Big Data Analytics

Beginner to Mastery

Big Data with Data Science

End-to-End Data

Applied Learning with Projects.

Multi-Role Data Proficiency

Practical application of theories to solve industry challenges.

Group Enrollment with Friends or Colleagues

Big Data with Data Science

Course Duration

450 Hours

Next Batch

14 September 2025

Course Material

Live. Online. Interactive.

Broad and versatile training for diverse career paths and industries.

Up-to-date with current industry trends and technologies.

190 hours of self-paced Learning

Designed for both working professionals and fresh graduates

Highlight Big Data with Data Science

KEY HIGHLIGHTS OF BIG DATA WITH DATA SCIENCE PROGRAM

  • Weekly sessions with industry professionals
  • Dedicated Learning Management Team
  • 450 hours of hands-on learning experience
  • Over 150 hours live sessions spread across 07 months
  • 190 hours of self-paced Learning
  • Learn from IIT Faculty & Industry experts
  • More than 35+ industry-related projects and case studies
  • Personalised mentorship sessions with industry experts
  • 24*7 Support
  • Dedicated Learning Management Team
  • 1:1 Mock Interview
  • No-Cost EMI Option
  • Designed for both working professionals and fresh graduates
  • High Demand and Career Opportunities
  • Competitive Edge and Innovation
  • Problem-Solving and Critical Thinking

WHY JOIN BIG DATA WITH DATA SCIENCE PROGRAM?

In-Demand Skills

Gain expertise in data science, machine learning, and AI, with a focus on both foundational and cutting-edge technologies.

Hands-On Learning

Acquire practical experience with industry-standard tools and real-world applications.

Career Advancement

Boost your career prospects with a curriculum tailored to meet the demands of top employers.

Expert Instruction

Learn from industry professionals with real-world experience.

UPCOMING BATCH:

14 September 2025

SkillzRevo

SkillzRevo Solutions

30 MINUTE MEETING

Web conferencing details provided upon confirmation.

Corporate Training, Enterprise training for teams

Batch schedule

BatchBatch Type
Online Live Instructor Led SessionFull-Time
Online Live Instructor Led SessionPart-Time

Regional Timings

BatchBatch Type
IST (India Standard Time)09:00 PM–12:00 AM
Bahrain, Qatar, Kuwait, Saudi Arabia06:30 PM–09:30 PM
UAE / Oman07:30 PM–09:00 PM

Big Data with Data Science OVERVIEW

This program offers a comprehensive curriculum covering essential tools and technologies for managing and analyzing large datasets. Students will begin with Big Data and Python Programming, then move to Statistics, Machine Learning and Ending With Deep learning. Its use is for processing big data in Java, Hadoop and Spark are also covered as part of this course along with NoSQL and MongoDB that may be used to store unstructured data. Students will also learn advanced querying in SQL and data visualization with Tableau to work well with big-data.

ENROLL NOW & BOOK YOUR SEAT AT FLAT 50% WAIVER ON FEE

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Big Data with Data Science Objectives

This course is to provide the participants with hands-on-experience on managing, analyzing and interpreting high dimensional datasets. Some of the important technologies and methods that this course covers is; Python programming, Statistics, Machine Learning, Deep Learning / Neural Networks, Big Data Technologies (like Hadoop, Spark & NoSQL databases). Students will also become well-versed in SQL and Tableau data visualization skills. Upon completion of the program, participants should be able to apply big data technologies and data science techniques in successful decision-making processes and solving complex business issues within many industries.

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Why Learn Big Data with Data Science ?

Comprehensive Skill Set

Gain a robust understanding of both data science and big data technologies, making you proficient in managing, analyzing, and visualizing large datasets.

Industry Relevance

Master the tools and techniques that are highly sought after in the data-driven industry, including Python, Hadoop, Spark, and SQL.

Career Opportunities

Unlock diverse career opportunities like Data Engineer, Big Data Analyst, and Machine Learning Engineer, by mastering cutting-edge technologies.

Problem-Solving Abilities

Learn to apply machine learning and deep learning techniques to solve complex business problems and make data-driven decisions.

Hands-On Experience

Get practical experience with real-world datasets, enhancing your ability to implement theoretical knowledge in practical scenarios.

Data Visualization Skills

Develop the ability to communicate insights effectively using advanced data visualization tools like Tableau, a critical skill in any data-centric role.

Stay Ahead of the Curve

Keep up with the rapid advancements in big data and data science, positioning yourself as a valuable asset in any organization.

Program Advantages

Gain a deep understanding of key tools, from Hadoop and Spark to Python and Machine Learning.

Equip yourself with industry-relevant skills that keep you competitive in the job market.

Develop proficiency in a wide range of tools, including SQL, NoSQL, MongoDB, and Tableau.

Engage in hands-on projects that simulate real-world industry challenges.

Build a versatile skill set that opens up diverse career paths in data engineering, analytics, and machine learning.

Benefit from comprehensive coverage, from foundational concepts to advanced techniques in big data and data science.

Learn to leverage big data technologies for data-driven strategies and impactful decision-making.

Description

Big Data with Data Science program Certifications

Nasscom

Nasscom

Course Completion

Course Completion

Project Completion

Project Completion

Big Data with Data Science Curriculum

Lecture 01: Orientation (Introduction to Data Science, Scope of Data Science)
Lecture 02: Introduction to Python, Why Python, Variables, Data Types, Type Casting, Strings, Indexing
Lecture 03: Operators and Conditional Statements, Looping Statements and its Control Statement
Lecture 04: Lambda Functions, *args, **kwargs, Functions
Lecture 05: Data Structures - List, Tuple and List Comprehensions
Lecture 06: Data Structures - Set and Dictionaries
Lecture 07: Classes, Objects and Constructors, Inheritance
Lecture 08: Polymorphism, Abstraction and Encapsulation
Lecture 09: Connecting to Databases, Establishing connections to databases, Executing SQL Queries, ORM, Working with NoSQL Databases
Lecture 10: Introduction to Numpy and Pandas
Lecture 11: Introduction to Seaborn and Matplotlib
Lecture 12: Introduction to Statistics, Descriptive Statistics, Sample, Population, Measures of Central Tendency, Standard Deviation
Lecture 13: Variance, Range, IQR, Outliers, Correlation, Covariance, Skewness, Kurtosis, Probability
Lecture 14: Probability, Probability Distributions, Central Limit Theorem, Binomial and Poisson Distribution
Lecture 15: Normal Distribution, Type I & Type II Error
Lecture 16: T-test, Z-test, Hypothesis Testing Interview Questions
Lecture 17: Introduction to ML, Types of Variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression
Lecture 18: Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest
Lecture 19: Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R² Score, Adjusted R² Score, F1 Score
Lecture 20: Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, XGBoost
Lecture 21: Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical Clustering
Lecture 22: Introduction to Neural Networks, Forward Propagation, Activation Function
Lecture 23: Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics Gradient Descent
Lecture 24: Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution, Checkpoints and Neural Networks Implementation
Lecture 25: Introduction to Time Series Analysis, Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)
Lecture 26: ARMA and ARIMA
Lecture 27: Basics of Database, Types of Database, Data Types, SQL Operators, Expressions, Create, Insert
Lecture 28: Drop, Truncate, Delete, Alter, Update, Select, Range, Operator, IN, Wildcard, Like, Clause
Lecture 29: Constraint, Aggregation Functions, Group By, Order By, Having
Lecture 30: Joins, Case, Complex Queries, Doubt Clearing
Lecture 31: Tableau Desktop, Tableau Products
Lecture 32: Data Import, Measures, Filters
Lecture 33: Data Transformation, Marks, Dual Axis
Lecture 34: Manage Worksheets, Data Visualization, Dashboarding, Project
Lecture 35: Introduction, SQL vs NoSQL, Data Model, Data Types, Object ID, Data Type, Binary Data, Date, Null, Boolean, Integer, String
Lecture 36: Collection Methods, Queries, CRUD Operations (Insert, Find, Update, Delete), Validate, Bulk Write, Delete One
Lecture 37: Introduction to Java, Installation, Syntax (main()/println()/print()), Variables (String, Int, Boolean, Float, Char), Data Types, Operators
Lecture 38: Conditions, Loops, Methods, Classes, File Handling
Lecture 39: Types of Data, Introduction to Big Data (History, V's of Big Data, Advantages & Disadvantages), Big Data Applications in Various Sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Parallel Computing, Distributed Computing, Hadoop Tools Overview, Big Data Analytics Lifecycle
Lecture 40: On-Premises Installation of Oracle Virtual Box, Setup of VM & Ubuntu, Basic Linux Commands, Download and Installation of Hadoop, Introduction to Hadoop, Core Components of Hadoop, Hadoop Working Principles
Lecture 41: VM Creation on Cloud (Azure), Configuration & Insight into Single Node Hadoop Deployment (bsshrc, hadoop-env, core-site, hdfs-site, mapred-site, yarn-site), Format HDFS Namenode
Lecture 42: HDFS Architecture, Hadoop Commands and Implementation
Lecture 43: MapReduce, MapReduce Implementation
Lecture 44: Introduction to Hive, Hive Installation, Hive Implementation
Lecture 45: Hive Query Language, SQL Operations
Lecture 46: Hive SQL Operations
Lecture 47: Installation of Spark, PySpark, Introduction to Sqoop, Installation of Sqoop
Lecture 48: PySpark Query, Installation of Hbase, Hbase Query
Lecture 49: PIG Installation and Query
Lecture 50: PIG Query, Oozie
Lecture 51: Flume and Doubt Clear

Big Data with Data Science Skills Covered

Data analysis techniques
Python programming
Statistical Analysis
Machine Learning Techniques
Deep Learning Techniques
Java programming
Hadoop ecosystem tools and components
Spark Data Processing and Analytics
NoSQL Database Management
SQL querying
Tableau Data Visualization

Big Data with Data Science Tools Covered

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Big Data with Data Science Program Benefits

Big Data with Data Science Program Benefits Illustration

CAREER OPPORTUNITIES AFTER THIS COURSE

Big Data Analyst Salary Range

Min

$60,000

Average

$100,000

Max

$150,000

Projects

MASTER GANS, TRANSFORMERS & DEEP LEARNING WITH REAL PROJECTS

Practice Essential Tools

Designed By Industry Experts

Get Real-world Experience

Machine Learning
NO. OF PROJECTS: 10
Deep Learning
NO. OF PROJECTS: 10
Generative AI
NO. OF PROJECTS: 8

Capstone Projects of this Program

Multimodal Text-to-Image Generator

A system that creates high-quality, realistic images from descriptive text inputs, blending multiple data types for creative visual output.

AI Music Composer with Genre Adaptation

An AI tool that generates original musical compositions while adapting to specific genre styles, tempo, and mood preferences.

Realistic Deepfake Video Synthesizer

A tool that realistically manipulates and synthesizes video content to create authentic-looking deepfake footage for various applications.

Personality-Driven Conversational AI Assistant

A chatbot designed to interact with a distinct and consistent personality, providing a more natural and engaging user experience.

Artistic Style Transfer Engine for Videos

A program that applies the artistic style of a source image to every frame of a video, creating a new, stylized visual experience.

Context-Aware Text Summarization System

An advanced system that generates concise summaries of long texts by understanding the document's broader context and key information.

Voice Cloning and Speech Style Transfer Tool

A tool that replicates a speaker's voice and transfers a specific speech style or emotion to new synthesized audio content.

Interactive AI Storyteller with Image Generation

An AI that collaboratively creates stories with users, dynamically generating scenes and characters based on a developing narrative.

2D Sketch to 3D Object Generation Pipeline

A seamless workflow that converts simple two-dimensional sketches into detailed, three-dimensional models for creative and design purposes.

Synthetic Data Generator for Model Training

A system that produces large and diverse datasets for machine learning models, overcoming data scarcity for effective training.

AI-Powered Video Subtitle Generator and Translator

An automated solution that transcribes spoken language in videos and translates it into accurate subtitles for global accessibility.

Generative Design Tool for Product Prototyping

An AI application that rapidly generates numerous product design prototypes based on a set of constraints and functional requirements.

RAG-based Research Assistant (Retrieval-Augmented Generation)

An intelligent assistant that answers complex queries by retrieving relevant information from large documents and generating coherent responses.

Multilingual Chatbot with Memory and Personality

A conversational agent that can communicate in multiple languages while maintaining a consistent personality and recalling past interactions.

AI-Powered Comic Strip Generator from Text Prompts

A tool that creates unique comic strips by converting user text prompts into sequential images with a consistent artistic style.

Explainable AI Engine for Text Classification

An AI system that classifies text and provides clear, understandable reasons for its decisions, ensuring transparency and trust.

Autonomous Slide Generator from Research Papers

An AI that automatically extracts key findings and data from research papers to create well-structured, presentable slide decks.

AI Assistant for Code Generation and Explanation

A helpful tool that writes code snippets and explains complex programming logic in simple terms, assisting developers with their tasks.

AI-Driven Marketing Content Generator Across Formats (text, image, video)

A unified platform that produces marketing materials in various formats, including text, images, and videos, from a single campaign brief.

Real-Time Emotion Detection and Response AI System

A system that identifies human emotions from facial expressions or speech and generates appropriate, empathetic responses in real time.

Job Obligation After This Course

WE CAN APPLY FOR JOBS IN

Collect, process, and analyze large datasets to extract valuable insights.

Build and implement machine learning and deep learning models for predictive analytics.

Design and maintain data pipelines, ensuring efficient data storage and retrieval.

Create and present data visualizations and reports to communicate findings effectively.

Utilize tools like Hadoop, Spark, and NoSQL databases to manage and process data.

Work with cross-functional teams to understand data needs and provide data-driven solutions.

Address and resolve data-related issues and optimize data processing workflows.

Document data processes, methodologies, and models for future reference and reproducibility.

Companies Hiring for this Course

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Admission Process

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Course Fees & Financing

Course Fees

Enroll Now & Save Up To

60%

In USD

$1799

In INR

149,999

Inclusive of All Taxes

Enroll Now →
Payment Partners

We partnered with financing companies to provide competitive finance options at 0% interest rate with no hidden costs.

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UPCOMING BATCHES/PROGRAM COHORTS

BatchDateTime (IST)Batch Type
Online Live Instructor Led Session30 August 20259 PM to 12 AMBatch 1
Online Live Instructor Led Session14th Sept 20259 PM to 12 AMBatch 2

COMPARISON WITH OTHERS

FeatureOur CourseCOMPETITOR A
Course CoverageComprehensive: Big Data, Data Science, Python, ML, DL, Hadoop, MapReduce, Sqoop, Scala, Hbase, Pyspark, Pig, Oogie, HDFS, Spark, SQL, NoSQL, MongoDB, TableauVaries; often focused on specific areas or fewer tools
Hands-On ExperienceExtensive practical projects and real-world dataMay have limited practical application
Industry-Relevant SkillsUp-to-date with current industry trends and technologiesMay lack focus on the latest tools and technologies
Career OpportunitiesWide range: Data Engineer, Data Scientist, ML Engineer, etc.May be narrower or less specialized
Practical ApplicationEmphasis on applying skills in real-world scenariosMay focus more on theoretical knowledge
VersatilityBroad skill set applicable to various roles and industriesOften more specialized or limited to specific roles
Technical ProficiencyMastery of key tools and technologiesMay offer basic or partial training in relevant tools

Frequently Asked Questions