CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN BANGALORE

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 BANGALORE

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 BANGALORE

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 BANGALORE

The DataMites® Data Engineer Course is intended to envelop all areas of data engineering using Python, including statistics, database fundamentals, Big Data, Data Wrangling, Numpy, Pandas, and other relevant topics. The demand for qualified Data Engineers is growing as data becomes more pervasive.

The Data Engineer Training Course includes a two-month real-world project as well as an internship opportunity to give students practical experience and real-world exposure. The Data Engineer course has no pre-requisites because it covers topics from the ground up.

A data engineer course is a valuable investment for individuals aspiring to excel in the field of data engineering. In today's data-driven world, organizations rely heavily on the effective management and processing of vast amounts of data. A data engineer course equips participants with the necessary skills and knowledge to design, build, and maintain robust data infrastructure and pipelines.

The International Association of Business Analytics Association (IABAC®), NASSCOM Future Skills Certification, and Jain University's JAINx have all given their approval to the curriculum.

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 provides a variety of learning alternatives, including live instructor-led training and in-person classroom training across many time zones. Both weekends and weekdays are accessible for training.

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

It's tough to describe data engineering accurately. It entails planning and constructing the data infrastructure required to gather, clean, and format data so that it is accessible and usable to end-users. It's frequently referred to as a relative of data science or a continuation of software engineering.

“It’s a huge competitive advantage to see in real-time what’s happening with your data.”

    - Hilary Mason

Why Should You Attend Data Engineer Training?

  1. Data Engineering is the foundation of data science.
  2. Data Engineering is a technically difficult field of research.
  3. It's exceedingly gratifying - Data engineers aren't wholly inspired by the desire to make data scientists' jobs trouble-free. Doubtless, data engineers are having a rising impact on society.
  4. It's a significant skill to have if you want to work in the field of data science.
  5. A lucrative career with high job security

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.

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 in India earn over 10,00,000 LPA each year.

The DataMites® Data Engineering Course is the initial step in a data engineering career. Develop the skills you'll need to break into this expanding field or brush up on what you already know about data warehousing and ETLs, data storage, and data consumption from a variety of sources. 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.

The following are some of the advantages of taking a Data Engineer course:

  1. Comprehend the basics of data engineering.
  2. Acknowledge the Data Engineering Ecosystem and Lifecycle
  3. Discover how to extract data from a wide range of files and databases.
  4. Grasp how to use various skills and tactics to clean, change, and improve your data.
  5. In relational and NoSQL databases, learn how to operate with various file types.
  6. Learn how to create dashboards to track progress and how to set up a data pipeline.
  7. Know how to scale data pipelines in a real-world setting.

A job as a Data Engineer is lucrative, secure, and extremely demanding.

In every organization, the function of a Data Engineer is critical in realizing 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. They're about to give data scientists a run for their money.

There are now 40K data engineer jobs available in India. (LinkedIn) One of the most appealing aspects of this career is that it pays well. Data Engineers are well compensated by companies like Amazon, Deloitte, Netflix, and IBM. And, like with any industry, the more job experience you have, the better the benefits you will receive in the market.

India is already one of the world's leading Big Data analytics marketplaces, and NASSCOM has set the goal of making India one of the top three. According to NASSCOM, the Indian analytics business would be worth USD 16 billion by 2025.

In regards to being India's IT capital, Bangalore is the residence of industries such as aeronautics, biotechnology, automotive components, electronics machine tools, scientific research, space research, defense science research, and silk. In this competitive industry, it's vital to find the best software companies in Bengaluru that can deliver the most innovative custom-based software. Bangalore is in the spotlight because it is home to a large number of the country's IT industries. A Data Engineer in Bangalore earns an average salary of 10,00,000 LPA. (Glassdoor.com)

Whilst job growth and income are both appealing, statistics show that Data Engineer is the fastest-growing position in the technology area, and you can get started on your professional beginning in the Data Engineering domain with our Data Engineering Certification Course in Bangalore!

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

ABOUT DATA ENGINEER COURSE IN BANGALORE

Large-scale data gathering, storage, and analysis systems are created through the process of data engineering. It is a broad field with applications in practically every sector.

The first and most crucial step in becoming a data engineer is to receive the necessary training. To upskill one's skills and gain a complete understanding of the data science and data engineering domain, one must enroll in a certification program.

You can learn more about how to become a data engineer by enrolling in courses that can range from three to twelve months. Contrarily, depending on the degree or certification sought after, the course program differs. Three-month courses can give you crucial Data Engineer experience and internship opportunities, which can lead to entry-level jobs at prestigious companies.

If you want to work in the industry, the Data Engineer Course is the one to enroll in since it accredits you as a data science specialist. After completing our extensive program, you'll possess the abilities required to be a successful data engineer as well as a portfolio that is ready for use in job interviews.

Entry into this field requires a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a closely related field. You'll need practical experience, like an internship, to even be considered for the majority of entry-level positions.

Depending on the level and kind of training you select, Data Engineer Training Fees in Bangalore can range anywhere between 20,000 INR and 80,000 INR in India.

DataMites® is the greatest institute for complete training in courses in data engineering, data science, artificial intelligence, and other related topics. DataMites® develops and makes available a comprehensive crafter training program in partnership with eminent data engineering experts.

Data engineering is not always an entry-level position. Instead, a lot of data engineers begin their careers as software engineers or business intelligence analysts. You might transition into administrative positions as your career progresses, or you might work as a machine learning engineer, data architect, or solutions architect.

Coding, data warehousing, database management, data analysis, critical thinking, comprehension of machine learning, and other abilities are among the fundamental data engineering skills.

  • 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 evaluate the data to identify trends, gain business insights, and provide answers to issues that are important to the organization. Data engineers create and manage the systems and structures that store, retrieve, and organize data.

Python for Data Engineering includes all aspects of data wrangling, including reshaping, collecting, and linking diverse sources, small-scale ETL, API interaction, and automation. There are several reasons why Python is well-liked. Its accessibility is one of the main benefits.

Overall, a career as a data engineer is a great fit for those who value accuracy, adherence to engineering standards, and the development of pipelines that turn raw data into actionable insights. Data engineering careers have excellent income potential and stable employment.

A profession as a data engineer is stable, physically demanding, and financially rewarding. Every firm needs a data engineer to help it realize the full potential of its data. It is one of the professions with the fastest global growth rates, with an over 88.3% rise in job posts in 2019 and over 50% growth in the number of vacant positions.

Before submitting a full-time data engineer job application, it's a good idea to start with an internship. Internships are essential for getting experience and increasing practical knowledge prior to full-time employment since data engineering takes practise. People who have never worked previously are more likely to receive internship offers from businesses. After completing an internship, it will be considerably simpler for you to land an entry-level position with the company.

In the hierarchy of data science requirements, it's also a crucial step because, without the architecture created by data engineers, analysts and scientists won't be able to access or work with data. And as a result, businesses run the danger of losing access to one of their most priceless assets. According to the Dice 2020 Tech Career Report, with a 50% increase in accessible positions year over year, data engineering is the position in technology with the biggest growth in 2019.

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FAQ’S OF DATA ENGINEER COURSE IN BANGALORE

The difficult task for data engineers is to strike a compromise between immediate needs and a longer-term perspective of where data demands will take the systems they oversee. With each new architecture you create, there is a persistent worry that you will reach a technical impasse. Data is certainly essential for expanding your organization and learning useful information. Despite being difficult to understand, a data engineering course can be useful for gaining the necessary domain knowledge.

A poll conducted by DICE, an online platform that maintains one of the largest databases of technology specialists, found that the fastest-growing position in technology is data engineer, with a year-over-year increase of over 50% in 2020. A recent survey found that demand for jobs in data engineering has significantly increased. Scalable solutions will be developed using your programming and problem-solving abilities.

The national average salary for a Data Engineer in India is 10,00,000 LPA. A Data Engineer in Bangalore earns an average salary of 10,00,000 LPA. (Glassdoor.com)

The DataMites® Data Engineer Courses in Bangalore are specifically designed to educate data engineering from scratch. Anyone can now enroll in the course. This career path is for those looking for a change in career, data professionals looking to broaden their skill set for the next promotion, and college students looking for employment.

There is a tonne of space for improvement in the data engineering field in terms of capacity, remuneration, and learning. Aspirants can enroll in the DataMites Data Engineer Course Online in Bangalore, where we offer comprehensive instruction for their future job.

The Data Engineer Course in Bangalore lasts for three months and includes 120 hours of instruction. Weekdays and weekends are both used for training sessions. You can select any option based on your availability.

No, a graduate degree is not required, however, it can be very helpful to have prior knowledge of mathematics, statistics, economics, or computer science.

  • The International Association of Business Analytics Certification (IABAC), NASSCOM, and Jain University have all granted accreditation to DataMites®, the world's leading institute for data engineer training.
  • The courses we provide are being taken by more than 50,000 students.
  • We present a three-step learning process. The applicants will be given books and self-study videos in Phase 1 to help them gain a thorough understanding of the curriculum. The second phase of the intensive live online instruction is the main phase. We'll also share the projects and placements during the third phase.
  • The entire program consists of case studies and real-world projects.
  • You will be awarded the IABAC, NASSCOM Future Skills, and JAINx Certifications after the training.
  • You will get the opportunity to complete an internship with AI company Rubixe, a major worldwide technology company, after completing your course.

The price of a data engineering course online in Bangalore is 42,000 INR, but thanks to a current discount, you may enroll for just 31,395 INR.

Yes, DataMites® offers Data Engineer Classroom Courses in the Indian states of Bangalore, Chennai, Pune, Hyderabad, and Kochi. Depending on the demand of the applicants and the availability of additional candidates from the precise place, we would be happy to host one in another location.

We are adamant about giving you access to certified, highly skilled trainers with years of experience in the field and a solid understanding of the material.

We provide a variety of flexible learning choices, including live online training, self-paced courses, and classroom instruction. You can make a decision based on your schedule.

For three months, you will be able to attend sessions from DataMites® relating to any query or revision you wish to clear thanks to our Flexi-Pass for Data Engineer training.

We will grant you IABAC®, NASSCOM Future Skills, and JAINx certificates, which offer widespread acknowledgment of necessary skills.

The results are immediately accessible if you take the exam online at exam.iabac.org. IABAC recommendations state that e-certificate issuance takes 7 to 10 business days.

Of course, we will give you a Data Engineer Course Completion Certificate once your course is over.

Yes. For the purpose of awarding the participation certificate and scheduling the certification exam as necessary, photo ID proofs such as a national ID card, driver's licence, etc.

You shouldn't stress over it. Simply contact your instructors about it and arrange a class time that works for you.

Each session of the Data Engineer Training Online in Bangalore will be filmed and published, allowing you to quickly catch up on the material you missed at your own pace and convenience.

Yes, you will be given a free sample class to provide you with a quick overview of the training's procedures and contents.

To reserve your seat for the entire course and to schedule your certification exams with IABAC, 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.

Using your specific certification number, you can verify all certificates at DataMites®.com. Alternatively, you can email care@DataMites®.com.

  • Using a Case Study Approach to Learning
  • Theory, Practical Application, Case Study, Project, and Model Deployment

You must, of course, maximize your training sessions. Of course, if you require any additional clarification, you can request a support session.

We take payments via;

  • Credit Card
  • Master card
  • PayPal
  • Visa
  • 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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