, expert tips and real time scenarios

Get expert interview tips from experts
Practice with mock interview sessions
Experience real time scenarios with technical and HR rounds
Receive personalized feedback to improve.
Get frequently asked Interview questions from senior data scientists.

WhiteScholars offers a placement process that fills the gap between learning and landing a job

Understanding the hiring requirements of the companies
Tailored training with a customised crash course as per the JD
Exclusive one-on-one Interview preparation
Go through a skill gap assessment test
On clearing it, appear for the interviews, else get additional training support
Unlimited Interview opportunities

Career Transitions

Meghna Velluri

Junior Data Scientist | UST

Junior Data Scientist

Whitescholars have helped in gaining industry knowledge with this Data Science course. I am thankful to every mentor for supporting me and giving me the right opportunity.

Fresher

Junior Data Scientist

Revanth

Product Support Engineer | ZETA GLOBAL

Placed as a Product Support Engineer

I always wanted to be a Data Scientists as I come from a CSE background. Whitescholars Data science course in Hyderabad has helped me to crack this interview. They have helped me beyond their limits.

Fresher

Product Support Engineer

Kamal

Software Developer | LTI Mindtree

Placed as Software Developer

The instructors are very helpful and has guided me throughout the Profeesional Data Science course with their expertise. The real time projects that I did here helped me to be visible to recruiters.

Fresher

software Developer

Abhinay

Product Support Engineer | ZETA Global

Placed as Product Support Engineer

The Data Science course at Intellipaat is comprehensive and practical. With their help, I received job offers from multiple companies. The industry-relevant curriculum and hands-on projects were exactly what I needed.

Product Analyst

Product Support Engineer

Jennifer

Junior Data Analyst | Tech Aspiron

Placed as Junior Data Analyst

I got placed through the WhiteScholars Placement drive, along with the Data Science Curriculum, we are also given mock interviews, doubt clearing sessions, weekly assignments and project presentations.

Fresher

Junior Data Analyst

Priya Sharma

Data Scientist | India

Placed as Data Scientist

Product Analyst

Data Scientist

55% Average Salary Hike

45 LPA The Highest Salary

12000+ Career Transitions

500+ Hiring Partners

Partnered Companies

Salary & Role Trends of Data Scientists

Explore average salaries, job roles, and top hiring companies in the Data Science field.

Designation

Data Scientist

AI & ML Engineer

Sr Data Scientist

Annual Salary

Source: Glassdoor

Companies Who Hire Our Students

Physical Classroom Session

Mode of training: Classroom

Only 20 students in one classroom
No cost EMI facility
Lifetime access to live recorded sessions
Informative sessions from Industry experts
Project presentations to guest faculties
360 degree career assistance
Guranteed Interview opportunities **

Next Batch: 5th May 2025

INR 64,999

2064 Learners

485 Review

Instructor Led Training

Mode of training: Online

Online classes: weekdays
Access to online class recordings for 365 days
No cost EMI facility
Informative sessions from Industry Experts
Interview support services
Guranteed Interview opportunities **

Next Batch: 5th May 2025

INR 59,999

2064 Learners

485 Review

Hybrid Classes

Mode of training: Online + Offline

Only 20 students in one classroom
No cost EMI facility
Lifetime access to live recorded sessions
Informative sessions from Industry experts
Project presentations to guest faculties
360 degree career assistance
Guranteed Interview opportunities **

Next Batch: 5th May 2025

INR 64,999

2064 Learners

485 Review

What is the scope of certified Data sciencists in India

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Date

Time

Batch Type

Date

Program Induction

5 May, 2025

19:00 IST

19:00 IST

Regular Classes

10 May, 2025 – 18 Jan, 2026

19:00 – 23:00 IST

Weekdays & Weekend

Video Testimonials

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