Advance Data Engineering With Analytics Industrial Internship Program

(Batches Start from 6th January 2025)

About The Program

With the belief to build a healthy ecosystem as per the Industry Standards REGex Software brings a 6 Months Industrial Internship Program on “Data Engineering With Analytics “. We organize Industrial Internship Program for improving the knowledge and skills of the Students, so that they can become expert in the field of Data Engineering With Analytics and get their Dream Job in Software Development Field in Big MNCs.

REGex Software Services’s Data Engineering With Analytics program is a valuable resource for both beginners and experts. This specialization program will introduce you to the domain of Data Engineering include Hadoop, Map-Reduce, HIVE, Apache Spark, Kafka Streaming Python, SQL, Data Analytics  Amazon EMR and much more starting from Basics to Advance. If you want to become Data Engineer / Business Analyst, REGex introduce this program for you.  

Weekly Duration

20 Hours Per week

Location

Physical (Jaipur)
or 
Online (Google Meet)

Duration

6 Months 

Participants

30 per Batch

What you will Learn

Python

SQL

Data Engineering

Power BI

Hadoop

Hive

Spark

Apache Kafka

nosql

No SQL

Amazon Emr

What you will Learn

  • Linux basics
  • Big Data Analytics & Hadoop
  • HDFS [ Hadoop Distributed File System ]
  • Map-Reduce [ Data Processing ]
  • HIVE
  • Apache Spark on Azure DataBricks
  • NoSQL DataBase
  • Data visualization
  • Power BI
  • SQL
  • Power Query & Editor
  • Dashboard & Graph
  • Amazon EMR
  • Learn how to use these tools in the field of Data Analytics

Study Material

  • E-Notes.
  • Poll Test & Assignments .
  • Accessing lecture videos and notes.
  • 24*7 Mentorship Support
  • Engaging in real-time project assignments

Output

  • Help you in Data Analytics Domain
  • Able to think out of the box
  • Expertise in different Big Data Tools like HDFS, Hive, Apache Spark, Amazon EMR
  • Become expert in multiple technology domains like: Python, SQL, Power BI etc.
  • Able to solve many Interview Questions of Top MNCs
  • Understand creating Data Insights by connecting data sets, transform & clean the data into data models and then create chars/graphs to provide visuals of the data
  • Build Charts/Graphs/Insights of data sets
  • Work on multiple data sets
  • Become Power BI Analyst / Power BI Developer after completion of this program
  • Able to get package  in Big MNCs upto 30 LPA

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

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.

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
  • 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
  • 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
  • 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
  • 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

Functions

  • FILTER Function
  • LOGICAL Functions
  • MATHEMATICAL Functions
  • STATISTICAL Functions
  • TEXT Functions
  • TIME INTELLEGENT Functions
  • Other Functions
  • Power Query Editor
  • Power BI Aggregation & Template
  • 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

● History of Hadoop
● Distributed File System
● What is Hadoop
● Characteristics of Hadoop
● RDBMS Vs Hadoop
● Hadoop Generations
● Components of Hadoop
● HDFS Blocks and Replication
● How Files Are Stored
● HDFS Commands
● Hadoop Daemons

● Difference between Hadoop 1.0 and 2.0
● New Components in Hadoop 2.x
● YARN/MRv2
● Configuration Files in Hadoop 2.x
● Major Hadoop Distributors/Vendors
● Cluster Management & Monitoring
● Hadoop Downloads

● What is distributed computing
● Introduction to Map Reduce
● Map Reduce components
● How MapReduce works
● Word Count execution
● Suitable & unsuitable use cases for MapReduce

● 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

● What is Spark?
● History of Spark
● Spark Architecture
● Spark Shell

● RDD Basics
● Creating RDDs in Spark
● RDD Operations
● Passing Functions to Spark
● Transformations and Actions in Spark
● Spark RDD Persistence

● Pair RDDs
● Transformations on Pair RDDs
● Actions Available on Pair RDDs
● Data Partitioning (Advanced)
● Loading and Saving the Data

● Accumulators
● Broadcast Variables
● Piping to External Programs
● Numeric RDD Operations
● Spark Runtime Architecture
● Deploying Applications

  •  Spark SQL Overview
  • Spark SQL Architecture

Data Frame

  • What are dataframe
  • Manipulating Dataframes
  • Reading new data from different file format
  • Group By & Aggregations functions
  •  What is Spark streaming?

  • Spark Streaming example

  • Understand the fundamentals of Kafka.
  • Understand the distributed nature of Kafka and its scalability.
  • Understand how data is organized into topics and partitions.
  • Install and set up Kafka on your local machine or a cluster.
  • Learn how to create topics, produce messages, and consume messages using
    Kafka APIs.
  • Overview of Amazon EMR and its features.
  • Setting up and configuring Amazon EMR clusters.
  • Running big data processing jobs on EMR.
  • Integrating Amazon EMR with other AWS services.
  • Monitoring and optimizing EMR clusters.
  • Security considerations for EMR.

● 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

NO SQL

  • Introduction to NoSQL databases and their characteristics.
  • Types of NoSQL databases: Document-oriented, Key-Value, Column-Family, Graph.
  • Use cases for NoSQL databases.
  • MongoDB: A popular document-oriented NoSQL database.
  • Redis: A widely used key-value NoSQL database.
  • Cassandra: A column-family NoSQL database.
  • Understand the fundamentals of ETL (Extract, Transform, Load) processes.
  • Learn how to install and configure Taland.
  • Explore Taland’s interface and understand its key components.
  • Practice using Taland to extract data from different sources, perform
    transformations, and load it into target systems.

● Pre-requisites
● Introduction
● Architecture

● Pseudo Live Project (PLP) program is primarily to handhold participants who are fresh into the technology. In PLP, more importance given to “Process Adherence”
● The following SDLC activities are carried out during PLP
o Requirement Analysis
o Design ( High Level Design and Low Level Design)
o Design of UTP(Unit Test Plan) with test cases
o Coding
o Code Review
o Testing
o Deployment
o Configuration Management
o Final Presentation

 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: ₹40,000/- (Flat 50% off) => ₹20,000/- 

Indian Fee (Online)

Price: ₹40,000/- (Flat 50% off) => ₹20,000/- 

 

Fee can be paid in NO COST EMI

Cashback Policy

  • You will get your Unique Referral Code after successful paid registration.
  • You will get ₹1000 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 ₹1000*10 = ₹10,000 on monthly basis.
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(Batches Start from 6th January 2025)

*It will help us to reach more
*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*