CERTIFIED DATA ENGINEER COURSES

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

Data Engineer Course is designed as job oriented course for Data Engineering roles.  The Data Engineering is the foundation for Data Science work flow, covering data gathering, manipulation, processsing and transforming data to get it read for further Data Science processes. Data Engineer course  apart from covering key data engineering concepts also covers Python Language, Statistics, Big Data popular frameworks. 

Data Engineer course bundled with project mentoring and internship facility.

Data engineering is the process of developing and constructing large-scale data collection, storage, and analysis systems. It's a wide-ranging field with applications in almost every industry.

To become a data engineer, the first and most important step is to get appropriate training in the field. Obtaining a thorough understanding of the data science and data engineering domain through a certification course and thereby upskilling the talents is a must for landing a job in the field.

Attending Data Engineer Courses, which may last anywhere from three to twelve months, can help you become a data engineer. The course curriculum, on the other hand, varies based on the degree or certification desired. 3-month courses can provide you with important Data Engineer experience and internship possibilities, leading to entry-level positions at top businesses.

The Data Engineer Course is the one to take if you want to work in the business because it certifies you as an expert in the field of data science. After finishing our comprehensive programme, you'll have the skills you need to succeed as a data engineer, as well as a job-ready portfolio to show off during the interview process.

A bachelor's degree in computer science, software or computer engineering, applied math, physics, statistics, or a related discipline is required for entry into this field. To even qualify for most entry-level roles, you'll need real-world experience, such as internships.

The cost of Data Engineer Training in the US can be anywhere from 257.68 USD to 1030.71 USD, depending on the course level and type of training you choose. Data Engineer Training in the UK can cost anywhere from 205.15 GBP to 820.60 GBP and the fees for Data Engineer Training in India can range from 20,000 INR to 80,000 INR.

DataMites® is the best institute for comprehensive training in courses in data engineering, data science, artificial intelligence, and other related fields. DataMites® collaborates with world-renowned Data Engineer professionals to build and offer an extensive crafter training curriculum. 

Data engineering isn't always an entry-level role. Instead, many data engineers start off as software engineers or business intelligence analysts. As you advance in your career, you may move into managerial roles or become a data architect, solutions architect, or machine learning engineer.

Some of the essential skills of a data engineer are coding, data warehousing, database system, data analysis, critical thinking, understanding of machine learning and more.

Data engineers face the difficult task of reconciling immediate needs with a longer-term view of where data demands will lead the systems they manage. With each new architecture you create, there's a persistent dread that you've trapped yourself into a technical dead-end. Without a doubt, data is essential for expanding your business and gaining important insights. Data engineering, often known as information engineering, is a software-based strategy for developing information systems.

It was an excellent decision. You've chosen a wealthy, secure, and demanding career path. As of June 2022, there are about 44,209 Data Engineer-related job openings globally. (Indeed.com)  According to a recent poll, there has been a considerable surge in demand for data engineering job positions. You'll utilise your programming and problem-solving skills to create scalable solutions.

DataMites® Data Engineer Courses are carefully crafted to teach Data Engineering from scratch. The course henceforth can be taken by anyone. This career path is for those who are searching for a career shift, data professionals who want to expand their skill set for the next promotion, and college students who want to get a job.

In the data engineering domain there is a lot of room for advancement in terms of learning, capacity, and pay. Aspirants can enrol at DataMites® for Data Engineer Course Online, we provide in-depth training for your further career.

The duration of the Data Engineer Course is 6 months, totalling 120 hours of training. Training sessions are imparted on weekdays and weekends. You can choose any as per your availability.

No, a PG degree is not necessary but having prior knowledge of Mathematics, Statistics, Economics or Computer Science can be highly beneficial.

• DataMites®™ is the global institute for Data Engineer Training accredited by the International Association of Business Analytics Certification (IABAC), NASSCOM and Jain University.
• We have more than 50,000 students enrolled in the courses we offer.
• We provide a three-step learning method. In Phase 1, self-study videos and books will be provided to the candidates to help them get adequate knowledge about the syllabus. Phase 2 is the primary phase of intensive live online training. And in the third phase, we will release the projects and placements.
• The entire training includes real-world projects and highly valuable case studies.
• After the training, you will receive the IABAC, NASSCOM Future Skills and JAINx Certifications. 
• After completing your training, you will get the chance to do an internship with AI company Rubix, a global technology company.

The cost of Data Engineer training in the United States can range from 567.01 USD to 201.61 USD, depending on the course level and type of training you choose. In Europe, the cost of a Data Engineer training course can range from 4187.38 Euro to  526.98 Euro. 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.

Yes, DataMites® do provide Data Engineer Classroom Courses in the Indian states of Bangalore, Chennai, Pune, Hyderabad and Kochi. We would be pleased to host one in other locations, ON-DEMAND of the applicants as according to the availability of other candidates from the exact location.

We are determined to provide you with trainers who are certified and highly qualified with decades of experience in the industry and well versed in the subject matter.