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.
Choose Your Learning Path
Flexible Plans Designed to Fit Your Schedule & Learning Style
Select the learning mode that works best for you — self-paced, live online, or in-person classroom training
Blended Learning
Self Learning + Live Mentoring
- Self Learning + Live Mentoring
- NASSCOM & IBM & AAAI Certification
- 1 Year Access to E-learning
- 25 Capstone & 1 Client Project
- Job Assistance
- 24x7 Learner Assistance
Live Virtual
Instructor Led Live Online
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- 365 Days Flexi Pass + Cloud Lab
- Placement Assurance
Classroom
In-Person Classroom Training
- NASSCOM & IBM & AAAI Certification
- 7-Month | 700 Learning Hours
- 120-Hour Live Online Training
- 25 Capstone & 1 Client Project
- Cloud Lab Access
- Placement Assurance
Tools & Technologies
Master Industry Tools
Hands-on training on the tools and technologies used by top data teams
worldwide.
Certification
NASSCOM & IBM & AAAI Certification
Flexible Financing Options
Pay in Easy Installments at 0% Interest
We’re dedicated to making our programs accessible. No hidden costs, transparent pricing
EMI Available
ShopSe
No Cost EMI & Scholarships Available
Our Partners
Companies That Hire Our Graduates
200+ leading companies trust Data Sprint graduates to drive their data
initiatives.
Why Data Sprint
Why Thousands Choose Data Sprint
We’re committed to transforming careers with quality education and real-
world experience.
Course Syllabus
Comprehensive Data Science Curriculum
A structured learning path covering foundations to advanced topics across data science, machine learning, and AI.
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
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