CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN KOTA

Live Virtual

Instructor Led Live Online

110,000
63,945

  • 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

55,000
36,645

  • 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

110,000
69,195

  • 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 KOTA

BEST CERTIFIED DATA ENGINEER CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN KOTA

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 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

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 : 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 PIPELINE WITH AWS KINESIS 

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

MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT 

  • AWS Redshift Overview
  • Analyze data using AWS Redshift from warehouses, data lakes and operations DBs
  • Develop Applications using AWS Redshift cluster
  • AWS Redshift federated Queries and Spectrum

MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE 

  • Azure Synapse setup
  • Understanding Data control flow with ADF
  • Data Pipelines with Azure Synapse
  • Prepare and transform data with Azure Synapse Analytics

MODULE 6 : STORAGE IN AZURE 

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

MODULE 7: AZURE DATA FACTORY

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

MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS

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

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 : DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2 : SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3 : DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4 : DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5 : SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6 : SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• 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

DATA ENGINEER TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ENGINEER TRAINING IN KOTA

In the digital universe, data engineers are the unsung superheroes who tame the wild data frontier, extracting insights from the chaos. Armed with their expertise in building scalable data architectures and employing advanced analytics tools, they navigate through the vast expanse of information to uncover hidden patterns and valuable knowledge. The data engineering market is set to skyrocket, with experts predicting a market value of $117.22 billion by 2027. These figures paint a picture of a world where data engineers are in high demand, revolutionizing industries and shaping the future of business.

The DataMites Data Engineer Course in Kota is a comprehensive program designed to equip participants with the skills and knowledge required for a career in data engineering. This 6-month course offers over 150 learning hours, providing a deep dive into the intricacies of data engineering. The course includes 50+ hours of live online training, where experienced instructors guide participants through the key concepts and techniques of data engineering. Additionally, participants will have the opportunity to work on 10 capstone projects and 1 client project, enabling them to apply their learning to real-world scenarios. With a 365-day Flexi Pass, participants have the flexibility to access course materials and resources at their own convenience. The Cloud Lab provides a practical environment for hands-on experimentation with data engineering tools and technologies. For those who prefer offline learning, DataMites also offers Data Engineer Offline Courses On Demand in Kota, catering to diverse learning preferences.

There are numerous reasons to choose DataMites for Data Engineer Training in Kota. 

  • The course is led by industry expert Ashok Veda and a team of highly qualified faculty members who bring their expertise and practical insights to the learning experience. 

  • The comprehensive course curriculum covers a wide range of topics, ensuring participants develop a strong foundation in data engineering.

    • Successful completion of the course leads to globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing career prospects. 

  • The program offers flexible learning options including online data engineer courses in Kota and data engineer offline training in Kota, allowing participants to balance their professional and personal commitments while pursuing the course. 

    • Projects with real-world data provide hands-on experience, enabling participants to tackle real-life data engineering challenges. A data engineer course internship opportunity is available to gain industry exposure, and data engineer training with placement assistance and job references are provided to support participants in their career advancement. 

  • Hardcopy learning materials and books are provided for offline studying, and participants can join the DataMites Exclusive Learning Community to collaborate and share knowledge. 

  • The course is priced affordably, and scholarships are available to make it accessible to a wider audience.

Kota, located in the state of Rajasthan, India, is a historic city known for its rich cultural heritage and educational institutions. The city's educational ecosystem and infrastructure make it an ideal destination for learning and skill development, including data engineering. The city's vibrant markets and delectable Rajasthani cuisine, including popular dishes like Dal Bati Churma, add to the cultural experience. Kota's strategic location and connectivity through rail and road networks make it easily accessible for learners. With its blend of educational opportunities, historical significance, and cultural richness, Kota provides an inspiring backdrop for pursuing a Data Engineer Certification.

Along with the data engineer courses, DataMites also provides data analytics, deep learning, mlops, machine learning, IoT, data analyst, artificial intelligence, AI expert, data mining, python training, tableau, r programming and data science courses in Kota.

ABOUT DATA ENGINEER COURSE IN KOTA

Data engineering involves the design, development, and maintenance of systems and processes for collecting, organizing, and analyzing large volumes of data. It requires technical expertise in programming, database management, data modeling, and ETL processes. While a background in IT can be beneficial, it is possible for individuals without an IT background to become data engineers by acquiring relevant skills through training and practical experience.

Yes, it is possible for someone without an IT background to become a data engineer. While a background in IT or computer science can provide a strong foundation, it is not a mandatory requirement. Many individuals from diverse educational backgrounds, such as mathematics, statistics, engineering, or business, have successfully transitioned into data engineering roles.

There is a high demand for data engineering professionals due to the increasing reliance on data-driven decision-making in various industries. As organizations collect and analyze vast amounts of data, the need for skilled data engineers who can ensure data quality, storage, and accessibility is on the rise.

Yes, data engineering is considered a promising career choice for the future. With the exponential growth of data in various industries, there is a rising demand for professionals who can effectively manage, process, and analyze data. Data engineers play a crucial role in building and maintaining data infrastructure, ensuring data quality and integrity, and enabling data-driven decision-making. As organizations continue to rely on data for insights and innovation, the demand for skilled data engineers is expected to remain high, offering abundant opportunities for career growth and advancement.

The prerequisites for enrolling in a Data Engineer Course in Kota may vary depending on the training provider. However, a basic understanding of programming concepts and familiarity with databases and SQL is usually beneficial.

The fees for data engineering training in Kota may differ based on factors such as the training institute, program duration, and level of instruction. Typically, the cost of data engineer training in Kota can range from approximately 40,000 INR to INR 1,00,000. To determine the exact fees for specific courses, it is recommended to conduct research and gather information from different training providers in Kota.

After completing Data Engineer Training, job opportunities can include roles such as Data Engineer, Database Developer, ETL Developer, Data Analyst, or Big Data Engineer. The demand for data engineering professionals is expected to continue growing across industries.

Key skills necessary for success as a data engineer include proficiency in programming languages (such as Python, SQL), database management, ETL processes, data modeling, problem-solving, and strong analytical and communication skills.

Transitioning from a mechanical domain to data engineering is feasible with the acquisition of relevant skills and knowledge in areas such as programming, databases, and data processing. Building a foundation in data engineering concepts and gaining hands-on experience through training and projects can help facilitate the transition.

Emerging trends in the field of data engineering include the adoption of cloud-based data platforms, big data technologies, machine learning, and AI integration, as well as the increasing focus on data privacy and security measures. Keeping up with these trends can enhance career prospects and opportunities in the field of data engineering.

FAQ’S OF DATA ENGINEER COURSE IN KOTA

DataMites® offers comprehensive data engineer training programs designed to equip individuals with the skills and knowledge needed to excel in the field of data engineering. Their training covers key concepts such as data integration, ETL processes, data modeling, and big data technologies. With experienced faculty and a practical hands-on approach, DataMites® ensures that participants gain the necessary expertise to tackle real-world data engineering challenges. Whether you are a beginner or a professional looking to enhance your skills, DataMites® provides a valuable learning experience to kickstart or advance your data engineering career.

Graduates: The course is open to individuals who have completed their graduation in any field.

Working Professionals: Professionals who are already working in the IT industry or related fields can also enroll in the course to enhance their skills and career prospects.

Non-IT Professionals: Even if you do not have an IT background, you can still participate in the course as long as you have a strong interest and willingness to learn data engineering.

Students: Students who are in their final year of graduation or pursuing higher education can also join the course to gain knowledge and skills in data engineering.

Career Switchers: Individuals who are looking to transition their career into the field of data engineering can enroll in the course to acquire the necessary skills and knowledge.

The duration of the DataMites Data Engineer Course in Kota depends on the learning mode selected, with online instructor-led training lasting around 6 months and comprising 150+ learning hours.

Flexibility: Online training allows you to learn at your own pace and schedule, giving you the flexibility to balance your learning with other commitments.

Convenience: You can access the training materials and participate in the classes from anywhere with an internet connection, eliminating the need for travel and saving time.

Expert Instructors: DataMites® ensures that their online training programs are led by experienced instructors who are industry professionals, providing you with quality education and practical insights.

Interactive Learning: Online training often includes interactive sessions, discussions, and hands-on exercises, allowing you to engage with the material and enhance your learning experience.

Access to Resources: With online training, you have access to a wide range of resources, including recorded lectures, study materials, and online forums, enabling you to deepen your understanding of data engineering concepts.

Networking Opportunities: Online training programs may provide networking opportunities with fellow learners and industry experts through virtual forums and discussion boards, allowing you to expand your professional network.

Cost-Effective: Online training is often more cost-effective compared to in-person training, as it eliminates travel and accommodation expenses associated with attending physical classes.

The DataMites Certified Data Engineer Training program in Kota covers a wide range of topics in its curriculum, including:

  • Data modeling and database design

  • Data integration and ETL processes

  • Big Data technologies like Hadoop and Spark

  • Data warehousing and dimensional modeling

  • Advanced analytics and machine learning algorithms for data engineering

The DataMites Data Engineer Training in Kota is priced competitively, with fees ranging from INR 26,548 to INR 68,000, depending on the selected learning mode and course features.

DataMites® offers classroom-based Data Engineer courses in Kota, providing students with the opportunity to participate in in-person training sessions ON DEMAND with experienced instructors and engage in interactive learning.

The Flexi-Pass concept offered by DataMites® provides flexibility and convenience to learners. With a Flexi-Pass, students can access multiple courses within a specific period, allowing them to explore different topics and acquire diverse skills.

Upon successful completion of the Data Engineer training program from DataMites®, you will be awarded certifications from esteemed organizations like IABAC, Jain (Deemed-to-be University), and NASSCOM FutureSkills Prime, recognizing your proficiency in data engineering.

The instructor for the Data Engineer Course in Kota at DataMites® is a highly experienced and knowledgeable professional with expertise in data engineering. They possess in-depth industry knowledge and are skilled in delivering effective training sessions to help you gain the necessary skills and knowledge in data engineering.

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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