CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN SURAT

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 SURAT

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 SURAT

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 SURAT

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.

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

A data engineer course equips individuals with the essential skills and knowledge to excel in the expanding field of data engineering. With increasing demand for data professionals, this course offers a valuable opportunity to gain expertise in data processing, integration, pipelines, and warehousing for career advancement.

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.

Surat is a city in Gujarat's western state, located near the mouth of the Tapti River. Surat is known as the Diamond City of India and is India's first smart IT city. It is home to significant IT companies and is known as the country's first smart IT city. A Data Engineer in Surat earns an average salary of 6,25,998 LPA. (Indeed.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 starting on your professional beginning in the Data Engineering domain with our Data Engineering Certification Course in Surat!

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

ABOUT DATA ENGINEER COURSE IN SURAT

The development and construction of massive data collection, storage, and processing systems are known as data engineering. With applications in practically every industry, it is a broad field.

The first and most crucial stage in becoming a data engineer is to complete the necessary training in the subject. If you want to work in the industry, you must complete a certification course to gain a deep understanding of the data science and data engineering domain and to upskill your skills.

You can learn how to become a data engineer by enrolling in courses, which can run anywhere from three to twelve months. On the other hand, the course content differs depending on the degree or certification sought after. 3-month courses can give you valuable Data Engineer experience and internship opportunities, which can lead to entry-level careers at reputable companies.

If you want to work in the industry, you should enroll in the Data Engineer Course in Surat because it accredits you as a data science specialist. After completing our extensive curriculum, you'll possess the abilities necessary to be a successful data engineer as well as a portfolio that is ready for use in employment interviews.

Entry into this field requires a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related field. Most entry-level positions require real-world experience, such as internships, to even be considered for.

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

For in-depth instruction in courses in data engineering, data science, artificial intelligence, and other related topics, DataMites® is the ideal educational facility. To create and provide a comprehensive crafter training program, DataMites® works with eminent data engineering professionals.

Not all positions in data engineering are entry-level. In opposition to this, a lot of data engineers begin their careers as software engineers or business intelligence analysts. As your career progresses, you might take on administrative responsibilities or work as a machine learning engineer, data architect, or solutions architect.

Coding, data warehousing, database systems, data analysis, critical thinking, comprehending machine learning, and other abilities are among the fundamental skills of 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 evaluate the data to identify patterns, gain business insights, and provide answers to issues that are pertinent 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 many sources, small-scale ETL, API interaction, and automation. Python is well-liked for many different reasons. Its accessibility is one of the biggest benefits.

Before applying for full-time data engineering work, it's a good idea to start with an internship. Because data engineering involves practice, internships are essential to gaining experience and increasing practical knowledge before landing a full-time job. People with no prior work experience are more likely to be offered internships by businesses. When you have finished an internship, it will be considerably simpler for you to land an entry-level job with the company.

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

A profession as a data engineer is stable, financially lucrative, and physically demanding. Every firm needs a data engineer to help it utilize data to its fullest potential. According to a poll, it is one of the professions with the fastest global growth, with over 88.3% rise in job posts in 2019 and over 50% growth in the number of available positions.

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

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FAQ’S OF DATA ENGINEER COURSE IN SURAT

The challenging work for data engineers is to balance short-term requirements with a longer-term view of where data demands will take the systems they oversee. There is a constant fear that you are stuck in a technical dead-end with every new architecture you design. Data is unquestionably crucial for growing your business and acquiring insightful knowledge. A data engineering course though challenging to learn can come in handy for proper expertise in the domain.

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 more than 50% in 2020. A recent survey revealed that demand for jobs in data engineering has significantly increased. To develop scalable solutions, you'll use your programming and problem-solving abilities.

The Data Engineer Training in Surat lasts for 3 months and includes 120 hours of instruction. On weekdays and weekends, training sessions are conducted. You can select any option depending on your availability.

The national average salary for a Data Engineer in India is 8,65,008 LPA. (Payscale.com) A Data Engineer in Surat earns an average salary of 6,25,998 LPA. (Indeed.com)

The Surat-based DataMites® Data Engineer Courses have been thoughtfully designed to educate data engineering from inception. Henceforth, anyone may enroll in the course. The people looking for a career change, the data professionals looking to broaden their skill set for the next promotion, and the job-seeking college students should all consider this career route.

There is a lot of space for improvement in the data engineering field in terms of capacity, remuneration, and learning. Aspirants can enroll in our Data Engineer Course Online in Surat at DataMites®, where we offer comprehensive instruction for their future careers.

No, a postgraduate degree is not required, however having prior knowledge of mathematics, statistics, economics, or computer science can be very helpful.

The International Association of Business Analytics Certification (IABAC), NASSCOM, and Jain University have all granted accreditation to the international institute for data engineer training known as DataMites.

  • More than 50,000 students are enrolled in our courses.
  • A three-step learning process is offered. The candidates will be given books and self-study videos in Phase 1 to help them gain a sufficient understanding of the curriculum. The second stage of the intensive live online training is the main stage. We will also share the projects and placements during the third phase.
  • Real-world projects and extremely helpful case studies are included throughout the entire training.
  • You will be given the IABAC, NASSCOM Future Skills, and JAINx Certifications after the session.
  • You will get the opportunity to complete an internship with the AI business Rubixe, a major worldwide technology company, after completing your course.

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

Yes, DataMites® offers classroom courses for data engineers in Bangalore, Chennai, Pune, Hyderabad, and Kochi in India. Depending on the demand of the applicants and the availability of other candidates from the specific place, we would be happy to host one in another location.

We are adamant about giving you access to certified, highly trained trainers that have years of expertise in the field and are knowledgeable about the material.

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

With our Flexi-Pass for Data Engineer training, you get three months to attend DataMites® sessions linked to any query or revision you want to clear.

We will provide you with certifications from IABAC®, NASSCOM Future Skills, and JAINx, which offer universal recognition for pertinent skills.

The results of the exam can be seen right away if you take it online at exam.iabac.org. IABAC rules state that e-certificate issuance takes 7 to 10 business days.

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

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.

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

To reserve your seat for the entire course and 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 limitations.

Using your particular certification number, you can verify all certificates at DataMites®.com. A different option is to email care@DataMites®.com.

  • You shouldn't worry about it. Simply contact your instructors and arrange a lesson time that works for you.
  • Concerning Data Engineer Training Online in Surat, each session will be recorded and published so that you can simply catch up on what you missed at your own pace and ease.
  • Learning Through Case Study Methodology
  • Theory, Practical, Case Study, Project, and Model Deployment

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

We accept payments through the;

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