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  • 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

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

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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 CERTIFIED DATA ENGINEER COURSES

Why DataMites Infographic

SYLLABUS OF CERTIFIED DATA ENGINEER COURSES

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
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 3: DATA PROCESSING

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

MODULE 4: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data Ingestion through Pandas methods
• Hands-on: Ingestion, Transform Data and Load data

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

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

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

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

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

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

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • 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 MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

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 WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• 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
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 3: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 4: 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 5: DOCKER AND KUBERNETES FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping, and managing images)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 6: 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 7: 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 Lamdba overview
  • AWS Glue overview
  • AWS Kinesis overview
  • AWS Dynamodb overview
  • AWS Anthena overview
  • AWS Redshift overview

MODULE 2 : DATA INGESTION USING AWS LAMDBA 

  • Setup AWS Lamdba  local development env
  • Deploy project to Lamdba console
  • Data pipeline setup with Lamdba
  • Validating data files incrementally
  • Deploying Lamdba function

MODULE 3 : DATA PIPELINE WITH AWS KINESIS 

  • AWS Kinesis overview and setup
  • Data Streams with AWS Kinesis
  • Data Ingesting from AWS S3 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 Blog Storage

MODULE 7: AZURE DATA FACTORY

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

MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS

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

MODULE 1: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• 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
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 2: DOCKER FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping and managing images)
• Publishing containers to Docker Hub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 3: KUBERNETES CONTAINER ORCHESTRATION

• Kubernetes Introduction
• Setting up Kubernetes Clusters
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

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 : 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
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

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

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

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

CERTIFIED DATA ENGINEER CAREER SUCCESS STORIES

CERTIFIED DATA ENGINEER COURSE REVIEWS

ABOUT CERTIFIED DATA ENGINEER COURSE

The DataMites® Certified Data Engineer Course is the most comprehensive, industry-aligned AI Course with an EU Framework Certification from IABAC that is designed to cover all the aspects of data engineering with Python, Statistics, Database essentials, Big Data, Data Wrangling, Numpy, Pandas, and other related topics. As data becomes ubiquitous so is the demand for qualified Data Engineers. 

 

The Data Engineer Training is for 6 months, with 3 months of the real-time project along with the internship facility to provide practical experience and real-world exposure. There are no hard prerequisites for the Data Engineer Course as the course covers topics from the scratch. The content is accredited by the International Association of Business Analytics Association (IABAC®) and JAINx® from Jain University.

 

The Data Engineer Curriculum includes a complete package of 10 courses:

  • Data Engineering Foundation - Data Engineering Introduction, Data Sources & Data Import, Data Processing, and Data Engineering Project.

  • Python Foundation - Python Basics, Python Control Statements, Python Data Structures, Python Functions, Python Numpy Package, and Python Panda Package.

  • Data Science Foundation -  Data Science Essentials, Data Engineering Foundation, Python for Data Science, Visualization with Python, R Language Essentials, Statistics, and Machine Learning Introduction.

  • Version Control with Git - GIT Introduction, GIT Repository & GitHub, Commits, Pull, Fetch & Push, Tagging, Branching & Merging, Undoing Changes, and GIT with GitHub & Bitbucket.

  • Big Data Foundation - Big Data Introduction, HDFS & MAP Reduce, PySpark Foundation, Spark SQL & HADOOP Hive, Machine Learning with Spark ML, Kafka & Spark.

  • Certified BI Analyst - Business Intelligence Introduction, BI with Tableau: Introduction, Tableau: Connecting to Data Source, Tableau: Business Insights, Dashboards, Stories & Pages, BI with Power BI.

  • Database: SQL and MongoDB - Database Introduction, SQL Basics, Data Types & Constraints, Database & Tables(MySQL), SQL Joins, SQL Commands & Clauses, Document DB/NO-SQL DB.

  • Data Engineering Associate - Data Warehouse Foundation, Docker Foundation, Kubernetes Container Orchestration, Data Orchestration with Apache Airflow, and Data Engineering Project.

  • Data Engineering on AWS Cloud - AWS Data Services Introduction, Data Ingestion using AWS Lambda, Data Preparation with AWS Glue, Spark APP using AWS EMR, Data Pipelines with AWS Kinesis, Data Warehouse with AWS Redshift, and Data Engineering Project.

  • Data Engineering on Azure Cloud - Azure Data Services Introduction, Storage in Azure, Azure Data Factory, Data Pipeline with Azure Synapse, and Data Engineering Project with Azure.

 

It is frequently updated to reflect changes in the industry and has been streamlined to make learning more systematic and conducive to agile learning.

 

DataMites offers flexible learning options with both Online Certified Data Engineer Training and Certified Data Engineer Classroom Training in various time zones. The training is available both on weekends and weekdays.

 

In every organisation, the function of a Data Engineer is critical in realising the full potential of data. It is one of the fastest-growing professions in the world, according to a survey, with over 88.3 percent rise in job posts in 2019 and over 50 percent year-over-year growth in several vacant positions. 

 

“Where there is data smoke, there is business fire.”

      - Thomas Redman (aka ‘data doc’)

 

Why Should You Attend Data Engineer Training?

  1. It's because it's the foundation of data science.

  2. It's a technically difficult field of research.

  3. It's extremely rewarding - Data engineers aren't merely motivated by the desire to make data scientists' jobs easier. There's no doubting that data engineers are having an ever-increasing impact on society.

  4. It's an important ability to have if you want to work in the field of data science.

  5. A well-paying career with high job security

 

DataMites® Data Engineering Course is the first step toward a career in data engineering. Develop the skills you'll need to enter into this burgeoning area or brush up on what you already know about data warehousing and ETLs, data storage, and consuming data from many 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 Certified Data Engineer Training is imparted through the following phases;

Phase 1 = For a seamless pre-learning experience, students will receive pre-course self-study materials and top-notch video sessions to watch.

Phase 2 = Students will participate in a 3-month live training program led by knowledgeable instructors and mentors.

Phase 3 = Students receive 3 months of internship and project exposure along with specialized coaching from professionals. This phase contains 10 capstone projects, 1 live/client project, and experience certification. The IABAC Certification, JAINx Certification, and DataMites Certified Data Engineer Course Completion Certification is awarded to students upon completion of the training and projects.

 

Learning Benefits of Data Engineer Course:

  1. Understand what Data Engineering entails.

  2. Recognize the Ecosystem and Lifecycle of Data Engineering

  3. Learn how to extract information from a variety of files and databases.

  4. Learn how to clean, alter, and enrich your data using various skills and strategies.

  5. Learn how to work with various file types in relational and NoSQL databases.

  6. Learn how to set up a data pipeline and create dashboards to track progress.

  7. Learn how to scale data pipelines in a production environment.

 

Working in the field of big data is an excellent choice for a career. Data engineers have seen a 30% increase in job postings over the previous five years, which is much more than the national average. Furthermore, according to Glassdoor, data engineers earn over $110,000 each year.

 

The worldwide big data and data engineering services market is predicted to develop at a Compound Annual Growth Rate (CAGR) of 17.6 percent from USD 29.50 billion in 2017 to USD 77.37 billion by 2023. The study's base year is 2017, with a projection span of 2018–2023. (Markets and Markets)

 

Furthermore, the national average salary for a Data Engineer is USD 1,12,493 per year in the United States. The salary for a Data Engineer is £41043 per annum in the UK. And a data engineer in India earns an average amount of INR 9,80,000 per year! (Glassdoor) 

 

While the job growth and pay are both attractive, according to studies, Data Engineer is the fastest-growing job in the technology field, and with our Data Engineering Certification Course, you can get started on your new career in the Data Engineering domain!

ABOUT DATAMITES CERTIFIED DATA ENGINEER COURSES

Data Engineer Course is designed as job oriented course for Data Engineering roles.  The Data Engineering is the foundation for Data Science work flow, covering data gathering, manipulation, processsing and transforming data to get it read for further Data Science processes. Data Engineer course  apart from covering key data engineering concepts also covers Python Language, Statistics, Big Data popular frameworks. 

Data Engineer course bundled with project mentoring and internship facility.

Data engineering is the process of developing and constructing large-scale data collection, storage, and analysis systems. It's a wide-ranging field with applications in almost every industry.

To become a data engineer, the first and most important step is to get appropriate training in the field. Obtaining a thorough understanding of the data science and data engineering domain through a certification course and thereby upskilling the talents is a must for landing a job in the field.

Attending Data Engineer Courses, which may last anywhere from three to twelve months, can help you become a data engineer. The course curriculum, on the other hand, varies based on the degree or certification desired. 3-month courses can provide you with important Data Engineer experience and internship possibilities, leading to entry-level positions at top businesses.

The Data Engineer Course is the one to take if you want to work in the business because it certifies you as an expert in the field of data science. After finishing our comprehensive programme, you'll have the skills you need to succeed as a data engineer, as well as a job-ready portfolio to show off during the interview process.

A bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related discipline is required for entry into this field. To even qualify for most entry-level roles, you'll need real-world experience, such as internships.

The cost of Data Engineer Training in the US can be anywhere from 257.68 USD to 1030.71 USD, depending on the course level and type of training you choose. Data Engineer Training in the UK can cost anywhere from 205.15 GBP to 820.60 GBP and the fees for Data Engineer Training in India can range from 20,000 INR to 80,000 INR.

DataMites® is the best institute for comprehensive training in courses in data engineering, data science, artificial intelligence, and other related fields. DataMites® collaborates with world-renowned Data Engineer professionals to build and offer an extensive crafter training curriculum. 

Data engineering isn't always an entry-level role. Instead, many data engineers start off as software engineers or business intelligence analysts. As you advance in your career, you may move into managerial roles or become a data architect, solutions architect, or machine learning engineer.

Some of the essential skills of a data engineer are coding, data warehousing, database system, data analysis, critical thinking, understanding of machine learning and more.

  • 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 engineers design and manage the systems and structures that store, retrieve, and organise data, whereas data scientists analyse that data to predict patterns, gain business insights, and answer questions that are relevant to the organisation.

Data Wrangling, such as reshaping, aggregating, and connecting disparate sources, small-scale ETL, API interaction, and automation, are all part of Python for Data Engineering. Python is popular for a variety of reasons. One of the most significant advantages is its accessibility.

Overall, becoming a data engineer is an excellent career choice for people who enjoy paying attention to detail, adhering to engineering requirements, and creating pipelines that transform raw data into useful insights. A profession in data engineering provides good earning potential and job security.

A career as a Data Engineer is financially rewarding, stable, and physically hard. The role of a Data Engineer is crucial in realising the full potential of data in every organisation. According to a poll, it is one of the fastest-growing professions in the globe, with over 88.3 percent growth in job postings in 2019 and over 50% year-over-year growth in numerous open positions.

It's a good idea to start with an internship before applying for full-time data science employment. Data engineering requires practice, thus internships are a must for gaining experience and broadening practical knowledge before full-time employment. Companies are more likely to provide internships to people who have never worked before. It will be much easier for you to obtain an entry-level position in the organisation after finishing an internship.

It's also an important stage in the hierarchy of data science requirements: without data engineers' architecture, analysts and scientists won't be able to access or work with data. And as a result, corporations risk losing access to one of their most precious assets. Data engineering is the fastest-growing position in technology in 2019, according to the Dice 2020 Tech Career Report, with a 50 percent rise in accessible jobs year over year.

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FAQ’S OF CERTIFIED DATA ENGINEER TRAINING COURSE

Data engineers face the difficult task of reconciling immediate needs with a longer-term view of where data demands will lead the systems they manage. With each new architecture you create, there's a persistent dread that you've trapped yourself into a technical dead-end. Without a doubt, data is essential for expanding your business and gaining important insights. Data engineering, often known as information engineering, is a software-based strategy for developing information systems.

It was an excellent decision. You've chosen a wealthy, secure, and demanding career path. As of June 2022, there are about 44,209 Data Engineer-related job openings globally. (Indeed.com)  According to a recent poll, there has been a considerable surge in demand for data engineering job positions. You'll utilise your programming and problem-solving skills to create scalable solutions.

DataMites® Data Engineer Courses are carefully crafted to teach Data Engineering from scratch. The course henceforth can be taken by anyone. This career path is for those who are searching for a career shift, data professionals who want to expand their skill set for the next promotion, and college students who want to get a job.

In the data engineering domain there is a lot of room for advancement in terms of learning, capacity, and pay. Aspirants can enrol at DataMites® for Data Engineer Course Online, we provide in-depth training for your further career.

The duration of the Data Engineer Course is 3 months, totalling 120 hours of training. Training sessions are imparted on weekdays and weekends. You can choose any as per your availability.

No, a PG degree is not necessary but having prior knowledge of Mathematics, Statistics, Economics or Computer Science can be highly beneficial.

  • DataMites®™ is the global institute for Data Engineer Training accredited by the International Association of Business Analytics Certification (IABAC), NASSCOM and Jain University.
  • We have more than 50,000 students enrolled in the courses we offer.
  • We provide a three-step learning method. In Phase 1, self-study videos and books will be provided to the candidates to help them get adequate knowledge about the syllabus. Phase 2 is the primary phase of intensive live online training. And in the third phase, we will release the projects and placements.
  • The entire training includes real-world projects and highly valuable case studies.
  • After the training, you will receive the IABAC, NASSCOM Future Skills and JAINx Certifications. 
  • After completing your training, you will get the chance to do an internship with AI company Rubix, a global technology company.

The cost of 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.

Yes, DataMites® do provide Data Engineer Classroom Courses in the Indian states of Bangalore, Chennai, Pune, Hyderabad and Kochi. We would be pleased to host one in other locations, ON-DEMAND of the applicants as according to the availability of other candidates from the exact location.

We are determined to provide you with trainers who are certified and highly qualified with decades of experience in the industry and well versed in the subject matter.

We offer you flexible learning options ranging from live online, self-learning methods to classroom training. You can choose as per your availability. 

Our Flexi-Pass for Data Engineer training will allow you to attend sessions from DataMites® for 3 months related to any query or revision you wish to clear.

We will issue you an IABAC®, NASSCOM Future Skills and JAINx certifications that provide global recognition of relevant skills.

If you take the exam online at exam.iabac.org, the results are available immediately. According to IABAC guidelines, e-certificate issuing takes 7-10 business days.

Of course, after your course is completed, we will issue you a Data Engineer Course Completion Certificate.

Yes. Photo ID proofs like a National ID card, Driving license etc. are needed for issuing the participation certificate and booking the certification exam as required.

You don't need to worry about it. Just get in touch with your instructors regarding the same and schedule a class as per your schedule. In the case of Data Engineer Training Online, each session will be recorded and uploaded so that you can easily learn what you missed at your own pace and comfort.

Yes, a free demo class will be provided to you to give you a brief idea of ​​how the training will be done and what will be involved in the training.

The course price must be paid in full to reserve your spot for the complete course as well as arrange your certification examinations with IABAC. If you have any unique limits, your DataMites® relationship manager will assist you with part payment agreements.

All certificates can be verified at DataMites®.com using your unique certification number. Alternatively, you may send an email to care@DataMites®.com.

Yes, we have a dedicated Placement Assistance Team (PAT) who will provide you with placement facilities after the completion of the course.

Learning Through Case Study Approach

Theory → Hands-on → Case Study → Project → Model Deployment

Yes, of course, you must make the most of your training sessions. You can of course ask for a support session if you need any further clarification.

 

We accept payment through;

  • Credit Card
  • PayPal
  • Visa
  • Master card
  • American Express
  • 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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