Data Science and Analytics specialization course in Jaipur

(Batches Start from April, May, June 2025)

About The Program

Looking for the best Data Science courses with placement guarantee in Jaipur? At Regex Software, we offer industry-focused Data Science specialization courses in Jaipur designed to help you master AI, Machine Learning, Big Data, and Analytics. Our Data Science training institute in Jaipur provides hands-on training with real-world projects, expert mentorship, and 100% placement assistance. Whether you’re a fresher, IT professional, or career switcher, our Data Science and Analytics courses in Jaipur equip you with the skills to land a high-paying job in top companies. Enroll now and start your journey with the best Data Science coaching centre in Jaipur!

At Regex Software, we take pride in being a leading Data Science training institute in Jaipur, offering top-notch education and career support. With increasing demand for data scientists, this field offers some of the most lucrative career opportunities. Join the best Data Science course in Jaipur with Regex Software and take the first step towards a successful career. Limited seats available – enroll today!

April Batches Dates

Batch 1: 07th April 2025
Batch 2: 14th  April 2025
 Batch 3: 21st  April 2025
Batch 4: 28th April 2025

May Batches Dates

Batch 1: 05th May 2025
Batch 2: 12th May 2025
Batch 3: 19th May 2025
Batch 4: 26th May 2025

June Batches Dates

Batch 1: 02nd June 2025
Batch 2: 09th June 2025
Batch 3: 16th June 2025
Batch 4: 23rd June 2025
Batch 5: 30th June 2025

Weekly Duration

20 Hours Per week

Location

Physical (Jaipur)
or 
Online (Google Meet)

Duration

10 Months + 6 Months Extra Support

Participants

30 per Batch

What you will Learn

Python

Machine Learning

Deep Learning

Database

Power BI

Apache Spark

Ansible

AWS

Generative Ai

Study Material

  • E-Notes
  • Assignments & Poll test
  • Live Video Lectures
  • Access of Recordings & Study Material
  • Mentorship Support
  • Work on multiple Minor Projects & Use Cases
  • Work on Live Projects

Output

  • Able to think out of the box
  • Become expert in multiple technology domains like: Python, Machine Learning, Deep Learning, Map Reduce, Apache Spark,  Generative Ai, Kafka, and AWS(for deployment)
  • Understand working of ML models deployment with AWS
  • Build projects on multiple technology domains
  • Work on more than 25 Use CASES & Projects
  • Learn to deploy your models on AWS Sagemaker or Google cloud Platform

Why Choose Us

Live Sessions

Live Sessions by Expertise Trainers and Access of Recorded Session is also available.

Live Projects
Get a chance to work on Industry Oriented Projects to implement your learning.
24*7 Support
24*7 Mentorship Support available for all Students to clear all of your doubts.
Opportunities
REGex provides Internship / Job opportunities to the best Students in different Companies.

Placement Opportunities in Companies

You can get Internship/Training Opportunities to get placed in HP, DELL, Honeywell, Rightpoint, Frontdoor, Fractal and many more according to your performance.

Package Offered So Far

5 LPA
8 – 10 LPA
32 LPA

Placement Process

  • At REGex Software, we are committed to providing a structured and results-driven training approach to ensure your career success.
    • 🔹 Training & Performance Analysis:
      • Your training will begin from day 1 of your joining, focusing on hands-on learning and practical implementation.
      • Our team will analyze your performance based on assignments, projects and weekly assessments from the second week onwards and we will provide weekly feedback to help you improve.

      🔹 Mandatory Criteria for Placement Opportunities:
      To be eligible for placement opportunities, you must meet the following criteria:
      80% attendance in live training sessions.
      80% completion and timely submission of assignments & projects.
      80% attendance in assessments, including tests, mock interviews, HR interviews and group discussions.

      🔹 Resume Preparation & Placement Process:

      • Between 6.5 to 7 months, our team will provide guidance on resume building and evaluate your resumes accordingly.
      • After completing 75-80% of the program, you will receive details about the placement opportunities based on your performance and company requirements.
      • Placement opportunities will be provided continuously via email, calls and WhatsApp groups, depending on your performance.

      🔹 Placement Assurance & Refund Policy:

      • This is a Placement Assured Program, with an additional 6-month post-program assistance.
      • If you successfully complete the program but do not secure a placement within 6 months, you will receive a 100% fee refund with 9% interest.

      🔹 Our Commitment to Your Success:

      At REGex Software, Placement Assurance = Skills + Opportunities
      .
      We equip you with industry-relevant skills and provide continuous job opportunities based on your performance. However, it is the student’s responsibility to crack interviews and enhance their skills based on feedback.
      For additional support, we offer the flexibility to rejoin previous batches to reinforce concepts and improve understanding.

       

      We are dedicated to your career success! 🚀

Placed Students//Partnership

What People Tell About Us

Placed Students

Course Content

Python

  • What is Python?
  • History and evolution of Python.
  • Python’s popularity and use cases.
  • Setting up Python (installation).
  • Running Python scripts and using the interactive shell.
  • Variables and data types (integers, floats, strings, booleans).
  • Comments in Python.
  • Basic arithmetic operations.
  • String manipulation.
  • Variables and naming conventions.
  • Conditional statements (if, elif, else).
  • Looping structures (for and while loops).
  • Iterating through sequences (lists, strings, dictionaries).
  • Range and enumeration.
  • Using break and continue.
  • Lists: creation, manipulation, and methods.
  • Tuples: creation, immutability, and uses.
  • Dictionaries: key-value pairs and operations.
  • Sets: unique elements and set operations.
  • List comprehensions.
  • Defining functions.
  • Function parameters and return values.
  • Scope and lifetime of variables.
  • Importing modules.
  • Creating and using custom modules.
  • Reading from and writing to files.
  • Working with different file modes (read, write, append).
  • Using context managers with files.
  • Classes and objects.
  • Attributes and methods.
  • Inheritance and encapsulation.
  • Polymorphism and method overriding.
  • Constructors and destructors.
  • Handling errors with try and except.
  • Raising exceptions.
  • Handling multiple exceptions.
  • Using the ‘finally’ block.
  • List comprehensions.
  • Lambda functions.
  • Decorators.
  • Generators and iterators.
  • Working with external libraries and APIs.
  • PEP 8 style guide.
  • Code readability and maintainability.
  • Writing docstrings.
  • Version control (e.g., Git).
  • Building simple applications.
  • Real-world examples.
  • Solving coding challenges.
  • Final project.
  • Popular Python libraries and frameworks (e.g., NumPy, pandas, Django).
  • Python for web development, data science, and machine learning.
  • Virtual environments and package management.
  • Real-world case studies.
  • Job opportunities and career paths.
  • Contributing to open-source projects.
  • Staying up to date with Python developments.

Machine Learning

  • Machine learning applications
  • ML vs DL
  • Basics of Python [Syntax]
  • Working with Pandas, Numpy & Matplotlib
    ■ Working with Missing Data
    ■ Data Grouping
    ■ Data Subsetting
    ■ Merging & Joining Data Frames
  • Importing Libraries & Datasets
  • Munging & handling missing data
  • Splitting the dataset into Training set & Test set
  • What is actual machine learning
  • Various Aspects of Data – type, Variables & Category
  • Machine Learning & its Various types
  • About Supervised & Unsupervised Learning
  • Understanding Simple Linear Regression
  • Understanding the P -Value
  • Support Vector Regression
  • K nearest neighbours
  • Logistic regression
  • Naive Bayes
  • Decision trees
  • Random forests
  • Bagging Boosting
  • Maximum likelihood classifier
  • Support vector machines
  • Principal Component analysis (PCA – Theory)

  • PCA with Case-Study

  • Linear Discriminant Analysis(LDA) for Dimension Reduction

  • Feature Selection to Select the Most Relevant Predictors

  • Principal Component analysis (PCA – Theory)

  • PCA with Case-Study

  • Linear Discriminant Analysis(LDA) for Dimension Reduction

  • Feature Selection to Select the Most Relevant Predictors

  • Confusion matrix

  • Accuracy Paradox

  • CAP Curve

  • K-Mean Clustering Intuition

  • K-Mean selecting Numbers of Cluster

  • About Reinforcement Learning

Deep Learning

  • NLP Intuition

  • Types of NLP

  • Classification vs Deep Learning Models

  • The Neuron

  • Activation function

  • How Neural network work & learn by itself

  • Gradient Descent

  • Stochastic Gradient Descent

  • Ethics of Deep Learning

  • What are convolutional neural networks [CNN] ?

  • CNN Architecture

  • CNN Code preparation

  • Recurrent Neural Networks

  • Several layers

    ■ ReLLu Operation

    ■ Pooling

    ■ Flattering

    ■ Full Connection

  • Statistics

  • Sample Selection

  • Probability Theory

  • Hypothesis

  • Model Relationship

  • Model Fit

  • Descriptive Statistics

  • Types of Data

  • Qualitative Data

  • Histograms

  • Different Plots

  • Centrality and Spread

  • Outliers

  • Median, Mean, Mode

Computer Vision

  • What is computer vision & its application

  • Face Detection

    ■ Adding more features & Categorization

    ■ Object Detection & Image creation

    ■ Working with Images & vectors

  • Facial Expression Recognition in Code (Binary / Sigmoid /Logistic Regression)

  • Object detection
  • Neural style transfer
  • YOLO
  • RCNNs
  • Resnet 50
  • Tensorboard
  • What are Vectors
  • Working with word Analogy
  • Text Classification
  • Pre Trained word vectors from word2vec
  • Language Models
  • Introduction to Tensorflow / Keras
  • Most used right now
  • Resources gathering
  • Keras dealing with Missing Data
  • Dealing with Categorical data
  • Introduction to GenAI
  • Types of GenAI Models
  • Generative Adversarial Networks (GANs)
  • How to use AI in Business
  • How Chatgpt & Google Gemini works
  • Prompt Engineering Fundamentals

Database

  • Functional Dependency
  • Closure of Attributes
  • Types of Keys: PrimaryKey CandidateKey & Super Key in DBMS
  • Normalization
  • Indexing
  • Transaction and Concurrency Control
  • Transaction in DBMS
  • ACID Propertise in DBMS
  • Joins in DBMS
  • Create & Alter Table
  • Constraints in SQL
  • Sql Queries & Sub Queries
  • SQL Stored Procedure
  • View, Cursor & Trigger in SQL
  • Common Table Expession
  • Replace Null and Coalesce Function
  • Running Total In SQL

Power BI

  • Introduction
  • Meet Microsoft Power BI Desktop
  • Interface & Workflow
  • Helpful Power BI Resources
  • New Power BI Ribbon
    • Introduction
    • Types of Data Connectors in Power BI Desktop
    • The Power BI Query Editor
    • Demo: Basic Table Transformations in Power BI
    • Power BI Demo:
  • Working with Text Tools
  • Numerical Values
  • Date & Time Tools
  • Creating a Rolling Calendar
  • Grouping & Aggregating Records
  • Pivoting & Unpivoting Data
    • Merging Queries in Power BI Desktop
    • Appending Queries in Power BI Desktop
    • Configuring Power BI Data Source Settings
    • Configuring Power BI Query Refresh Settings
    • Additional Data Types & Categories in Power BI
    • Defining Hierarchies in Power BI Desktop
  • Importing Models from Excel to Power BI
    • Introduction
    • What is a “Data Model”?
    • Principles of Database Normalization
    • Understanding Data Tables vs. Lookup Tables
    • Understanding Table Relationships vs. Merged Tables
    • Creating Table Relationships in Power BI Desktop
  • Snowflake Schemas in Power BI
  • Managing & Editing Table Relationships in Power BI Desktop
  • Managing Active vs. Inactive Relationships
  • Understanding Relationship Cardinality
  • Connecting Multiple Data Tables in Power BI Desktop
  • Understanding Filter Flow
  • Two-Way Filters in Power BI Desktop (USE WITH CAUTION!)
  • Hiding Fields from the Power BI Report View
  • New Power BI Desktop “Model” View
  • Introduction
  • Meet Data Analysis Expressions (DAX)
  • Intro to DAX Calculated Columns
  • Intro to DAX Measures
  • Adding Columns & DAX Measures in Power BI Desktop
  • Implicit vs. Explicit DAX Measures
  • Filter Context Examples in Power BI
  • Understanding DAX Syntax & Operators
  • Common DAX Function Categories
  • DAX Demo: 
    • Basic Date & Time Functions
    • Conditional & Logical Functions (IF/AND/OR)
    • Common Text Functions
    • Joining Data with RELATED
    • Basic Math & Stats Functions
    • COUNT Functions (COUNTA, DISTINCTCOUNT, COUNTROWS)
    • CALCULATE
    • CALCULATE & ALL
    • CALCULATE & FILTER
    • Iterator Functions (SUMX, RANKX)
    • Time Intelligence Formulas
  • FILTER Function
  • LOGICAL Functions
  • MATHEMATICAL Functions
  • STATISTICAL Functions
  • TEXT Functions
  • TIME INTELLEGENT Functions
  • Other Functions
  • Power Query Editor
  • Power BI Aggregation & Template
    • Introduction
    • Exploring the “Report” View in Power BI Desktop
    • Adding Simple Objects to the Power BI Report Canvas
    • Inserting Basic Charts & Visuals in Power BI
    • Conditional Formatting
    • Power BI Report Formatting Options
    • Power BI Report Filtering Options
    • Power BI Demo:
      • Exploring Data with Matrix Visuals
      • Filtering with Date Slicers
  • Showing Key metrics with Cards & KPI Visuals
    • Inserting Text Cards
    • Visualizing Geospatial Data with Maps
    • Visualizing Data with Treemaps
    • Showing Trends with Line & Area Charts
    • Adding Trend Lines & Forecasts 
    • Goal pacing with Gauge Chart
    • Adding Drillthrough Filters
  • Editing Power BI Report Interactions
  • Managing & Viewing Roles in Power BI Desktop

Big Data Tools

● Data Warehouse
● History of Data Warehousing
● Need for Data Warehouse
● Data Warehouse Architecture
● Data Mining Works with DWH
● Features of Data warehouse
● Data Mart
● Application Areas

● Dimension modeling
● Fact and Dimension tables
● Database schema
● Schema Design for Modeling
● Star, SnowFlake
● Fact Constellation schema
● Use of Data mining
● Data mining and Business Intelligence
● Types of data used in Data mining
● Data mining applications
● Data mining products

● What’s Big Data? ● Big Data: 3V’s ● Explosion of Data ● What’s driving Big Data ● Applications for Big Data Analytics ● Big Data Use Cases ● Benefits of Big Data
● What is distributed computing ● Introduction to Map Reduce ● Map Reduce components ● How MapReduce works ● Word Count execution ● Suitable & unsuitable use cases for MapReduce
● Architecture ● Basic Syntax ● Import data from a table in a relational database into HDFS ● import the results of a query from a relational database into HDFS ● Import a table from a relational database into a new or existing Hive table ● Insert or update data from HDFS into a table in a relational database
● Define a Hive-managed table ● Define a Hive external table ● Define a partitioned Hive table ● Define a bucketed Hive table ● Define a Hive table from a select query ● Define a Hive table that uses the ORCFile format ● Create a new ORCFile table from the data in an existing non-ORCFile Hive table ● Specify the delimiter of a Hive table ● Load data into a Hive table from a local directory ● Load data into a Hive table from an HDFS directory ● Load data into a Hive table as the result of a query ● Load a compressed data file into a Hive table ● Update a row in a Hive table ● Delete a row from a Hive table ● Insert a new row into a Hive table ● Join two Hive tables ● Use a subquery within a Hive query
● An overview of functional programming ● Why Scala? ● REPL ● Working with functions ● objects and inheritance ● Working with lists and collections ● Abstract classes
● What is Spark? ● History of Spark ● Spark Architecture ● Spark Shell Working with RDDs in Spark ● RDD Basics ● Creating RDDs in Spark ● RDD Operations ● Passing Functions to Spark ● Transformations and Actions in Spark ● Spark RDD Persistence Working with Key/Value Pairs ● Pair RDDs ● Transformations on Pair RDDs ● Actions Available on Pair RDDs ● Data Partitioning (Advanced) ● Loading and Saving the Data Spark Advanced ● Accumulators ● Broadcast Variables ● Piping to External Programs ● Numeric RDD Operations ● Spark Runtime Architecture ● Deploying Applications
● Spark SQL Overview ● Spark SQL Architecture ● What is Spark streaming? ● Spark Streaming example
● What are dataframe ● Manipulating Dataframes ● Reading new data from different file format ● Group By & Aggregations functions

● Introduction of HBase
● Comparison with traditional database
● HBase Data Model (Logical and Physical models)
● Hbase Architecture
● Regions and Region Servers
● Partitions
● Compaction (Major and Minor)
● Shell Commands
● HBase using APIs

● Pre-requisites
● Introduction
● Architecture

Talend Data Integration

● Installation and Configuration
● Repository
● Projects
● Metadata Connection
● Context Parameters
● Jobs / Joblets
● Components
● Important components
● Aggregation & working with Input & output data

DevOps

  • Configuration Management (Ansible)
    • What & Why ?
    • Ad-hoc Commands
    • Playbooks
    • Facts
    • Handlers

AWS (For Deployment)

  • Create your own ML models
  • Deploy ML models
  • AWS Recognition
  • ML & DL Projects
    • Titanic Classification
    • Carbon Emissions
    • Car price analysis
    • Drug Prediction
    • Hand written Digit Recognition
    • CIFAR 10
    • CIFAR 100
    • Cats vs dogs
    • Intel scene Classification
    • Transfer Learning
    • Object Detection
  • Power BI Projects & Use Cases
    • Importing Models from Excel to Power BI
    • Snowflake Schemas in Power BI
    • Showing Key metrics with Cards & KPI Visuals
  • BigData Projects
    • Zomato Analysis
    • Cricket & Football Data Analysis
    • Flipkart Product Data Analytics

 Note: Content may Subject to Change by REGex as per Requirement

Extra Sessions

Additinal Session on GIT, Linux, Docker, AWS Basics, Jenkins and many more for all students.

Fee Structure

Indian Fee (Physical)

Price: ₹60,000/- 

Indian Fee (Online)

Price: ₹60,000/- 

International Fee

Price: $1500 

Fee can be paid as No Cost EMI @5000/month

Cashback Policy

  • You will get your Unique Referral Code after successful paid registration.
  • You will get ₹2500 Cashback directly in your account for each paid registration from your Unique Referral Code on monthly basis(After Closing Registrations of this program) .
  • For Example:- If we received 10 paid registration from your Unique Referral Code then you will receive ₹2500*10 = ₹25,000 on monthly basis.
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(Batches Start from April, May, June 2025)

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*Extra off is applicable on 1 time payment only. Seats can be filled or Price can be increased at any time. Refund policy is not available*