CERTIFICATION AUTHORITIES

COURSE FEATURES

de LEAD MENTORS

DATA ENGINEER COURSE FEES IN KANPUR

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 KANPUR

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 KANPUR

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 KANPUR

The DataMites® Data Engineer Course is meant to encompass all components of data engineering with Python, as well as statistics, database basics, Big Data, Data Wrangling, Numpy, Pandas, and other relevant topics. The demand for qualified Data Engineers is stretching as data becomes all-pervasive.

The Data Engineer Training Course includes a two-month real-time project as well as an internship opportunity to provide hands-on experience and exposure to the real world. Because the Data Engineer course covers topics from the ground up, there are no strict prerequisites.

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

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 features live instructor-led training and in-person classroom learning options in multiple time zones. Both weekends and weekdays are suitable for the training.

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

The field of data science receives a lot of attention and buzz. However, we've witnessed an increase in interest in using our technical skills testing platform for data engineering opportunities in recent months.

“Everything is going to be connected to cloud and data, all of this will be mediated by software.”

         -  Satya Nadella

What Are the Benefits of Data Engineer Training?

  1. The foundation 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 solely motivated by a desire to make data scientists' jobs easier. Data engineers, without a doubt, are having an increasing impact on society.
  4. If you want to work in the field of data science, this is an important skill to be capable of.
  5. A wealthy job with a top-level job security

Data engineering, often known as information engineering, is a method of designing information systems using the software. Analytics teams must demonstrate the long-term value that engineering expertise can bring to the table to work efficiently with organizations and persuade them to invest in data engineering, regardless of current analytics competence. Data engineers may help organizations "unlock" data science and analytics, as well as create well-curated, accessible data foundations.

While the job growth in AI Engineering and pay are both attractive, it's a good idea to know what to anticipate from a profession before jumping in.

The DataMites® Data Engineering Course is a great way to get started 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.

Data Engineer Course Learning Advantages:

  1. Learn the basics of data engineering.
  2. Understand the Data Engineering Ecosystem and Lifecycle.
  3. Pick up how to extract data from several different files and databases.
  4. Using diverse skills and tactics, learn how to clean, edit, and improve your data.
  5. In relational and NoSQL databases, learn how to work with different file types.
  6. Learn how to establish dashboards to track progress and set up a data pipeline.
  7. Understand how to scale data pipelines in a real-world setting.

Every firm nowadays is mining data for the goals of growth and development. Companies use data not simply to make strategic decisions, but also for extensive research-based campaigns and programs.

Industries are moving toward a data-driven strategy to figure out what their consumers want and how well they're performing in the market. Data Engineers are in high demand as a result of this. According to a recent poll, demand for data engineering employment openings has increased significantly.

Data engineers are one of the top three analytical professions in the Indian market, according to industry studies. At least 7,500 employment openings exist for these well-paid analytical talents. Companies are employing twice as many data engineers as data scientists, with pay ranging from 20% to 30% higher.

Kanpur is a metropolitan city in Uttar Pradesh, a state in India. It is today known for its colonial architecture, gardens, and parks, as well as high-quality leather items that are primarily sold in the Western world. Kanpur is the largest city in the state and the center of commercial and industrial activities. 

In addition to that, the national average salary for a Data Engineer in India earns an average amount of INR 10,49,170 per year! And the salary for a big data engineer in Kanpur is 7,00,000 LPA. (Indeed)

Even as the job market and salary are both enticing, data suggest that Data Engineer is the fastest-growing career in the technology field, and with our Data Engineering Certification Course in Kanpur, you can get established on your career debut in the Data Engineering domain!

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

ABOUT DATA ENGINEER COURSE IN KANPUR

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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FAQ’S OF DATA ENGINEER COURSE IN KANPUR

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

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

The national average salary for a Data Engineer in India earns an average amount of INR 10,49,170 per year! And the salary for a big data engineer in Kanpur is 7,00,000 LPA. (Indeed)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

You shouldn't stress over it. Simply contact your instructors about it and arrange a class time that works for you. Each session of the Data Engineer Training Online in Kanpur will be filmed and published, allowing you to quickly catch up on the material you missed at your own pace and convenience.

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

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

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

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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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