Combo Program

★★★★★

(2,088 ratings) 50,000+ Student

AI & DATA SCIENCE COMBO Training in Pune

An AI (Artificial Intelligence) and Data Science combo is a highly synergistic skill set that empowers individuals to excel in extracting valuable insights, building intelligent systems, and making data-driven decisions. the AI and Data Science combo leverages the power of AI technologies to enhance data analysis and decision-making.

Radical Technologies

50,000+ English 57 Modules · 6 Courses

Online / Classroom

AI & DATA SCIENCE COMBO

PYTHON – CORE + Project Work – PYTHON : DATA ANALYSIS + Introduction to Data Science with Python + Introduction to Statistics + Introduction with Artificial Intelligence + Projects

Courses Included 6 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 Machine Learning Data Analysis Jupyter Excel Data Warehousing Deep Learning Bootstrap

Next batch: Contact us ·

Call Now

100% placement assistance

What you'll learn

Introduction

Variables & Data Types

Getting started with Python Libraries

NumPy Arrays

Python Essentials

Accessing/Importing And Exporting Data Using Python Modules

Introduction to Predictive Modelling

Data Exploration For Modelling

ML and other related terms to AI

A working example of AI and ML

Programme Overview

57 modules across PYTHON – CORE, Project Work – PYTHON : DATA ANALYSIS, Introduction to Data Science with Python, Introduction to Statistics, Introduction with Artificial Intelligence, Projects — a single, progressive learning arc from foundational concepts to real-world projects.

Courses Combined

Total Modules

392

Total Topics

4.8★

Average Rating

50K+

Students Trained

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.

Python Pandas NumPy

Introduction to Statistics

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

Machine Learning Python Bootstrap

Introduction with Artificial Intelligence

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

ML and other related terms to AI A working example of AI and ML

Projects

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

Python Machine Learning NumPy

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 AI & 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

6 courses • 57 modules • 392 topics

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

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

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

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

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

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

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

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

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

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

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.

Introduction with Artificial Intelligence

Courses Combined

100% Practical · Unlimited Doubt Sessions

What is AI (Artificial Intelligence) ?

What types of intelligences we are talking about?

Different definitions and Ultimate goal of AI.

What are application areas for AI?

History of AI and some real life examples of AI.

What is ML and How it is related with AI?

What is NLP and How it is related with AI?

What is DL and How it is related with ML and AI?

What are ANNs and DNNs and How are they related to AI?

Projects

Courses Combined

100% Practical · Unlimited Doubt Sessions

These simple tasks are to make you understand how AI and ML can find their applications in real life.

What are Libraries

packages and Modules?

What are top Python libraries for ML in Python?

Why choosing Anaconda development environment?

Setting up Anaconda development environment on Windows 10 PC.

Verifying proper installation of Anaconda environment.

What is a classifier in ML?

Important elements and flow of any ML projects.

Let’s develop our first ML program – explanations

Let’s develop our first ML program – development

These simple tasks are going to give you some great experience with Machine Learning introductory programs or better say

“Hello world” programs of Machine Learning.

What all ML techniques are there?

Evaluation methods of all ML techniques.

Developing complete ML project – understanding data set

Developing complete ML project – understanding flow of project

Developing complete ML project – visualizing data set through Python

Developing complete ML project – development

Developing complete ML project – concepts explanations

Developing another project of ML (Digit recognition project)

After completing these project

you have done and understood multiple complete projects of Machine Learning.

Installation

CPU Software Requirements

CPU Installation of PyTorch

PyTorch with GPU on AWS

PyTorch with GPU on Linux

PyTorch with GPU on MacOSX

Matrix Basics

Seed for Reproducibility

Torch to NumPy Bridge

NumPy to Torch Bridge

GPU and CPU Toggling

Basic Mathematical Tensor Operations

Summary of Matrices

Variables

Gradients

Summary of Variables and Gradients

Linear Regression Introduction

Linear Regression in PyTorch

Linear Regression From CPU to GPU in PyTorch

Summary of Linear Regression

Logistic Regression Introduction

Linear Regression Problems

Logistic Regression In-depth

Logistic Regression with PyTorch

Logistic Regression From CPU to GPU in PyTorch

Summary of Logistic Regression

Logistic Regression Transition to Feedforward Neural Network

Non-linearity

Feedforward Neural Network in PyTorch

More Feedforward Neural Network Models in PyTorch

Feedforward Neural Network From CPU to GPU in PyTorch

Summary of Feedforward Neural Network

Feedforward Neural Network Transition to CNN

One Convolutional Layer

Input Depth of 1

One Convolutional Layer

Input Depth of 3

One Convolutional Layer Summary

Multiple Convolutional Layers Overview

Pooling Layers

Padding for Convolutional Layers

Output Size Calculation

CNN in PyTorch

More CNN Models in PyTorch

CNN Models Summary

Expanding Model’s Capacity

CNN From CPU to GPU in PyTorch

Summary of CNN

Introduction to RNN

RNN in PyTorch

More RNN Models in PyTorch

RNN From CPU to GPU in PyTorch

Summary of RNN

Introduction to LSTMs

LSTM Equations

LSTM in PyTorch

More LSTM Models in PyTorch

LSTM From CPU to GPU in PyTorch

Summary of LSTM

Tools & Technologies

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

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

Python

Covered in Introduction to Data Science with Python

Pandas

Covered in Introduction to Data Science with Python

NumPy

Covered in Introduction to Data Science with Python

Data Analysis

Covered in Introduction to Data Science with Python

Introduction to Statistics

Machine Learning

Covered in Introduction to Statistics

Python

Covered in Introduction to Statistics

Bootstrap

Covered in Introduction to Statistics

Pandas

Covered in Introduction to Statistics

Introduction with Artificial Intelligence

Covered in Introduction with Artificial Intelligence

Projects

Python

Covered in Projects

Machine Learning

Covered in Projects

NumPy

Covered in Projects

Deep Learning

Covered in Projects

10+

Tools & Libraries

392+

Hands-On Topics

Total Modules

Courses Combined

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

A combined capstone project across all 6 courses — the same way you'd apply these skills on the job.

PROJECT // 01

AI & DATA SCIENCE COMBO Capstone Project

→PYTHON – CORE

→Project Work – PYTHON : DATA ANALYSIS

→Introduction to Data Science with Python

→Introduction to Statistics

→Introduction with Artificial Intelligence

→Projects

Capstone Project Combined

Apply everything you learn across PYTHON – CORE, Project Work – PYTHON : DATA ANALYSIS, Introduction to Data Science with Python, Introduction to Statistics, Introduction with Artificial Intelligence and 1 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 Machine Learning Data Analysis Jupyter Excel Data Warehousing Deep Learning Bootstrap

Hands-on Practice Areas

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: Introduction to Statistics Hands-on Practice: Introduction with Artificial Intelligence Hands-on Practice: Projects

All 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.

Career Support

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Came from any other Institute?

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Radical Learning Eco-System

Exam Simulator

Cloud SandBox

Hands-on Cloud Lab

Developer Coding Ground

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Combo Course In Other Cities

Bangalore

Kochi

Online Batches Available For These Areas

Online / Classroom

AI & DATA SCIENCE COMBO

PYTHON – CORE + Project Work – PYTHON : DATA ANALYSIS + Introduction to Data Science with Python + Introduction to Statistics + Introduction with Artificial Intelligence + Projects

Courses Included 6 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 Machine Learning Data Analysis Jupyter Excel Data Warehousing Deep Learning Bootstrap

Next batch: Contact us ·

Call Now

100% placement assistance