BIGDATA

★★★★★

(2,095 ratings) 50,000+ Student

ADVANCED BIG DATA SCIENCE TRAINING

This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analyzing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.

Radical Technologies

50,000+ English 10 weeks Weekdays / Weekends Classroom / Online / Corporate

Online / Classroom

IT Training Programme

Duration 10 weeks

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Globally Recognized

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100% placement assistance

What you'll learn

Understand core concepts and architecture from the ground up

Get hands-on with the tools used by working professionals

Build real-world projects you can add to your portfolio

Learn industry best practices and coding standards

Practice with real datasets and real-world scenarios

Prepare for certification and technical interviews

Work on collaborative, team-based exercises

Apply performance tuning and optimization techniques

Understand how the technology fits into a larger ecosystem

Complete assignments reviewed by mentors

Programme Overview

14 sections covering the complete curriculum — a single, progressive learning arc.

10 weeks

Training Duration

Core Modules

159

Total Lessons

4.4★

Average Rating

50K+

Students Trained

Foundations & Core Concepts

Get hands-on with the fundamentals and architecture — the building blocks for everything that follows.

Fundamentals Architecture Setup

Hands-On Practical Training

Work through real exercises and assignments designed to mirror what you will do on the job.

Practicals Assignments Labs

Real-World Projects

Apply what you have learned to end-to-end projects that go straight into your portfolio.

Projects Portfolio Case Studies

Advanced Techniques

Go beyond the basics with advanced concepts, integrations and production-grade practices.

Advanced Integration Best Practices

Ecosystem Integration

Understand how this technology connects with the broader tools and platforms used in the industry.

Ecosystem Tools Platforms

Performance & Interview Prep

Master optimization techniques and prepare for the technical interview questions employers actually ask.

Optimization Interview Prep Certification

Who is this programme for?

Whether you're already writing code, working with data, or supporting applications today — this programme is built to take you into a BIGDATA role.

Software Developers

Engineers who want to add this skill set to their toolkit

Analysts & Consultants

Professionals moving into a more technical, hands-on role

IT Professionals

System admins and support engineers upskilling into a new domain

Fresh Graduates

CS/IT graduates aiming for a job-ready technical role

Course Curriculum

14 sections • 159 lessons • 10 weeks

Jump to Section

Total Duration

10 weeks

100% Practical · Unlimited Doubt Sessions

01 Course content summary/ Python Essentials (Core)

Course content summary

Introduction To Data Science

What is Data Science?

Why Python for data science?

Relevance in industry and need of the hour

How leading companies are harnessing the power of Data Science with Python?

Different phases of a typical Analytics/Data Science projects and role of python

Anaconda vs. Python

Python Essentials (Core)

Overview of Python- Starting with Python

Introduction to installation of Python

Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)

Understand Jupyter notebook & Customize Settings

Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)

Installing & loading Packages & Name Spaces

Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)

List and Dictionary Comprehensions

Variable & Value Labels – Date & Time Values

Basic Operations – Mathematical – string – date

Reading and writing data

Simple plotting

Control flow & conditional statements

Debugging & Code profiling

How to create class and modules and how to call them?

Scientific distributions used in python for Data Science – Numpy, scify, pandas, scikitlearn, statmodels, nltk etc

02 Accessing/Importing And Exporting Data Using Python Modules / Data Manipulation – Cleansing – Munging Using Python Modules

Accessing/Importing And Exporting Data Using Python Modules

Importing Data from various sources (Csv, txt, excel, access etc)

Database Input (Connecting to database)

Viewing Data objects – subsetting, methods

Exporting Data to various formats

Important python modules: Pandas, beautifulsoup

Data Manipulation – Cleansing – Munging Using Python Modules

Cleansing Data with Python

Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)

Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)

Python Built-in Functions (Text, numeric, date, utility functions)

Python User Defined Functions

Stripping out extraneous information

Normalizing data

Formatting data

Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

03 Data Analysis – Visualization Using Python / Basic Statistics & Implementation Of Stats Methods In Python

Data Analysis – Visualization Using Python

Introduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)

Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

Basic Statistics & Implementation Of Stats Methods In Python

Basic Statistics – Measures of Central Tendencies and Variance

Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem

Inferential Statistics -Sampling – Concept of Hypothesis Testing

Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square

Important modules for statistical methods: Numpy, Scipy, Pandas

Details coming soon.

05 Python: Machine Learning -Predictive Modeling – Basics / Machine Learning Algorithms & Applications – Implementation In Python

Python: Machine Learning -Predictive Modeling – Basics

Introduction to Machine Learning & Predictive Modeling

Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting

Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)

Overfitting (Bias-Variance Trade off) & Performance Metrics

Feature engineering & dimension reduction

Concept of optimization & cost function

Concept of gradient descent algorithm

Concept of Cross validation(Bootstrapping, K-Fold validation etc)

Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics)

Machine Learning Algorithms & Applications – Implementation In Python

Linear & Logistic Regression

Segmentation – Cluster Analysis (K-Means)

Decision Trees (CART/CD 5.0)

Ensemble Learning (Random Forest, Bagging & boosting)

Artificial Neural Networks(ANN)

Support Vector Machines(SVM)

Other Techniques (KNN, Naïve Bayes, PCA)

Introduction to Text Mining using NLTK

Introduction to Time Series Forecasting (Decomposition & ARIMA)

Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)

Fine tuning the models using Hyper parameters, grid search, piping etc.

06 Project – Consolidate Learnings / Introduction To Big Data

Project – Consolidate Learnings

Applying different algorithms to solve the business problems and bench mark the results

Introduction To Big Data

Introduction and Relevance

Uses of Big Data analytics in various industries like Telecom, E- commerce, Finance and Insurance etc.

Problems with Traditional Large-Scale Systems

07 Hadoop(Big Data) Eco-System / Hadoop Cluster-Architecture-Configuration Files

Hadoop(Big Data) Eco-System

Motivation for Hadoop

Different types of projects by Apache

Role of projects in the Hadoop Ecosystem

Key technology foundations required for Big Data

Limitations and Solutions of existing Data Analytics Architecture

Comparison of traditional data management systems with Big Data management systems

Evaluate key framework requirements for Big Data analytics

Hadoop Ecosystem & Hadoop 2.x core components

Explain the relevance of real-time data

Explain how to use Big Data and real-time data as a Business planning tool

Hadoop Cluster-Architecture-Configuration Files

Hadoop Master-Slave Architecture

The Hadoop Distributed File System – Concept of data storage

Explain different types of cluster setups(Fully distributed/Pseudo etc)

Hadoop cluster set up – Installation

Hadoop 2.x Cluster Architecture

A Typical enterprise cluster – Hadoop Cluster Modes

Understanding cluster management tools like Cloudera manager/Apache ambari

08 Spark GraphX / Introduction To Machine Learning Using Spark

Spark GraphX

Overview of GraphX module in spark

Creating graphs with GraphX

Introduction To Machine Learning Using Spark

Understand Machine learning framework

Implement some of the ML algorithms using Spark MLLib

09 Project / Outline for this course

Project

Consolidate all the learnings

Working on a Big Data Project by integrating various key components

Outline for this course

What is Data Science?

Why Python for data science?

Relevance in industry and the need of the hour

How are leading companies harnessing the power of Data Science with Python?

Different phases of a typical Analytics/Data Science project and the role of Python

Anaconda vs. Python

10 Spark: Introduction / Spark: Spark In Practice

Spark: Introduction

Introduction to Apache Spark

Streaming Data Vs. In Memory Data

Map Reduce Vs. Spark

Modes of Spark

Spark Installation Demo

Overview of Spark on a cluster

Spark Standalone Cluster

Spark: Spark In Practice

Invoking Spark Shell

Creating the Spark Context

Loading a File in Shell

Performing Some Basic Operations on Files in Spark Shell

Caching Overview

Distributed Persistence

Spark Streaming Overview(Example: Streaming Word Count)

Details coming soon.

12 Spark: Spark Meets Hive / Spark Streaming

Spark: Spark Meets Hive

Analyze Hive and Spark SQL Architecture

Analyze Spark SQL

Context in Spark SQL

Implement a sample example for Spark SQL

Integrating hive and Spark SQL

Support for JSON and Parquet File Formats Implement Data Visualization in Spark

Loading of Data

Hive Queries through Spark

Performance Tuning Tips in Spark

Shared Variables: Broadcast Variables & Accumulators

Spark Streaming

Extract and analyze the data from twitter using Spark streaming

Comparison of Spark and Storm – Overview

13 Data Analysis Using Impala / Introduction To Other Ecosystem Tools

Data Analysis Using Impala

Impala & Architecture

How Impala executes Queries and its importance

Hive vs. PIG vs. Impala

Extending Impala with User Defined functions

Introduction To Other Ecosystem Tools

NoSQL database – Hbase

Introduction Oozie

14 Data Analysis Using Pig / Data Analysis Using Hive

Data Analysis Using Pig

Introduction to Data Analysis Tools

Apache PIG – MapReduce Vs Pig, Pig Use Cases

PIG’s Data Model

PIG Streaming

Pig Latin Program & Execution

Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF

Writing JAVA UDF’s

Embedded PIG in JAVA

PIG Macros

Parameter Substitution

Use Pig to automate the design and implementation of MapReduce applications

Use Pig to apply structure to unstructured Big Data

Data Analysis Using Hive

Apache Hive – Hive Vs. PIG – Hive Use Cases

Discuss the Hive data storage principle

Explain the File formats and Records formats supported by the Hive environment

Perform operations with data in Hive

Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts

Hive Script, Hive UDF

Hive Persistence formats

Loading data in Hive – Methods

Serialization & Deserialization

Handling Text data using Hive

Integrating external BI tools with Hadoop Hive

Tools & Technologies

Every tool listed here is installed, configured and used in a hands-on lab session.

Core Tools

Hands-On Labs

Practical Environment

Industry-Standard Tools

Real-World Setup

Guided Exercises

Skill Building

Sample Datasets

Practice Material

Practice & Projects

Mini Projects

Applied Practice

Assignments

Mentor Reviewed

Doubt Sessions

Live Support

Career Readiness

Resume Building

Career Support

Mock Interviews

Interview Prep

Certification Prep

Global Recognition

Deployment & Delivery

Production Practices

Real-World Ready

Best Practices

Industry Standards

159+

Hands-On Lessons

Core Modules

10 weeks

Training Duration

100%

Practical Training

You don't just learn ADVANCED BIG DATA SCIENCE TRAINING. You ship it.

Three major projects, each mirroring how production teams actually work — from guided foundations to a portfolio-ready capstone.

PROJECT // 01

Guided Foundation Project

→Requirement Analysis

→Guided Implementation

→Mentor Review

→Iteration

Foundation Beginner

Apply the fundamentals in a structured, mentor-reviewed project

Take the core concepts from the first half of the curriculum and apply them to a realistic scenario, with guidance and feedback from your mentor at every step.

Structured project brief

Step-by-step implementation

Mentor feedback and review

Documented outcome

Stack Core Concepts Best Practices

PROJECT // 02

Applied Practice Project

→Scenario Design

→Independent Build

→Testing & Validation

→Peer Review

Applied Intermediate

Build a more independent project mirroring real production scenarios

Work through a project that combines multiple concepts from the curriculum, closer to how work is actually structured on the job — less hand-holding, more ownership.

End-to-end implementation

Testing and validation

Documentation

Peer/mentor review

Stack Applied Skills Testing

PROJECT // 03

Capstone Project

→Planning

→End-to-End Build

→Review & Refinement

→Presentation

Capstone Advanced

Take a project from requirements to a polished, portfolio-ready deliverable

Your final project — plan, build, test and present a complete solution using everything covered in the curriculum, reviewed by mentors before you graduate.

Complete working solution

Presentation-ready documentation

Mentor sign-off

Portfolio-ready deliverable

Stack Full Curriculum Portfolio

All 3 projects go directly into your portfolio & resume — reviewed by mentors before you graduate.

See Sample Project Reports

Upcoming Batches

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Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation support on your system
24/7 Email and Phone support
100% Placement Assistance
Global Certification Preparation
Trainer-Student Interactive Portal
Assignments and Projects by Mentors

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Live Classroom Training

Weekend / Weekdays / Morning / Evening batches
80:20 Practical and Theory ratio
Real-life Case Studies
Easy make-up for missed sessions
PSI | Kryterion | Redhat Test Centers
Lifetime Video Classroom Access (coming soon)
Resume Prep and Mock Interviews

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Self-Paced Training

Learn 300+ courses at your own time
50,000+ Satisfied Learners
Course Completion Certificate
Practical Labs available
Mentor Support available
Doubt Clearing Session available
10% Discounted Global Certification

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Global Certification

Radical Technologies is the leading IT certification institute in Pune, offering globally recognized certifications across various domains. With expert trainers and comprehensive materials, we ensure students gain in-depth knowledge and hands-on experience to excel in their careers. Our certification programs are tailored to meet industry standards — from cloud technologies to data science — empowering individuals to stay ahead in the ever-evolving tech landscape.

Career Services

At Radical Technologies, we are committed to your success beyond the classroom. Our 100% Job Assistance program ensures that you are not only equipped with industry-relevant skills but also guided through the job placement process. With personalised resume building, interview preparation, and access to our extensive network of hiring partners, we help you take the next step confidently into your IT career.