CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN GANGTOK

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

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

Enquire Now

UPCOMING DATA ENGINEER ONLINE CLASSES IN GANGTOK

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.

images not display images not display

WHY DATAMITES INSTITUTE FOR DATA ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN GANGTOK

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 GANGTOK

Data engineering is the secret sauce that turns raw data into a valuable resource, propelling businesses to new heights of success. Behind every data-driven organization, there's a team of talented data engineers who weave their magic, leveraging cutting-edge technologies to build efficient data pipelines. The global data engineering market is poised for unprecedented growth, with analysts forecasting a market size of $117.22 billion by 2027. This remarkable surge is driven by the increasing realization that data holds the key to unlocking innovation, efficiency, and competitive advantage.

Experience the DataMites Data Engineer Course in Gangtok, a comprehensive 6-month program designed to equip participants with the essential skills and knowledge for a successful career in data engineering. With a focus on hands-on learning, this course offers over 150 learning hours, including 50+ hours of live online training. Participants will engage in 10 capstone projects and 1 client project, allowing them to apply their knowledge to real-world scenarios. The course provides a flexible learning experience with a 365-day Flexi Pass, granting access to course materials and a Cloud Lab for practical exercises. For those who prefer offline learning, DataMites also offers Data Engineer Offline Courses On Demand in Gangtok to cater to diverse learning preferences.

Discover the top reasons to choose DataMites for Data Engineer Training in Gangtok. 

  • Led by industry expert Ashok Veda and a team of highly qualified faculty, the program offers a comprehensive curriculum that covers essential data engineering topics. 

  • Graduates receive globally recognized certifications such as IABAC, NASSCOM FutureSkills Prime, and JainX, boosting their career prospects. 

  • Flexibility is key, as participants can balance their personal and professional commitments while pursuing the course, by opting for online data engineer training in Gangtok or data engineer offline training in Gangtok. 

  • Real-world data projects provide practical experience, while a data engineer course with internship opportunity in Gangtok, data engineer training with placement assistance, and job references support career growth.

  •  Hardcopy learning materials and books are provided, and participants can join the exclusive DataMites Learning Community for collaboration. 

  • The program is affordable, and scholarships are available to make it accessible to a wider audience.

Nestled amidst the breathtaking Himalayan mountains, Gangtok is the captivating capital city of Sikkim, offering a serene and picturesque setting. Known for its natural beauty, vibrant monasteries, and warm hospitality, Gangtok attracts visitors from around the world. The city provides an ideal environment for focused learning and personal growth. With well-established connectivity by road, rail, and air, Gangtok is easily accessible from major cities in India. Its pleasant weather throughout the year makes it an inviting destination for study and exploration. Immerse yourself in the city's rich cultural heritage and experience the joy of learning in the peaceful ambiance of Gangtok.

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

ABOUT DATA ENGINEER COURSE IN GANGTOK

Data engineering encompasses the design, construction, and management of systems and processes to enable the efficient collection, storage, processing, and analysis of large volumes of data. It involves creating effective data pipelines, ensuring data quality and reliability, and supporting data-driven decision-making.

The specific requirements for joining a Data Engineer Course in Gangtok may vary depending on the course and training provider. Generally, having a basic understanding of programming, databases, and data concepts is beneficial. Some courses may also recommend a background in computer science or a related field.

A career in data engineering typically requires a strong educational background in computer science, information technology, or a related field. While a bachelor's degree is often the minimum requirement, some positions may prefer or require a master's degree, especially for more advanced or research-oriented roles.

Yes, data engineering is considered to have a promising future. With the increasing importance of data-driven decision-making and the exponential growth of data, the demand for skilled data engineers is expected to rise. Data engineering plays a critical role in effectively managing and deriving insights from data assets.

After completing Data Engineer Training, individuals can pursue various career paths, including roles such as Data Engineer, Data Architect, ETL Developer, Data Warehouse Manager, Big Data Engineer, Database Administrator, or Cloud Data Engineer. These roles involve designing and managing data infrastructure, developing data pipelines, and ensuring efficient data processing and storage.

Data Engineer Training offers several advantages, including comprehensive knowledge of data engineering concepts, practical skills in data pipeline development and management, improved career prospects in the growing field of data engineering, and staying up-to-date with industry practices and technologies.

Various aspects, including the curriculum, expertise of faculty members, industry connections, reviews from former students, and training delivery options, should be considered when selecting the optimal institute for data engineering training. DataMites is widely recognized as one of the premier institutes in this field. With its comprehensive curriculum, hands-on projects that reflect real-world scenarios, and highly skilled instructors, the institute ensures a strong understanding of data engineering concepts, tools, and techniques.

Data Engineer Training in Gangtok may have varying costs depending on factors such as the chosen institute, duration of the program, mode of delivery (online or classroom), and additional offerings. Typically, the fees for data engineer training in Gangtok fall within the range of 40,000 INR to INR 1,00,000.

No, DevOps and data engineering are not interchangeable terms. DevOps refers to a set of practices that combines software development and IT operations to achieve faster and more reliable software delivery. Data engineering, on the other hand, specifically focuses on managing and processing data to support data-driven decision-making and analytics. While there may be some overlap in skills and concepts, they are distinct disciplines within the technology field.

While a postgraduate degree is not necessarily mandatory for Data Engineer Training, it can be advantageous for individuals seeking advanced knowledge and research skills in data engineering. However, a bachelor's degree in computer science, information technology, or a related field is often sufficient to start a career in data engineering.

FAQ’S OF DATA ENGINEER COURSE IN GANGTOK

There are various options for acquiring data engineering training in Gangtok, such as enrolling in reputable institutes like DataMites®. These institutes offer comprehensive data engineering courses in Gangtok through online or classroom modes, providing hands-on training, practical projects, and expert guidance to develop your data engineering skills.

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

The cost of DataMites Data Engineer Training in Gangtok can vary, ranging from approximately INR 26,548 to INR 68,000.

Yes, upon successful completion of the Data Engineer training from DataMites®, you will receive certifications. DataMites offers globally recognized certifications from organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX in collaboration with Jain (Deemed-to-be) University, which validate your expertise in data engineering.

The eligibility criteria for enrolling in the Data Engineer Course in Gangtok 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 specific documents required for the training session at DataMites may vary based on the course and program. Typically, participants are advised to bring 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 duration to become certified by the IABAC (International Association of Business Analytics Certifications) can vary depending on the specific certification and individual 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 DataMites Certified Data Engineer Training in Gangtok covers a wide range of topics, including data engineering concepts, tools, and technologies like 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.

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.

View more

DATA ENGINEER PROJECTS

DATA ENGINEER JOB INTERVIEW QUESTIONS

OTHER DATA ENGINEER TRAINING CITIES IN INDIA

Global CERTIFIED DATA ENGINEER COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog