CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN DISPUR

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 DISPUR

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 DISPUR

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 DISPUR

The data engineering landscape is witnessing substantial expansion. A Market Study Report predicts that the global data engineering market will reach $112.53 billion by 2027, registering a CAGR of 21.8% from 2021 to 2027. The report attributes this growth to the increasing adoption of cloud-based data solutions, the proliferation of data-centric technologies, and the growing focus on data-driven decision-making across industries.

DataMites offers a comprehensive Data Engineer course in Dispur, designed to provide students and professionals with the necessary skills and knowledge required for a successful career in data engineering. The course spans over 6 months and consists of more than 150 learning hours. It includes 50+ hours of live online training, allowing participants to interact with expert instructors in real-time. The course also features 10 capstone projects and 1 client project, offering hands-on experience in solving real-world data engineering challenges. 

Additionally, learners receive a 365-day Flexi Pass, which provides access to the course materials and Cloud Lab for continuous learning and practice. For individuals who prefer offline learning, DataMites also offers Data Engineer courses on demand in Dispur. These courses provide the flexibility to learn at one's own pace, with pre-recorded lectures and comprehensive study materials.

There are several reasons to choose DataMites for Data Engineer training in Dispur. 

  • Firstly, DataMites boasts experienced trainers, including renowned expert Ashok Veda and a dedicated faculty team. 

  • The course curriculum is comprehensive, covering all the essential topics and technologies required to excel as a data engineer. 

  • Upon successful completion of the course, participants receive globally recognized certifications from prestigious organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX.

  • DataMites offers flexible learning options including online data engineer course in Dispur and data engineer courses offline in Dispur, allowing individuals to balance their training with other commitments.

  • The course includes projects that involve working with real-world data, enabling learners to gain practical experience. Data Engineer Internship opportunities are also provided to further enhance their skills and industry exposure. 

  • DataMites offers data engineer courses with placement assistance and job references to help students kickstart their careers in data engineering. 

  • Participants receive hardcopy learning materials and books to support their studies. DataMites also provides an exclusive learning community where learners can collaborate and network with peers and industry professionals. 

  • The courses are affordably priced, and scholarships are available to deserving candidates.

Dispur is the capital city of the state of Assam, India. It is a bustling city with a rich cultural heritage and serves as an important administrative and educational center. Dispur offers a favorable environment for learning, with various educational institutes and training centers. Pursuing a Data Engineer certification in Dispur can open doors to exciting career opportunities in the field of data engineering, both locally and globally. The city's proximity to other major cities and its growing technology sector make it an attractive location for professionals looking to build a successful career in data engineering.

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

ABOUT DATA ENGINEER COURSE IN DISPUR

Data engineering encompasses the design, development, and maintenance of systems and infrastructure to collect, process, store, and manage large volumes of data. It involves tasks such as data ingestion, data transformation, database management, and data pipeline creation.

The requirements for enrolling in a Data Engineer Course in Dispur may vary depending on the institute and program. Generally, a basic understanding of programming, databases, and data concepts is beneficial. Some courses may have prerequisites like knowledge of SQL, Python, or familiarity with data manipulation techniques.

The cost of Data Engineer Training in Dispur can vary depending on factors such as the institute, program duration, delivery mode (online or classroom), and additional features. Typically, the fees for data engineer training in Dispur range from 40,000 INR to INR 1,00,000.

The choice of the best institute depends on factors like the curriculum, faculty expertise, industry connections, alumni reviews, and training delivery modes. Datamites is considered one of the best institutes for data engineering training. With comprehensive curriculum, industry-relevant projects, and experienced instructors, the institute provides a strong foundation in data engineering concepts, tools, and techniques.

Data Science and Data Engineering are related fields but have distinct focuses. Data Science primarily deals with extracting insights and knowledge from data through statistical analysis and machine learning techniques. Data Engineering, on the other hand, focuses on building and maintaining the infrastructure and pipelines to process, store, and prepare data for analysis.

Data Engineering is a recommended career path for individuals interested in working with data and technology. The demand for skilled data engineers is growing rapidly as organizations increasingly rely on data-driven decision-making. It offers diverse opportunities, competitive salaries, and the chance to work on cutting-edge technologies.

The field of Data Engineering has promising prospects. With the exponential growth of data and the increasing need for efficient data processing and analysis, data engineers play a crucial role in organizations across various industries. There is a high demand for professionals who can design and manage data infrastructure and pipelines.

While prior experience can be advantageous, individuals with no prior experience can still secure entry-level Data Engineer job positions. Starting as a junior data engineer or intern and gradually gaining experience and skills through practical projects and on-the-job learning can pave the way for career growth in the field.

Python is widely used in the context of Data Engineering due to its versatility, extensive libraries, and ease of use. It is commonly used for data manipulation, transformation, and scripting tasks. Python frameworks like Apache Spark and libraries like Pandas provide powerful tools for data processing, making it a valuable language for data engineers.

To establish a career as a data engineer in Dispur, you can follow these steps: gain a strong foundation in programming languages like Python and SQL, develop skills in data manipulation and database management, familiarize yourself with big data technologies like Hadoop and Spark, and consider pursuing relevant certifications or joining data engineering training programs to enhance your knowledge and increase your chances of securing job opportunities in the field. Networking and gaining practical experience through internships or projects can also be beneficial.

FAQ’S OF DATA ENGINEER COURSE IN DISPUR

To acquire training in data engineering in Dispur, you can enroll in reputable institutes like DataMites®, which offer comprehensive data engineering courses through online or classroom modes. They provide hands-on training, practical projects, and expert guidance to develop your skills in data engineering.

The DataMites Certified Data Engineer Training in Dispur covers a wide range of topics including data engineering concepts, tools, and technologies such as Hadoop, Spark, and SQL. The training program includes interactive sessions, practical assignments, and real-world case studies to enhance your understanding and proficiency in data engineering.

Eligibility criteria for enrolling in the Data Engineer Course in Dispur at DataMites® may vary. Generally, individuals with a background in computer science, mathematics, or related fields, as well as professionals aspiring to work in data engineering roles, are eligible to enroll.

The duration of the DataMites Data Engineer Course in Dispur can vary depending on the chosen learning mode. Generally, for online instructor-led training, it is typically around 6 months with 150+ learning hours, while the duration may differ for self-paced learning options.

The data engineer course fee at DataMites in Dispur can vary from around INR 26,548 to INR 68,000.

  • IABAC (International Association of Business Analytics Certifications): You will receive a globally recognized certification from IABAC, validating your expertise in data engineering.
  • NASSCOM FutureSkills Prime: DataMites is an authorized training partner of NASSCOM FutureSkills. Completing the Data Engineer training program will earn you a certification accredited by NASSCOM, which is widely recognized in the industry.
  • JainX: DataMites has collaborated with Jain (Deemed-to-be) University to offer certification. You will be awarded a certification from JainX, further showcasing your proficiency in data engineering.

The duration to become certified by the IABAC (International Association of Business Analytics Certifications) can vary depending on the specific certification and the individual's preparation. It typically requires rigorous study, practical experience, and successfully passing the certification examination.

Yes, upon successful completion of the Data Engineer Course from DataMites®, you will receive a Data Engineer Course Completion Certificate. This certificate acknowledges your successful completion of the course and serves as evidence of your proficiency in data engineering concepts and techniques.

The specific documents required for the training session at DataMites may vary based on the course and program. Typically, participants are advised to carry a valid ID proof, such as a government-issued ID card, and any specific documents mentioned in the communication received from DataMites.

DataMites® accepts various payment methods for their training programs, including online payment through credit cards, debit cards, net banking, and other digital payment options.

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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