CERTIFICATION AUTHORITIES

COURSE FEATURES

de LEAD MENTORS

DATA ENGINEER COURSE FEES IN CHANDIGARH

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 CHANDIGARH

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 CHANDIGARH

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 CHANDIGARH

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.

Chandigarh, the capital of Punjab and Haryana, is India's first planned metropolis. This self-contained city is a remarkable blend of architecture, natural beauty, wildlife, heritage, and civilization, and it has shown itself worthy of the title of the best Indian city.

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 Chandigarh!

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

ABOUT DATA ENGINEER COURSE IN CHANDIGARH

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 Chandigarh 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 Chandigarh 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.

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.

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.

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 CHANDIGARH

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.

Furthermore, the national average salary for a Data Engineer in India is INR 8,65,008 per year! (Payscale) A data engineer in Chandigarh receives an annual salary of 10,62,806 INR! (Indeed.com)

The Chandigarh-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 Chandigarh at DataMites®, where we offer comprehensive instruction for their future careers.

The Data Engineer Training in Chandigarh 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.

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 Chandigarh 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.

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 Chandigarh, each session will be recorded and published so that you can simply catch up on what you missed at your own pace and ease.

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.

  • 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;

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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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