Master
Data Engineering
With Industry Experts
Comprehensive Data Engineering program covering Python, Statistics, Database Essentials, Big Data, Data Wrangling, NumPy, and Pandas with intensive 1-month classroom/LVC training + 2 months of LIVE project mentoring and unlimited access to the Data Science Cloud Lab for hands-on practice.
- 3+ Live Projects
- Skill AI Certificate
(21,693 reviews)
Choose Your Learning Path
Blended Learning
Self Learning + Live Mentoring
₹23,005
- Self Learning + Live Mentoring
- NASSCOM® Certification
- 1 Year Access To Elearning
- 10 Capstone & 1 Client Project
- Job Assistance
- 24*7 Leaner assistance and support
Live Virtual
₹48,005
- NASSCOM® Certification
- 6-Month | 200 Learning Hours
- 80-Hour Live Online Training
- 10 Capstone & 1 Client Project
- 365 Days Flexi Pass + Cloud Lab
- Internship + Job Assistance
Classroom
₹55,005
- NASSCOM® Certification
- 6-Month | 200 Learning Hours
- 32-Hour Classroom Sessions
- 10 Capstone & 1 Client Project
- Cloud Lab Access
- Internship + Job Assistance
Tools & Technologies You'll Master
TensorFlow
PyTorch
Keras
SciPy
NLTK
NumPy
Pandas
Scikit Learn
Matplotlib
Seaborn
Why Skill AI Institute?
Expert Trainers
Ph.ds And Industry Experts
Elite faculty from prestigious
Universities with deep research
And coaching expertise
Career Guidance
Expert Counselors
Personalized counselling for career Enhancement in managerial roles
Specialized Syllabus
Specialized Syllabus For Managers
Focused on data science for decision making, Managing data science projects with essential technical overview
5 Case Studies
Practical Decision-making Cases
Techniques for scenarios with certainty, Low uncertainty and high certainty from Decision tree to monte carlo simulation
Flexible Financing Options
We’re dedicated to making our programs accessible. Pay in easy installments at 0% interest with no hidden costs.
EMI Available
Bajaj Finserv & ShopSe
Admission Closing
31st December 2026
Course Syllabus
Data Engineering Foundation – 4 Modules
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.
Python Foundation – 5 Modules
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
Statistics Essentials – 4 Modules
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
• Multi stage Sampling
• 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 & Properties
• Z Value / Standard Value
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
Module 4: Hypothesis Testing
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• 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
Data Engineering On Cloud (Aws & Azure) – 10 Modules
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
Data Engineering Associate – 5 Modules
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
• Data Warehouse vs Data Lake
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
Version Control With Git – 6 Modules
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
Database: Sql And Mongodb – 6 Modules
Module 1: Database Introduction
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
Module 2: Sql Basics
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
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
• Self Join, Cross join
• Windows function: Over, Partition, Rank
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
Big Data Foundation – 3 Modules
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
Module 3: Pyspark Foundation
• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs
Big Data Advance – 3 Modules
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 2: Kafka And Spark
• Creating an HDFS cluster with containers
• Creating pyspark cluster with containers
• Processing data on hdfs cluster with pyspark cluster
Business Intelligence Associate – 4 Modules
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
TESTIMONIALS
What Our Students Have To Say
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.
Because of this, I am attracting many companies for job interviews on LinkedIn.
Truly impressed with their support and guidance.