Combo Program

★★★★★

(2,088 ratings) 50,000+ Student

DATA SCIENCE COMBO Training in Pune

A Data Science combo typically refers to the combination of skills and knowledge from various domains that are essential for a successful career in data science. In summary, a Data Science combo encompasses a diverse set of skills and knowledge areas, including statistics, programming, machine learning, domain expertise, and communication skills.

Radical Technologies

50,000+ English 53 Modules · 17 Courses

Online / Classroom

DATA SCIENCE COMBO

ORACLE SQL + Oracle 12c SQL : SQL Basic + SQL Advanced + PYTHON – CORE + Project Work – PYTHON : DATA ANALYSIS + Introduction to Data Science with Python + Python Essentials + Accessing/Importing And Exporting Data Using Python Modules + Data Manipulation – Cleansing – Munging using python modules + Introduction to Statistics + Introduction to Predictive Modelling + Segmentation : Solving Segmentation Problems + Linear Regression : Solving Regression Problems + Logistic Regression : Solving Classification Problems + Time Series Forecasting : Solving Forecasting Problems + Machine Learning : Predictive Modelling + Text Mining And Analytics

Courses Included 17 Courses Combined

Batch Type Weekdays / Weekends

Mode of Training Classroom / Online / Corporate

Locations Pune, Bangalore, Kochi

Language English

Certification Globally Recognized

Tools you'll master

Python Pandas NumPy SQL Oracle Data Analysis Jupyter Machine Learning Excel Data Warehousing

Next batch: Contact us ·

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100% placement assistance

What you'll learn

Introduction to Oracle DB 12c

Retrieving Data using SQL SELECT statement

Using Set Operator

Managing Tables using DML Statements

Introduction

Variables & Data Types

Getting started with Python Libraries

NumPy Arrays

Data Analysis – Visualization Using Python

Data Exploration For Modelling

Programme Overview

53 modules across ORACLE SQL, Oracle 12c SQL : SQL Basic, SQL Advanced, PYTHON – CORE, Project Work – PYTHON : DATA ANALYSIS, Introduction to Data Science with Python, Python Essentials, Accessing/Importing And Exporting Data Using Python Modules, Data Manipulation – Cleansing – Munging using python modules, Introduction to Statistics, Introduction to Predictive Modelling, Segmentation : Solving Segmentation Problems, Linear Regression : Solving Regression Problems, Logistic Regression : Solving Classification Problems, Time Series Forecasting : Solving Forecasting Problems, Machine Learning : Predictive Modelling, Text Mining And Analytics — a single, progressive learning arc from foundational concepts to real-world projects.

Courses Combined

Total Modules

424

Total Topics

4.8★

Average Rating

50K+

Students Trained

ORACLE SQL

Master the core concepts and hands-on skills of ORACLE SQL — from fundamentals to real-world application.

SQL Oracle

Oracle 12c SQL : SQL Basic

Master the core concepts and hands-on skills of Oracle 12c SQL : SQL Basic — from fundamentals to real-world application.

SQL Oracle

SQL Advanced

Master the core concepts and hands-on skills of SQL Advanced — from fundamentals to real-world application.

Using Set Operator Managing Tables using DML Statements Introduction to Data Definition Language

PYTHON – CORE

Master the core concepts and hands-on skills of PYTHON – CORE — from fundamentals to real-world application.

Python Jupyter

Project Work – PYTHON : DATA ANALYSIS

Master the core concepts and hands-on skills of Project Work – PYTHON : DATA ANALYSIS — from fundamentals to real-world application.

Pandas Python NumPy

Introduction to Data Science with Python

Master the core concepts and hands-on skills of Introduction to Data Science with Python — from fundamentals to real-world application.

Data Warehousing

Who is this programme for?

Whether you're starting from scratch, know some SQL, or are already writing code — this programme takes you from database fundamentals through Python programming to real-world data analysis.

Students & Freshers

Build a strong foundation in DATA SCIENCE COMBO and gain practical, job-ready experience from day one.

Working Professionals

Upskill and combine complementary skills from multiple courses in one structured programme.

Career Switchers

Move into a new technical role with a single, structured path covering everything you need.

IT Professionals

Deepen your expertise and broaden your skill set with hands-on, practical training.

Course Curriculum

17 courses • 53 modules • 424 topics

ORACLE SQL

Courses Combined

100% Practical · Unlimited Doubt Sessions

For whom SQL is? :

IT or NON IT people can easily enter into leading Database Technology which is commonly used for all domain application software as back-end. There are so many reason to go ahead :

Oracle SQL is basic learning platform to get in IT

which is easy to learn.

On completion Oracle SQL training you can attempt certification exam.

Certified candidate can take benefit as IT document and can get good opportunity in IT world.

Oracle SQL is easy for any support project for initial stage which get into IT carrier. In case you want to grow for development environment than it’s entry for programming.

Now a days SQL is basic skill set for any IT/NON IT folks

It’s always helpful for any functional

support and technical consultant.

Oracle 12c SQL : SQL Basic

Courses Combined

100% Practical · Unlimited Doubt Sessions

Oracle Database 12c: Focus Areas

Fusion Middle-ware

Oracle Cloud

Services

Deployment Methods

RDBMS

Data Model

ER Diagram

Relation DB Terminology

SQL Statements and Development Environment

HR Schema Tables

DB Documentation.

Basic Select Statement

Arithmetic Expression

Defining Null values

Concatenation Operator & Literal

Duplicate Rows

Displaying table structure

Hands-On Practice

› Hands-on: Practice Overview

Use of Where clause

Character Strings & Date Data

Comparison

Logical

Range

Pattern

& Other operators

Rules Precedence

Order By Clause

Substitution variables. Practice Overview .

SQL function

Single Row Function

Character/Case Functions

Number Functions

Nesting Function

Date Functions etc

Hands-On Practice

› Hands-on: Practice Overview

Conversion Function

Implicit/Explicit Data Conversion

General Function with covering NVL

NVL2

NULLIF

COALESCE

Conditional Expression with Decode function and Case Expression

Hands-On Practice

› Hands-on: Practice Overview

Usages of Group function

Nesting Group Function

Creating Group Data

Restricting group data using Having Clause

Hands-On Practice

› Hands-on: Practice Overview

Type of Joins

Explaining with Natural Join

Using Clause

On Clause

SQL 99 Syntax

Three-Way Join

Self Join

NonEqui Join

Inner Versus Outer Join

Cross Join etc. Practice overview.

Scenario to use Sub-query

Rules for Sub-queries

Type of Sub-queries

Single Row Sub-queries and Multi Row Sub-queries. Null value in Sub-query etc

Hands-On Practice

› Hands-on: Practice Overview

SQL Advanced

Courses Combined

100% Practical · Unlimited Doubt Sessions

Set Operator Rules

Covering Set Operator as UNION

UNION ALL

INTERSECT

MINUS etc

Hands-On Practice

› Hands-on: Practice Overview

Data Manipulation Language

Covering INSERT

UPDATE

DELETE & DB Transaction control using COMMIT

ROLLBACK

SAVEPOINT. Use of For update clause in SELECT Statement.

Database Object

Naming Rules

Various Data Types

CREATE TABLE statement

Constraint guidelines

Defining constraints as NOT NULL

UNIQUE

PRIMARY KEY

FOREIGN KEY & CHECK etc. ALTER TABLE

DROP TABLE Statement. Create Table using Sub-query

Hands-On Practice

› Hands-on: Practice Overview

Data Dictionary Structure

How to use Data Dictionary

USER_OBJECTS

ALL_OBJECT

USER_CONSTRAININTS. TABLE Information

Column Information etc

Hands-On Practice

› Hands-on: Practice overview

Importance of Sequence

Synonyms & Indexes. Defining Sequence

Synonyms & Indexes & DROP statement etc

Hands-On Practice

› Hands-on: Practice overview

What’s View? Advantage of Views

CREATE Simple & Complex views

Rules for performing DML operation on views. Modifying & Removing a view etc

Hands-On Practice

› Hands-on: Practice Overview

Multiple Column Sub-query

Column Comparison

Pair & Non Pair Sub-queries

Scalar Sub queries

Co-related Sub-queries

Use of WITH clause

Hands-On Practice

› Hands-on: Practice Overview

PYTHON – CORE

Courses Combined

100% Practical · Unlimited Doubt Sessions

What is Python..?

A Brief history of Python

Why Should I learn Python..?

Installing Python

How to execute Python program

Write your first program

Overview on Jupyter Notebook

Overview on Command Line

Variables

Numbers

String

Boolean

List

Set

Tuple

Dictionary

if…statement

if…else statement

elif…statement

The while…Loop

The for….Loop

continue statement

break statement

pass statement

Define function

Calling a function

Function arguments

Built-in functions

Modules

How to import a module…?

Built-in Module – OS

Built-in Module – sys

Built-in Module – argparse

Built-in Module – statistics

Built-in Module – math

Packages

How to create packages

Introduction about classes & objects

Creating a class & object

Inheritance

Methods Overriding

Data hiding

Writing data to a file

Reading data from a file

Read and Write data from csv file

try…except

try…except…else

finally

Project Work – PYTHON : DATA ANALYSIS

Courses Combined

100% Practical · Unlimited Doubt Sessions

what is data analysis ?

why python for data analysis ?

Essential Python Libraries

Installation and setup

Ipython

2.7 VS 3.5

Creating multidimensional array

NumPy-Data types

Array attributes

Indexing and Slicing

Creating array views and copies

Manipulating array shapes

I/O with NumPy

Installing pandas

Pandas dataframes

Pandas Series

Data aggregation with Pandas DataFrames

Concatenating and appending DataFrames

Joining DataFrames

Handling missing data

Writing CSV files with numpy and pandas

HDF5 format

Reading and Writing to Excel with pandas

JSON data

Parsing HTML with Beautiful Soup

PyTables

What are regular expressions?

The match Function

The search Function

Matching vs searching

Search and Replace

Extended Regular Expressions

Wildcard

Install the cx_Oracle and other Packages

Create Database Connection

CREATE

INSERT

READ

UPDATE and DELETE Operation

DML and DDL Oepration with Databases

Performing Transactions

Handling Database Errors

Disconnecting Database

Random password generator

Mini

CLI based scientific calculator

Mini

Instagram bot

Mini

Expense Tracker

Mini

Site connectivity checker

Mini

Lawn Tennis Match Highlight (Can be extended to any sport)

Major

NLP library

Major

Introduction to Data Science with Python

Courses Combined

100% Practical · Unlimited Doubt Sessions

What is analytics & Data Science?

Common Terms in Analytics

Analytics vs. Data warehousing

OLAP

MIS Reporting

Relevance in industry and need of the hour

Types of problems and business objectives in various industries

How leading companies are harnessing the power of analytics?

Critical success drivers

Overview of analytics tools & their popularity

Analytics Methodology & problem solving framework

List of steps in Analytics projects

Identify the most appropriate solution design for the given problem statement

Project plan for Analytics project & key milestones based on effort estimates

Build Resource plan for analytics project

Python Essentials

Courses Combined

100% Practical · Unlimited Doubt Sessions

Why Python for data science?

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

pandas

scikit-learn

stat models

nltk

Accessing/Importing And Exporting Data Using Python Modules

Courses Combined

100% Practical · Unlimited Doubt Sessions

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

Database Input (Connecting to database)

Viewing Data objects – subsetting Data

methods

Exporting Data to various formats

Important python modules : Pandas

beautiful soup

Data Manipulation – Cleansing – Munging using python modules

Courses Combined

100% Practical · Unlimited Doubt Sessions

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.)

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.)

Introduction to Statistics

Courses Combined

100% Practical · Unlimited Doubt Sessions

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)

Analysis of variance

Correlations and Chi-square

Important modules for statistical methods : NumPy

SciPy

Pandas

Introduction to Predictive Modelling

Courses Combined

100% Practical · Unlimited Doubt Sessions

Concept of model in analytics and how it is used?

Common terminology used in analytics & Modelling process

Popular modelling algorithms

Types of Business problems – Mapping of Techniques

Different Phases of Predictive Modelling

Need for structured exploratory data

EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

Identify missing data

Identify outliers data

Visualize the data trends and patterns

Need of Data preparation

Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction

Variable Reduction Techniques – Factor & PCA Analysis

Segmentation : Solving Segmentation Problems

Courses Combined

100% Practical · Unlimited Doubt Sessions

Introduction to Segmentation

Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)

Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)

Behavioural Segmentation Techniques (K-Means Cluster Analysis)

Cluster evaluation and profiling – Identify cluster characteristics

Interpretation of results – Implementation on new data

Linear Regression : Solving Regression Problems

Courses Combined

100% Practical · Unlimited Doubt Sessions

Introduction – Applications

Assumptions of Linear Regression

Building Linear Regression Model

Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)

Assess the overall effectiveness of the model

Validation of Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis

Error distribution (histogram), Model equation, drivers etc.)

Interpretation of Results – Business Validation – Implementation on new data

Logistic Regression : Solving Classification Problems

Courses Combined

100% Practical · Unlimited Doubt Sessions

Introduction – Applications

Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models

Building Logistic Regression Model (Binary Logistic Model)

Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)

Validation of Logistic Regression Models (Re running Vs. Scoring)

Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)

Interpretation of Results – Business Validation – Implementation on new data

Time Series Forecasting : Solving Forecasting Problems

Courses Combined

100% Practical · Unlimited Doubt Sessions

Introduction – Applications

Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition

Classification of Techniques (Pattern based – Pattern less)

Basic Techniques – Averages

Smoothening

etc

Advanced Techniques – AR Models

ARIMA

etc

Understanding Forecasting Accuracy – MAPE

MAD

MSE

etc

Machine Learning : Predictive Modelling

Courses Combined

100% Practical · Unlimited Doubt Sessions

Introduction to Machine Learning & Predictive Modelling

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 Modelling (Data Pre-processing, Sampling, Model Building, Validation)

Overfitting (Bias-Variance Trade off) & Performance Metrics

Feature engineering & dimension reduction

Concept of optimization & cost function

Overview of gradient descent algorithm

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

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

What is segmentation & Role of ML in Segmentation?

Concept of Distance and related math background

K-Means Clustering

Expectation Maximization

Hierarchical Clustering

Spectral Clustering (DBSCAN)

Principle component Analysis (PCA)

Decision Trees – Introduction – Applications

Types of Decision Tree Algorithms

Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain

Gini Index

Chi Square

Regression Trees

Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness

Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules

Decision Trees – Validation

Overfitting – Best Practices to avoid

Concept of Ensembling

Manual Ensembling Vs. Automated Ensembling

Methods of Ensembling (Stacking, Mixture of Experts)

Bagging (Logic, Practical Applications)

Random forest (Logic, Practical Applications)

Boosting (Logic, Practical Applications)

Ada Boost

Gradient Boosting Machines (GBM)

XGBoost

Motivation for Neural Networks and Its Applications

Perceptron and Single Layer Neural Network

and Hand Calculations

Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques

Neural Networks for Regression

Neural Networks for Classification

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating ANN models

Motivation for Support Vector Machine & Applications

Support Vector Regression

Support vector classifier (Linear & Non-Linear)

Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)

Interpretation of Outputs and Fine tune the models with hyper parameters

Validating SVM models

What is KNN & Applications?

KNN for missing treatment

KNN For solving regression problems

KNN for solving classification problems

Validating KNN model

Model fine tuning with hyper parameters

Concept of Conditional Probability

Bayes Theorem and Its Applications

Naïve Bayes for classification

Applications of Naïve Bayes in Classifications

Text Mining And Analytics

Courses Combined

100% Practical · Unlimited Doubt Sessions

Taming big text

Unstructured vs. Semi-structured Data; Fundamentals of information retrieval

Properties of words; Creating Term-Document (TxD); Matrices; Similarity measures

Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)

Finding patterns in text: text mining

text as a graph

Natural Language processing (NLP)

Text Analytics – Sentiment Analysis using Python

Text Analytics – Word cloud analysis using Python

Text Analytics – Segmentation using K-Means/Hierarchical Clustering

Text Analytics – Classification (Spam/Not spam)

Applications of Social Media Analytics

Metrics(Measures Actions) in social media analytics

Examples & Actionable Insights using Social Media Analytics

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

Fine tuning the models using Hyper parameters

grid search

piping etc.

Tools & Technologies

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

ORACLE SQL

SQL

Covered in ORACLE SQL

Oracle

Covered in ORACLE SQL

Oracle 12c SQL : SQL Basic

SQL

Covered in Oracle 12c SQL : SQL Basic

Oracle

Covered in Oracle 12c SQL : SQL Basic

SQL Advanced

Covered in SQL Advanced

PYTHON – CORE

Python

Covered in PYTHON – CORE

Jupyter

Covered in PYTHON – CORE

Project Work – PYTHON : DATA ANALYSIS

Pandas

Covered in Project Work – PYTHON : DATA ANALYSIS

Python

Covered in Project Work – PYTHON : DATA ANALYSIS

NumPy

Covered in Project Work – PYTHON : DATA ANALYSIS

Data Analysis

Covered in Project Work – PYTHON : DATA ANALYSIS

Introduction to Data Science with Python

Data Warehousing

Covered in Introduction to Data Science with Python

Python Essentials

Python

Covered in Python Essentials

Jupyter

Covered in Python Essentials

Pandas

Covered in Python Essentials

NumPy

Covered in Python Essentials

Accessing/Importing And Exporting Data Using Python Modules

Python

Covered in Accessing/Importing And Exporting Data Using Python Modules

Pandas

Covered in Accessing/Importing And Exporting Data Using Python Modules

Excel

Covered in Accessing/Importing And Exporting Data Using Python Modules

Data Manipulation – Cleansing – Munging using python modules

Python

Covered in Data Manipulation – Cleansing – Munging using python modules

Data Analysis

Covered in Data Manipulation – Cleansing – Munging using python modules

Pandas

Covered in Data Manipulation – Cleansing – Munging using python modules

NumPy

Covered in Data Manipulation – Cleansing – Munging using python modules

Introduction to Statistics

Pandas

Covered in Introduction to Statistics

NumPy

Covered in Introduction to Statistics

Introduction to Predictive Modelling

Covered in Introduction to Predictive Modelling

Segmentation : Solving Segmentation Problems

Covered in Segmentation : Solving Segmentation Problems

Linear Regression : Solving Regression Problems

Covered in Linear Regression : Solving Regression Problems

Logistic Regression : Solving Classification Problems

Covered in Logistic Regression : Solving Classification Problems

Time Series Forecasting : Solving Forecasting Problems

Covered in Time Series Forecasting : Solving Forecasting Problems

Machine Learning : Predictive Modelling

Machine Learning

Covered in Machine Learning : Predictive Modelling

Bootstrap

Covered in Machine Learning : Predictive Modelling

Text Mining And Analytics

Python

Covered in Text Mining And Analytics

Machine Learning

Covered in Text Mining And Analytics

10+

Tools & Libraries

424+

Hands-On Topics

Total Modules

Courses Combined

You don't just learn DATA SCIENCE COMBO. You apply it.

A combined capstone project across all 17 courses — the same way you'd apply these skills on the job.

PROJECT // 01

DATA SCIENCE COMBO Capstone Project

→ORACLE SQL

→Oracle 12c SQL : SQL Basic

→SQL Advanced

→PYTHON – CORE

→Project Work – PYTHON : DATA ANALYSIS

→Introduction to Data Science with Python

→Python Essentials

→Accessing/Importing And Exporting Data Using Python Modules

→Data Manipulation – Cleansing – Munging using python modules

→Introduction to Statistics

→Introduction to Predictive Modelling

→Segmentation : Solving Segmentation Problems

→Linear Regression : Solving Regression Problems

→Logistic Regression : Solving Classification Problems

→Time Series Forecasting : Solving Forecasting Problems

→Machine Learning : Predictive Modelling

→Text Mining And Analytics

Capstone Project Combined

Apply everything you learn across ORACLE SQL, Oracle 12c SQL : SQL Basic, SQL Advanced, PYTHON – CORE, Project Work – PYTHON : DATA ANALYSIS and 12 more to one real-world project

Bring together every course in this programme into a single, end-to-end project — the same way you would apply these combined skills on the job.

A working project combining all course modules

Added directly to your project portfolio

Reviewed by mentors before you graduate

Stack Python Pandas NumPy SQL Oracle Data Analysis Jupyter Machine Learning Excel Data Warehousing

Hands-on Practice Areas

Hands-on Practice: ORACLE SQL Hands-on Practice: Oracle 12c SQL : SQL Basic Hands-on Practice: SQL Advanced Hands-on Practice: PYTHON – CORE Hands-on Practice: Project Work – PYTHON : DATA ANALYSIS Hands-on Practice: Introduction to Data Science with Python Hands-on Practice: Python Essentials Hands-on Practice: Accessing/Importing And Exporting Data Using Python Modules Hands-on Practice: Data Manipulation – Cleansing – Munging using python modules Hands-on Practice: Introduction to Statistics Hands-on Practice: Introduction to Predictive Modelling Hands-on Practice: Segmentation : Solving Segmentation Problems Hands-on Practice: Linear Regression : Solving Regression Problems Hands-on Practice: Logistic Regression : Solving Classification Problems Hands-on Practice: Time Series Forecasting : Solving Forecasting Problems Hands-on Practice: Machine Learning : Predictive Modelling Hands-on Practice: Text Mining And Analytics

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

See Sample Project Reports

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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.