Master Data Analytics With Industry Experts
Industry-focused No-Code Data Analytics program covering Excel, MySQL, Tableau, and Power BI with 6 months of in-depth training (200+ hours) + real client project experience and internship certification.
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 Analytics Foundation – 6 Modules
Module 1: Data Analysis Foundation
• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain
Module 2: Classification Of Analytics
• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics
Module 3: Crip-dm Model
• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling, Evaluation, Deploying,Monitoring
Module 4: Univariate Data Analysis
• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.
Module 5: Data Analysis With Visual Charts
• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot
Module 6: BI-variate Data Analysis
• Scatter Plots
• Regression Analysis
• Correlation Coefficients
Python Foundation – 4 Modules
Module 1: Python Basics
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
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
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
Module 4: 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
• 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 Analysis Associate – 7 Modules
Module 1: Comparision And Correlation Analysis
• Data comparison Introduction,
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis
Module 2: Variance And Frequency Analysis
• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for Frequency Analysis
Module 3: Ranking Analysis
• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis
Module 4: Break Even Analysis
• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Manufacturing
Module 5: Pareto (80/20 Rule) Analsysis
• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• Hands-on case study: Pareto Analysis
MODULE 6: Time Series and Trend Analysis
• Introduction to Time Series Data
Advance Data Analytics – 4 Modules
Module 1: Data Analytics Foundation
• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity
Module 2: Optimization Models
• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity
Module 3: Predictive Analytics With Regression
• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel
Module 4: Decision Modeling
Predictive Analytics With Ml – 8 Modules
Module 1: Machine Learning Introduction
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
Module 2: Ml Algo: Linear Regresssion
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool
Module 3: Ml Algo: Logistic Regression
• Introduction to Logistic Regression;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool
Module 4: Ml Algo: Knn
• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool
Module 5: Ml Algo: K Means Clustering
• Decision Tree and How it works
• Hands-on: Decision Tree with ML Tool
Module 6: Ml Algo: Decision Tree
• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering
Module 7: Ml Algo: Support Vector Machine (SVM)
• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering
Database: Sql And Mongodb – 7 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
• 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
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
Bi Analyst – 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 Data Sources
• Connecting to a CSV File
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