CERTIFICATION AUTHORITIES

COURSE FEATURES

de LEAD MENTORS

DATA ENGINEER COURSE FEES IN JAIPUR

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 JAIPUR

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 JAIPUR

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 JAIPUR

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

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.

Jaipur is surrounded by a new talent pool, with over 30 engineering and technological universities, including NIT Jaipur, BITS Pilani, and best-in-class engineering colleges. Attracting, developing, and maintaining the talent needed to create a long-term workforce is becoming increasingly important to every business community.  A Data Engineer in Jaipur earns an average salary of 5,18,327 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 Jaipur!

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 Jaipur.

ABOUT DATA ENGINEER COURSE IN JAIPUR

Large-scale data gathering, storage, and analysis systems are developed and built through the process of data engineering. It is a broad field with applications in practically all industries.

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.

Because it qualifies you as a specialist in the subject of data science, the Data Engineer Course in Jaipur is the one to take if you want to work in the industry. After completing our extensive curriculum, you'll possess the abilities necessary to be a successful data engineer in addition to a portfolio that is ready for employment that you can use to impress potential employers.

Getting the right training in the field is the first and most crucial step to becoming a data engineer. For one to find employment in the sector, one needs to complete a certification course to gain a comprehensive understanding of the data science and data engineering domain and upskill one's skills.

For admittance into this field, one must possess a bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related field. You'll need practical experience, like an internship, to even be considered for most entry-level positions.

The greatest institute for thorough instruction in courses in data engineering, data science, artificial intelligence, and other related topics is DataMites®. In order to develop and provide a comprehensive artisan training program, DataMites® works with recognized data engineering professionals.

Depending on the type of training you select and the level of the course, the cost of Data Engineer training in Jaipur can be anywhere between 20,000 INR and 80,000 INR.

It's not always an entry-level position for data engineering. Many data engineers, however, begin their careers as software engineers or business intelligence analysts. As your career progresses, you might take on administrative responsibilities or work as a data architect, solutions architect, or machine learning engineer.

Coding, data warehousing, database management, data analysis, critical thinking, and an understanding of machine learning are some of 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 AUD 98,646 per year in Australia. (Payscale)
  • 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 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)

While data scientists analyze the data to identify trends, generate business insights, and provide answers to pertinent organizational questions, data engineers create and manage the systems and structures that store, retrieve, and organize data.

Data engineering is a steady, financially lucrative, and physically demanding profession. Realizing data's full potential in any organization requires the expertise of a data engineer. With over 88.3% more job postings in 2019 and more than 50% more open positions year over year, a poll found that it is one of the professions with the strongest global growth rates.

Python for Data Engineering includes all aspects of data wrangling, including reshaping, collecting, and tying together many sources of data, small-scale ETL, API interaction, and automation. Many factors contribute to Python's popularity. Its accessibility is one of its most important benefits.

Overall, a career as a data engineer is a great fit for those who value accuracy, following engineering specifications and building pipelines that turn raw data into actionable insights. Data engineers have an excellent chance of making a good living and having stable employment.

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 prior to 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.

It is also a critical step in the hierarchy of data science requirements since analysts and scientists cannot access or interact with data without the architecture created by data engineers. Businesses run the danger of losing access to one of their most priceless assets as a result. 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 that is growing the quickest in 2019.

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FAQ’S OF DATA ENGINEER COURSE IN JAIPUR

Data Engineering is taught from inception in DataMites® Data Engineer Courses in Jaipur. Anybody can now enroll in the course. This career path is for people looking for a change in their career, data professionals looking to broaden their skill set for the next promotion, and college students looking for employment.

Balancing immediate needs with a longer-term perspective of where data demands will take the systems they oversee is difficult for data engineers to do. With each new architecture you create, you constantly worry that you'll run into a technical wall. Data is unquestionably essential for developing your company and learning insightful information. Despite being difficult to learn, a data engineering course can be useful for gaining the necessary expertise in the field.

According to a survey conducted by DICE, an online platform that manages one of the largest databases of technology specialists, the position of Data Engineer will experience the fastest growth in the field of technology in 2020, with a growth rate of over 50% over the previous year. An extensive increase in demand for jobs in data engineering has been detected, according to a recent survey. To develop scalable solutions, you'll draw on your programming and analytical abilities.

The national average salary for a Data Engineer in India is 10,00,000 LPA. A Data Engineer in Jaipur earns an average salary of 5,18,327 LPA. (Glassdoor.com)

There is a lot of room for growth in the data engineering field in terms of knowledge, capability, and income. Aspirants can enroll in the DataMites online Data Engineer Course in Jaipur, where we offer comprehensive instruction for their future job.

Three months and a total of 120 hours of instruction make up the Data Engineer Course in Jaipur. Weekdays and weekends both have training sessions. Any option is there for you to select.

No, a postgraduate degree is not required, although having prior experience in mathematics, statistics, economics, or computer science can be very helpful.

  • The International Association of Business Analytics Certification (IABAC), NASSCOM, and Jain University have all approved DataMites, the world's leading institute for data engineer training.
  • Our courses are being taken by more than 50,000 students.
  • We offer a three-step learning process. To assist the candidates in gaining a sufficient understanding of the material during Phase 1, self-study books and videos will be made available to them. The main stage of intensive live online instruction is phase 2. The projects and placements will then be made public during the third phase.
  • Real-world projects and extremely useful case studies are a part of the entire training program.
  • The IABAC, NASSCOM Future Skills, and JAINx Certifications are yours to keep once the course is complete.
  • You will be given the possibility to intern at the AI business Rubixe, a major worldwide technology company, after completing your course.

With the current discount, you can enroll in the data engineering course online for just 31,395 INR instead of the 42,000 INR that it would normally cost in Jaipur.

In the Indian states of Bangalore, Chennai, Pune, Hyderabad, and Kochi, DataMites® does indeed provide Data Engineer Classroom Courses. Depending on the availability of additional candidates from the exact place, we would be happy to host one in other locations upon the applicants' DEMAND.

With decades of experience in the field and a strong understanding of the material, we're adamant about giving you access to certified, highly experienced trainers.

We provide you with a variety of flexible learning alternatives, such as live online training, self-paced courses, and classroom instruction. Depending on your schedule, you can make a decision.

You can attend DataMites® classes for three months that are connected to any query or revision you want to clear thanks to our Flexi-Pass for Data Engineer training.

The results are immediately available if you take the exam online at exam.iabac.org. IABAC regulations state that issuing an e-certificate takes 7 to 10 business days.

We will grant you certifications from IABAC®, NASSCOM Future Skills, and JAINx, which guarantee your skills' global recognition.

Of course, we'll provide you with a Data Engineer Course Completion Certificate once your training is finished.

Yes. A National ID card, a driver's license, or another form of photo ID is necessary to book the certification exam and provide the participation certificate.

Concerning it is unnecessary. To plan a lesson that fits within your schedule, just contact your professors about the issue. For Data Engineer Training Online in Jaipur, every session will be recorded and published so you can simply catch up on what you missed at your own pace and ease.

Yes, a free trial class will be offered to you so that you can have a taste of what the training entails and how it will be conducted.

You must pay the entire course fee to reserve your seat in the entire program and to schedule your certification exams with IABAC. Your DataMites® relationship manager can help with part payment agreements if you have any special limitations.

At DataMites®.com, you can use your specific certification number to verify all certificates. You can also email care@DataMites®.com as an alternative.

  • Case study-based instruction
  • Model deployment, case study, project, hands-on learning, and theory

It goes without saying that you need to maximize your training sessions. If you need more clarity, you may request a support session, of course.

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

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

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

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

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

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