CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN LUDHIANA

Live Virtual

Instructor Led Live Online

110,000
62,423

  • IABAC® & NASSCOM® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
35,773

  • IABAC® & NASSCOM® Certification
  • One year access to Self Learning
  • 10 Capstone Projects
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Classroom

In - Person Classroom Training

110,000
67,548

  • IABAC® & NASSCOM® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Classroom Training
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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UPCOMING DATA ENGINEER ONLINE CLASSES IN LUDHIANA

BEST CERTIFIED DATA ENGINEER CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN LUDHIANA

MODULE 1: DATA ENGINEERING INTRODUCTION

• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools and platforms
• Core concepts of Data engineering

MODULE 2: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Managing Big data

MODULE 3: DATA INTEGRITY AND PRIVACY

• Data integrity basics
• Various aspects of data privacy
• Various data privacy frameworks and standards
• Industry related norms in data integrity and privacy: data engineering perspective

MODULE 4: DATA ENGINEERING ROLE

• Who is a data engineer?
• Various roles of data engineer
• Skills required for data engineering
• Data Engineer Collaboration with Data Scientist and other roles.

 

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• String functions part 
• String functions part 
• Python Operators

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement, IF-ELSE
• NESTED IF
• Python Loops Basics, WHILE Statement
• BREAK and CONTINUE statements
• FOR statements

MODULE 3: PYTHON PACKAGES

• Introduction to Packages in Python
• Datetime Package and Methods

MODULE 4: PYTHON DATA STRUCTURES

• Basic Data Structures in Python
• Basics of List
• List methods
• Tuple: Object and methods
• Sets: Object and methods
• Dictionary: Object and methods

MODULE 5: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

MODULE 1 : OVERVIEW OF STATISTICS 

• Introduction to Statistics: Descriptive And Inferential Statistics
• a.Descriptive Statistics
• b.Inferential Statistis
• Basic Terms Of Statistics
• Types Of Data

MODULE 2 : HARNESSING DATA 

• Random Sampling 
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling 
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multistage Sampling 
• Sampling Error
• Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies, Measure of Spread
• Data Distribution Plot: Histogram
• Normal Distribution
• Z Value / Standard Value
• Empherical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance and Correlation

MODULE 4 : HYPOTHESIS TESTING 

• Hypothesis Testing Introduction 
• Types of Hypothesis
• P- Value, Crtical Region
• Types of Hypothesis Testing: Parametric, Non-Parametric
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis Testing (Proposed)

MODULE 1: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• Data Lake house
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart —Know the Difference
• Data Lake Introduction architecture
• Data Warehouse vs Data Lake

MODULE 2: DATA PROCESSING

• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas

MODULE 3: DOCKER AND KUBERNETES FOUNDATION

• Docker Introduction
• Docker Vs.VM
• Hands-on: Running our first container
• Common commands (Running, editing,stopping,copying and managing images)YAML(Basics)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers 
• Docker swarm vs kubernetes

MODULE 4: DATA ORCHESTRATION WITH APACHE AIRFLOW

• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow

MODULE 5: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines

MODULE 1 : AWS DATA SERVICES INTRODUCTION 

• AWS Overview and Account Setup
• AWS IAM Users, Roles and Policies
• AWS S overview
• AWS EC overview
• AWS Lamdba overview
• AWS Glue overview
• AWS Kinesis overview
• AWS Dynamodb overview
• AWS Athena overview
• AWS Redshift overview

MODULE 2 : DATA PIPELINE WITH GLUE

• AWS Glue Crawler and setup
• ETL with AWS Glue
• Data Ingesting with AWS Glue

MODULE 3 : DATA PIPELINE WITH AWS KINESIS 

• AWS Kinesis overview and setup
• Data Streams with AWS Kinesis
• Data Ingesting from AWS S using AWS Kinesis

MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT 

• AWS Redshift Overview
• Analyze data using AWS Redshift from warehouses, data lakes and operations DBs
• Develop Applications using AWS Redshift cluster
• AWS Redshift federated Queries and Spectrum

MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE 

• Azure Synapse setup
• Understanding Data control flow with ADF
• Data Pipelines with Azure Synapse
• Prepare and transform data with Azure Synapse Analytics

MODULE 6 : STORAGE IN AZURE 

• Create Azure storage account
• Connect App to Azure Storage
• Azure Blob Storage

MODULE 7: AZURE DATA FACTORY

• Azure Data Factory Introduction
• Data transformation with Data Factory
• Data Wrangling with Data Factory

MODULE 8 : AZURE DATABRICKS

• Azure databricks introduction
• Azure databricks architecture
• Data Transformation with databricks

MODULE 9 : AZURE RDS

• Creating a Relational Database
• Querying in and out of Relational Database
• ETL from RDS to databricks

MODULE 10 : AZURE RDS

• Hands-on Project Case-study
• Setup Project Development Env
• Organization of Data Sources
• AZURE/AWS services for Data Ingestion
• Data Extraction Transformation

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers

MODULE 1 : DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2 : SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3 : DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4 : DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5 : SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6 : SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Key Terms: Output Format
• Partitioners Combiners Shuffle and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Resilient distributed datasets (RDD),Working with RDDs in PySpark, Spark Context , Aggregating Data with Pair RDDs
• Spark Databricks
• Spark Streaming

MODULE 1: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 2: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

MODULE 3: KAFKA and Spark

• Creating an HDFS cluster with containers
• Creating pyspark cluster with containers
• Processing data on hdfs cluster with pyspark cluster

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

MODULE 2: POWER-BI Basics

• Power BI Introduction 
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 3: DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 4: CONNECTING TO VARIOUS SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

 

 

 

 

 

 

 

 

 

 

 

 

 

DATA ENGINEER TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ENGINEER TRAINING IN LUDHIANA

The DataMites® Data Engineer Course is formulated in a manner to run through all the needfuls of data engineering with Python, Statistics, Database essentials, Big Data, Data Wrangling, Numpy, Pandas, and other related topics. With the growing demand and usage of data per day, the call for skilled data engineers is also skyrocketing.

The Data Engineer Training Course is designed for two months of the real-time project along with the internship facility to provide practical experience and real-world expertise. There are no major prerequisites for the Data Engineer course as the course covers topics from square one.

A data engineer course is incredibly valuable for individuals interested in pursuing a career in data engineering. This specialized training equips students with the essential skills and knowledge to efficiently handle large volumes of data, build robust data pipelines, optimize data storage and retrieval, and ensure data quality. With a focus on industry-standard tools and technologies, the course provides practical experience that prepares students for real-world challenges in the field. Overall, a data engineer course is highly beneficial for those seeking to excel in the data engineering profession.

The content is accredited by the International Association of Business Analytics Association (IABAC®), NASSCOM Future Skills Certification from NASSCOM, and JAINx from Jain University.

Data Engineer Course Curriculum

  1. Data Engineering Introduction
  2. Python Programming Foundation
  3. Database (RDBMS) Foundation 
  4. Statistics for Data Engineering
  5. Introduction to Big Data
  6. Big Data - Hadoop
  7. Data Manipulation - Python Numpy & Pandas
  8. Data Cleaning and Transformation
  9. Data Visualization
  10. AWS Data Services
  11. PySpark Introduction
  12. Database (RDBMS - SQL & PL/SQL)

DataMites offers live instructor-led training corresponding to in-person classroom learning in many time zones. The training can be done on weekends as well as weekdays.

  1. Classroom Training
  2. Online Live Virtual Training
  3. Self Learning

When it comes to data, data engineering is a crucial discipline, yet few individuals can effectively articulate what data engineers perform. Small and large organizations alike rely on data to run their operations. Data is used by businesses to respond to pertinent questions ranging from customer interest to product feasibility. Without a question, data is critical to growing your company and getting useful insights. As a result, data engineering is equally vital.

“Analysis of data will not by itself produce new ideas.”

      -  Edward De Bono 

What Are the Perks of Attending Data Engineer Training?

  1. Data science is built on the foundation of data engineering.
  2. Data Engineering is a technically challenging topic of study.
  3. It's quite rewarding - data engineers aren't solely motivated by a desire to make data scientists' jobs easier. Data engineers, without a doubt, are having a growing impact on society.
  4. If you wish to work in the field of data science, you'll need this talent.
  5. A fulfilling career with an upper level of job security.

Because of the increased importance, duties, and technologies available, it can be difficult to describe the data engineer position today, and many firms have different expectations for their technical expertise. 

A Data Engineer's job description is one of the most in-demand in the business. They are highly recognized by businesses in all areas and are compensated well for their contributions.

The DataMites® Data Engineering Course is an excellent data engineering resource. Develop the skills you'll need to enter into this rapidly growing area or brush up on what you already know about data warehousing and ETLs, data storage, and data consumption from a variety of sources.

The cost of a Data Engineer Training in the United States can range from 567.01 USD to 201.61 USD, depending on the course level and type of training you choose. In Europe, the cost of a Data Engineer Training course can range from 4187.38 Euro to  526.98 Euro. Depending on the course level and type of training you choose, the cost of Data Engineer training in India can range from INR  15,645 to 44,000 INR.

The phase learning process is followed where the phases are as follows:

Phase 1 = In this phase candidates are provided with the industry's best study materials including self-study materials and video classes to help get a ground on the domain. 

Phase 2 = In this phase candidates will have Data Engineer Courses Online that will be imparted by expert trainers with domain knowledge and experience. The IABAC Data Engineer Certification will be issued to the candidates as well.

Phase 3 = The third phase comprises the practical part of the Projects, Internships, and Job ready Program.

Data Engineer Course Learning Benefits:

  1. Know what Data Engineering is and what it includes.
  2. Apprehend the Data Engineering Ecosystem and Lifecycle.
  3. Pick up on extracting data from a wide range of files and databases.
  4. Comprehend on how to use a variety of skills and tactics to clean, edit, and enrich your data.
  5. In relational and NoSQL databases, you'll learn how to work with a variety of file kinds.
  6. Discover how to develop dashboards and set up a data pipeline to track progress.
  7. Learn how to scale data pipelines in a production setting.

We have entered the 'data age,' when data skills are in great demand, thanks to the proliferation of new-generation IT enterprises throughout the world. Today's businesses recognize that the true value of data can only be realized with a solid data infrastructure and design, and they are willing to invest in it. 

Almost every company is developing and investing in cutting-edge technological goods as a result of the renewed focus on customer experience. Data engineering has become even more appealing as a result of this. To meet the need, IT organizations are rushing for competent labor, and data engineering has risen in scale and visibility.

Ludhiana, a major industrial city in Punjab, is located on the banks of the Sutlej River. Ludhiana, called "India's Manchester," is a major manufacturing centre in the country. According to research and markets, the global big data and data engineering services market is expected to increase at a CAGR of 17.6 percent from USD 34.47 billion in 2018 to USD 77.37 billion by 2023.

A data engineer in India earns an average amount of INR 10,00,000 per year! (Glassdoor) The average salary for a Data Engineer in Ludhiana is 6,24,076 LPA. (Indeed.com)

Even if the employment market and pay are both tempting, data show that Data Engineer is the fastest-growing vocation in the technology industry, and you can get proceeded with our Data Engineering Certification Course in Ludhiana!

Along with the data engineer courses, DataMites also provides artificial intelligence, tableau, data analytics, deep learning, python, r programming, data science training, and machine learning courses in Ludhiana.

ABOUT DATA ENGINEER COURSE IN LUDHIANA

Large-scale data gathering, storage, and analysis systems are created through a process called data engineering. There are uses for it in practically every business, and it is a broad field.

The first and most crucial stage in the process of becoming a data engineer is to receive the necessary training. Finding work in the industry requires taking a certification course to gain a complete understanding of the data science and data engineering domain and to upskill one's talents.

You can become a data engineer by enrolling in one of the three- to twelve-month-long Data Engineer Courses in Ludhiana. The targeted degree or certification, on the other hand, determines the course content. You can gain valuable Data Engineer experience and open up internship opportunities through 3-month courses, which will help you land entry-level jobs at reputable companies.

If you want to work in the industry, the Data Engineer Course in Ludhiana is the one you should enroll in because it accredits you as a data science specialist. You'll have the abilities required to be a successful data engineer after completing our in-depth curriculum, as well as a portfolio that's ready for a job interview.

To enter this area, one must hold a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a closely related field. You will require practical experience, such as an internship, to even be considered for the majority of entry-level positions.

Depending on the level of instruction and the training program you select, the Data Engineer Training Fee in Ludhiana might be anywhere between 20,000 INR and 80,000 INR.

For thorough instruction in programs in data engineering, data science, artificial intelligence, and other related topics, DataMites® is the ideal institution. To create and provide a comprehensive artisan training program, DataMites® works with eminent data engineering experts.

An entry-level position isn't always available in data engineering. Rather, a lot of data engineers start off as software engineers or business intelligence analysts. You might take on administrative responsibilities as your career develops, or you might work as a machine learning engineer, data architect, or solutions architect.

Data analysis, coding, data warehousing, database management, critical thinking, and an understanding of machine learning are just a few of the fundamental skills needed for a data engineer.

  • The national average salary for a Data Engineer is USD 1,12,493 per year in the United States. (Glassdoor)
  • The national average salary for a Data Engineer is £41043 per annum in the UK.  (Glassdoor)
  • The national average salary for a Data Engineer is INR 9,80,000 per year in India. (Glassdoor)
  • The national average salary for a Data Engineer is CAD 81,870 per year in Canada. (Payscale)
  • The national average salary for a Data Engineer is AUD 98,646 per year in Australia. (Payscale)
  • The national average salary for a Data Engineer is 63,515 EUR per annum in Germany. (Glassdoor)
  • The national average salary for a Data Engineer is CHF 129,009 per year in Switzerland. (Glassdoor)
  • The national average salary for a Data Engineer is AED 171,553 per year in UAE. (Payscale)
  • The national average salary for a Data Engineer is SAR 180,000 per year in Saudi Arabia. (Payscale.com)
  • The national average salary for a Data Engineer is ZAR 453,460 per year in South Africa. (Payscale.com)

Data scientists study the data to identify patterns, generate business insights, and provide appropriate answers to queries for the organization, whereas data engineers design and manage the systems and structures that store, retrieve, and organize data.

Python for Data Engineering includes data wrangling activities like reshaping, aggregating, and linking many sources, small-scale ETL, API interaction, and automation. There are several reasons why Python is popular. Its accessibility is one of the main advantages.

Overall, working as a data engineer is a great career choice for those who value attention to detail, following engineering specifications, and building pipelines that turn raw data into insightful information. A career in data engineering offers significant earning potential as well as work stability.

Being a data engineer is a physically demanding yet financially rewarding occupation. Realizing the full potential of data in every organization requires the contribution of a data engineer. It is one of the professions with the strongest global growth rates, with over 88.3 percent more job posts in 2019 and over 50% more open opportunities year over year.

Before submitting an application for a full-time data engineer job, it's a good idea to start with an internship. Internships are essential for getting experience and increasing practical knowledge before landing a full-time job in data engineering because this field requires practice. People who have never worked before are more likely to be offered internships by companies. Once your internship is over, it will be much simpler for you to land an entry-level job with the company.

Furthermore, it has a significant place in the hierarchy of prerequisites for data science because analysts and scientists cannot access or operate with data without the infrastructure created by data engineers. And as a result, businesses run the danger of losing access to one of their most valuable assets. According to the Dice 2020 Tech Career Report, data engineering will have the biggest growth in employment in 2019, with a 50% increase in open positions.

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FAQ’S OF DATA ENGINEER COURSE IN LUDHIANA

Data engineers' difficult task is to strike a balance between immediate needs and a longer-term perspective of where data demands will take the systems they supervise. With every new architecture you create, there is a persistent worry that you will encounter a technological brick wall. Without a doubt, data is essential for developing your company and learning new information. Even if it is difficult to learn, a data engineering course might be useful for gaining the necessary domain knowledge.

A data engineer in India earns an average amount of INR 10,00,000 per year! (Glassdoor) The average salary for a Data Engineer in Ludhiana is 6,24,076 LPA. (Indeed.com)

According to a poll conducted by DICE, an online platform that manages one of the largest databases of technology specialists, Data Engineer will have a growth rate of over 50% year over year by the year 2020, making it the fastest-growing career in technology. A recent survey revealed that there has been a significant increase in demand for jobs in data engineering. To design scalable solutions, you'll use your programming knowledge and analytical abilities.

There is a tonne of opportunity for development in the field of data engineering, both in terms of knowledge and ability as well as compensation. For a thorough training program for your future job, applicants can enroll in the DataMites Online Data Engineer Course in Ludhiana.

The three-month Data Engineer Course in Ludhiana includes 120 hours of instruction. On weekdays and weekends, training sessions are held. Depending on your availability, you can pick any.

The DataMites® Data Engineer Courses in Ludhiana have been thoughtfully designed to educate data engineering from inception. Everyone is now able to enroll in the course. This career path is intended for persons looking for a change in career, data professionals looking to advance their skill set, and college students looking for employment.

Having prior knowledge of mathematics, statistics, economics, or computer science can be very helpful, but a PG degree is not required.

  • The International Association of Business Analytics Certification (IABAC), NASSCOM, and Jain University have all granted accreditation to DataMites®, a global training center for data engineers.
  • The courses we provide are being taken by more than 50,000 students.
  • The learning process is broken down into three steps. The candidates will receive self-study books and videos during Phase 1 to assist them in gaining a sufficient understanding of the curriculum. The second and most important stage of intensive live online instruction is phase 2. Furthermore, we will share the projects and placements during the third phase.
  • Real-world projects and extremely useful case studies are included throughout the entire training.
  • Your IABAC, NASSCOM Future Skills, and JAINx Certifications will be awarded to you after the program.
  • You will have the opportunity to participate in an internship with the worldwide technology business Rubixe, an AI company, after completing your training.

The price of the online data engineering course in Ludhiana is 42,000 Indian Rupees, but thanks to the current discount, you can enroll for just 31,395 INR.

Bangalore, Chennai, Pune, Hyderabad, and Kochi do indeed provide Data Engineer Classroom Courses through DataMites®. On the applicants' request and based on the availability of additional candidates from the specific place, we would be happy to host one in other locations.

We provide you with customizable learning alternatives that range from live online training to self-paced courses and classroom instruction. You may select according to your schedule.

You can attend sessions from DataMites® for any query or revision you want to clear for three months with our Flexi-Pass for Data Engineer training.

We will provide you certifications for your pertinent abilities from IABAC®, NASSCOM Future Skills, and JAINx, which are recognized internationally.

We are committed to offering you trainers who are highly qualified, certified, have a lot of experience in the field, and are knowledgeable about the material.

The exam results are immediately accessible if you take them online at exam.iabac.org. E-certificate issuance takes 7 to 10 business days, as per IABAC requirements.

You will receive a Data Engineer Course Completion Certificate once your course is over, of course.

Yes. The participation certificate must be issued and the certification exam must be scheduled using photo IDs such as a national ID card, driver's license, etc.

You will receive a complimentary demo session to provide you with a quick overview of the training's objectives and methodology.

To reserve your seat for the whole course and to work with IABAC to schedule your certification exams, the course fee must be paid in full. Your DataMites® relationship manager can help you with part payment agreements if you have any special constraints.

You shouldn't be concerned about it. Simply get in touch with your instructors and arrange a lesson time that works for you. Data Engineer Training Online in Ludhiana will record and publish each session so that you can simply catch up on what you missed at your own pace and comfort.

Using your specific certification number, all certificates can be validated at DataMites®.com. You could also send a message to care@DataMites®.com.

  • Utilizing Case Studies to Learn
  • Model Deployment, Case Study, Project, and Theory

Yes, you must undoubtedly maximize your training sessions. If you require any additional clarity, you can, of course, request a support session.

Payment can be made by using;

  • Credit Card
  • American Express
  • Master card
  • PayPal
  • Visa
  • Cash
  • Net Banking
  • Check
  • Debit Card

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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