DATA ENGINEER COURSE IN AGARTALA

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
• Managing Big data

MODULE 3: DATA INTEGRITY AND PRIVACY

• Data integrity basics
• Various aspects of data privacy
• Various data privacy frameworks and standards
• Industry related norms in data integrity and privacy: data engineering perspective

MODULE 4: DATA ENGINEERING ROLE

• Who is a data engineer?
• Various roles of data engineer
• Skills required for data engineering
• Data Engineer Collaboration with Data Scientist and other roles.

 

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• String functions part 
• String functions part 
• Python Operators

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement, IF-ELSE
• NESTED IF
• Python Loops Basics, WHILE Statement
• BREAK and CONTINUE statements
• FOR statements

MODULE 3: PYTHON PACKAGES

• Introduction to Packages in Python
• Datetime Package and Methods

MODULE 4: PYTHON DATA STRUCTURES

• Basic Data Structures in Python
• Basics of List
• List methods
• Tuple: Object and methods
• Sets: Object and methods
• Dictionary: Object and methods

MODULE 5: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

MODULE 1 : OVERVIEW OF STATISTICS 

• Introduction to Statistics: Descriptive And Inferential Statistics
• a.Descriptive Statistics
• b.Inferential Statistis
• Basic Terms Of Statistics
• Types Of Data

MODULE 2 : HARNESSING DATA 

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

MODULE 3 : EXPLORATORY DATA ANALYSIS 

• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies, Measure of Spread
• 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 Minkowski Distance
• Covariance and Correlation

MODULE 4 : HYPOTHESIS TESTING 

• Hypothesis Testing Introduction 
• Types of Hypothesis
• P- Value, Crtical Region
• Types of Hypothesis Testing: Parametric, Non-Parametric
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis Testing (Proposed)

MODULE 1: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• Data Lake house
• 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 architecture
• Data Warehouse vs Data Lake

MODULE 2: 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 3: DOCKER AND KUBERNETES FOUNDATION

• Docker Introduction
• Docker Vs.VM
• Hands-on: Running our first container
• Common commands (Running, editing,stopping,copying and managing images)YAML(Basics)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers 
• Docker swarm vs kubernetes

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 : AWS DATA SERVICES INTRODUCTION 

• AWS Overview and Account Setup
• AWS IAM Users, Roles and Policies
• AWS S overview
• AWS EC overview
• AWS Lamdba overview
• AWS Glue overview
• AWS Kinesis overview
• AWS Dynamodb overview
• AWS Athena overview
• AWS Redshift overview

MODULE 2 : DATA PIPELINE WITH GLUE

• AWS Glue Crawler and setup
• ETL with AWS Glue
• Data Ingesting with AWS Glue

MODULE 3 : DATA PIPELINE WITH AWS KINESIS 

• AWS Kinesis overview and setup
• Data Streams with AWS Kinesis
• Data Ingesting from AWS S 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 Blob Storage

MODULE 7: AZURE DATA FACTORY

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

MODULE 8 : AZURE DATABRICKS

• Azure databricks introduction
• Azure databricks architecture
• Data Transformation with databricks

MODULE 9 : AZURE RDS

• Creating a Relational Database
• Querying in and out of Relational Database
• ETL from RDS to databricks

MODULE 10 : AZURE RDS

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

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

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
• Key Terms: Output Format
• Partitioners Combiners Shuffle and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Resilient distributed datasets (RDD),Working with RDDs in PySpark, Spark Context , Aggregating Data with Pair RDDs
• Spark Databricks
• Spark Streaming

MODULE 1: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 2: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

MODULE 3: KAFKA and Spark

• Creating an HDFS cluster with containers
• Creating pyspark cluster with containers
• Processing data on hdfs cluster with pyspark cluster

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

MODULE 2: POWER-BI Basics

• Power BI Introduction 
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 3: DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 4: CONNECTING TO VARIOUS SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

 

 

 

 

 

 

 

 

 

 

 

 

 

The field of data engineering involves the design, development, and management of systems and processes for handling and analyzing large volumes of data. Data engineers build and maintain data pipelines, databases, and infrastructure, ensuring data quality, reliability, and accessibility for organizations to gain insights and make informed decisions based on data.

The educational qualifications required for a career in data engineering typically include a bachelor's or master's degree in computer science, information technology, data science, or a related field. However, practical experience and relevant certifications can also be valuable.

Yes, data engineering is considered an IT-related occupation as it involves working with data infrastructure, databases, programming languages, and related technologies to ensure effective data management and analysis.

The eligibility criteria for enrolling in a Data Engineer Course in Agartala may vary depending on the training provider. Generally, a basic understanding of computer programming, databases, and data processing concepts is beneficial.

The cost of Data Engineer Training in Agartala can vary depending on factors such as the training provider, course duration, and delivery mode (online or classroom). The cost of data engineering training in Agartala typically ranges from 40,000 INR to 1,00,000 INR, depending on factors such as the institute, program duration, and level of instruction. To find accurate information about training costs, it is recommended to research and compare various training providers in Agartala.

After completing Data Engineer Training, job prospects can include roles such as Data Engineer, Database Administrator, ETL Developer, Data Integration Specialist, or Big Data Engineer. Opportunities can be found in various industries that deal with large volumes of data, such as technology, finance, healthcare, and e-commerce.

A certified data engineer course typically covers a comprehensive curriculum that includes topics like data modeling, database design, data integration, ETL processes, data warehousing, big data technologies, cloud platforms, and data governance. It aims to provide the necessary skills and knowledge to work as a professional data engineer.

Both data engineering and data science are distinct fields with their own focus areas. Data engineering primarily deals with building and managing data infrastructure and pipelines, while data science focuses on extracting insights and building predictive models from data. The choice between the two depends on individual interests and career goals.

Data engineering is currently in high demand as organizations across industries are increasingly relying on data-driven decision-making and need professionals to manage and process their data effectively.

The key skills necessary for a successful data engineer include proficiency in programming languages (such as Python, SQL), data modeling and database management, ETL (Extract, Transform, Load) processes, knowledge of big data technologies, familiarity with cloud platforms, problem-solving abilities, and strong communication skills for effective collaboration with cross-functional teams.