CERTIFICATION AUTHORITIES

COURSE FEATURES

de LEAD MENTORS

DATA ENGINEER COURSE FEES IN MYSORE

Live Virtual

Instructor Led Live Online

68,000
46,095

  • IABAC® & JAINx® 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

41,000
26,145

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

Classroom

In - Person Classroom Training

68,000
50,295

  • IABAC® & JAINx® 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 MYSORE

CERTIFIED DATA ENGINEER COURSE 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 MYSORE

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: DATA SCIENCE ESSENTIALS

• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow

MODULE 2: DATA ENGINEERING FOUNDATION

• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering

MODULE 3: PYTHON FOR DATA SCIENCE

• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures: Series and DataFrame
• Pandas DataFrame key methods

MODULE 4: VISUALIZATION WITH PYTHON

• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations

MODULE 5: R LANGUAGE ESSENTIALS

• R Installation and Setup
• R STUDIO – R Development Env
• R language basics and data structures
• R data structures, control statements

MODULE 6: STATISTICS

• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T-Tests
• Direct And Indirect Correlation
• Regression Theory

MODULE 7: MACHINE LEARNING INTRODUCTION

• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression

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: 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 a 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: 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: 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
• Bitbucket Git account

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

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing

MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages

MODULE 6: BI WITH POWER-BI

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI

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 PREPARATION WITH AWS GLUE 

• AWS Glue Components
• Spark with Glue jobs
• AWS Glue Catalog and Glue Job APIs
• AWS Glue Job Bookmarks

MODULE 4: SPARK APP USING AWS EMR

• PySpark Introduction
• AWS EMR Overview and setup
• Deploying Spark app using AWS EMR

MODULE 5: DATA PIPELINE WITH AWS KINESIS

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

MODULE 6: 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 7: DATA ENGINEERING PROJECT

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

MODULE 1: AZURE DATA SERVICES INTRODUCTION

• Azure Overview and Account Setup
• Azure Storage
• Azure Data Lake
• Azure Cosmos DB
• Azure SQL Database
• Azure Synapse Analytics
• Azure Stream Analytics
• Azure HDInsight
• Azure Data Services

MODULE 2: STORAGE IN AZURE

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

MODULE 3: AZURE DATA FACTORY

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

MODULE 4: 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 5: DATA ENGINEERING PROJECT WITH AZURE

• Hands-on Project Case-study
• Setup Project Development Env
• Organization of Data Sources
• Setup AZURE services for Data Ingestion
• Data Extraction Transformation with Azure Data Factory and Azure Synapse

DATA ENGINEER TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ENGINEER TRAINING IN MYSORE

The DataMites® Data Engineer Course blankets all the significant elements of data engineering with Python, Statistics, Database essentials, Big Data, Data Wrangling, Numpy, Pandas, and other related topics. In a world where data is widespread the call for qualified and competent data engineers seems to only grow. 

The Data Engineer Training Course is of two months of the real-time project along with the internship opportunity offering practical experience and real-world exposure. There are no mandatory prerequisites for the Data Engineer Training as the course covers topics from the bottom up.

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

Data Engineer Course Curriculum

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

DataMites offers flexible learning options with both live instructor-led training and in-person classroom training in various time zones. The training is available both on weekends and weekdays.

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

The position of the data engineer has changed dramatically during the previous decade. Data is acquired more regularly, is significantly less segregated, and is more available to everyone throughout an organization than ever before, thanks to industry innovations like the introduction of scalable processing and cloud data warehouses. As these systems demand increasingly sophisticated infrastructure, the engineer's position has become increasingly important.

“Data that is loved tends to survive.” 

      -  Kurt Bollacker, computer scientist

What are the Advantages of Data Engineer Training?

  1. The building block of data science is data engineering.
  2. Data engineering is a technically challenging area of study.
  3. It's quite satisfying - data engineers aren't entirely motivated by a desire to make data scientists' jobs easier. Data engineers, without a doubt, are having an increasingly significant impact on society.
  4. If you want to work in the field of data science, this is a crucial ability to have.
  5. Rewarding employment with a solid job security

Data Engineering's purpose is to offer an orderly, uniform data flow that enables data-driven models like machine learning models and data analysis. The data flow described above can pass via numerous companies and teams. We utilize the data pipeline approach to achieve the data flow. It's a system with several separate programs that perform various tasks.

Data engineers have overtaken data scientists by a factor of two, with a 20 to 30% higher compensation than the latter. Year after year, earnings continue to rise significantly.

The DataMites® Data Engineering Course is a great channel in data engineering. 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 multiple 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 reasons for taking a Data Engineer course:

  1. Understand the basics of data engineering.
  2. Know inside out of the Data Engineering Ecosystem and Lifecycle
  3. Discover how to extract data from a wide range of files and databases.
  4. Learn 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. Know how to create dashboards to track progress and how to set up a data pipeline.
  7. Find out how to scale data pipelines in a real-world setting.

Data engineers make a good living in the analytics field, and they're in high demand in a variety of industries.

India is a talent hotspot. Nearly half of the world's data engineers live in the United States, while India comes second with 11.96 percent of data engineering expertise, according to a poll. The data industry is expected to reach $680 billion by 2020, with a CAGR of 24%. However, as the importance of data and skilling in IT and technology organizations grows, it is time for India to establish itself as the leading state-of-the-art' destination for qualified data engineers and professionals.

Mysore is Karnataka’s cultural capital is now on its path toward becoming a prominent IT hub besides the country’s biggest IT hub, Bangalore. The national average salary for a Data Engineer in India is INR 8,65,008 per year! (Payscale) A data engineer in Mysore receives an annual salary of 5,37,312 INR! (Indeed.com)

Even if the job market and compensation are both appealing, statistics suggest that Data Engineer is the fastest-growing vocation in the technology area, and you can get going in the Data Engineering sector with our Data Engineering Certification Course in Mysore!

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

ABOUT DATA ENGINEER COURSE IN MYSORE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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FAQ’S OF DATA ENGINEER COURSE IN MYSORE

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

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

The national average salary for a Data Engineer in India is INR 8,65,008 per year! (Payscale) A data engineer in Mysore receives an annual salary of 5,37,312 INR! (Indeed.com)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Payment can be made by using;

  • Credit Card
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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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