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Data Analytics Training in Pune with 100% Placement Assistance at Version IT
- Learning Experience 360 Degree Mini-State Facilities.
- Marketable Data Analytics Credentials.
- Professional Training With the freedom of time to suit all.
Overview Data Analytics Training in Pune at Version IT
Within the contemporary digital world, information is being referred to as the new oil. Nevertheless, just like crude oil, raw data is of little use. The real worth is what is achievable through refining, analyzing, and interpolating this information to be used in making strategic decisions. With Pune continuing to establish itself as a leading IT and education hub, there has been an explosion in the demand of skilled data professionals.
Version IT boasts of being one of the few Data Analytics Training Programs that is comprehensive and industry aligned in Pune that is specifically aimed at meeting the gap between education and practice.
Why Data Analytics is the Definitive Career Choice
The move towards the data-driven economy is not a trend; it is a paradigm shift as far as the functioning of businesses is concerned. Having made its mark in healthcare and finance, retail and manufacturing sectors all operate under data to streamline the processes and derive future results.
The Role of a Data Analyst
- A Data Analyst is the link between the technical data and business strategy. Their major roles are:
- Data Collection: Extraction of data with multiple sources such databases, web scraping and sensor logs.
- Data Cleaning: Data should be correct, stable and must be noise-free.
Advantages of Learning Data Analytics at Version IT in Pune
The most important aspect of your career is choosing the right training institute. This is the distinction of Version IT:
Practical Project-based Learning
In our opinion, the learning occurs through doing. In our program, you will develop real-world projects that involve messy, unstructured data to address business issues.
Expert Faculty
We do not merely hire teachers; practitioners are our teachers, and they have worked over the years in the analytics departments of some of the largest corporations in the world. They have day-in-the-life insights, which you will never see in a textbook.
Version IT’s Placement Support and Career Guidance
We do not stop communicating with you after the course ends. We provide:
- Resume Building: Designing A Data-rich CV that goes past ATS (Applicant Tracking Systems).
- Mock Interviews: Optimizing the technical and HR interview rounds.
- Direct Referrals: Tapping into our base of partner companies in Pune and the rest.
Data Analytics: Industry Use Cases
To see the difference your training might make, we can consider how the data analytics is used in different spheres:
Industry | Application of Data Analytics |
Retail | Anticipate demand seasonally and supply chain logistics. |
Finance | Real time detection of fraudulent transactions and credit scoring. |
Healthcare | Anticipating the readmission rates of patients and staff optimization. |
E-commerce | Recommend products by personalizing them based on browsing history. |
Who Should Enroll in Data Analytics Course?
The course is meant to serve a wide variety of applicants:
- Fresh Graduates: Interested in high paying job in future-proof profession.
- IT Professionals: Looking to move off the software creation side to the data-centric side.
- Business Analysts: Would like to modernize their technical tools using Python and SQL.
- Business people: Who would like to scale their business efficiency using data.
Join Version IT Pune today and start your journey toward excellence.
Topics You will Learn
What You Will Learn in Python
By the end of this module, you will be able to:
✔ Write clean and structured Python programs
✔ Perform data cleaning and transformation using Pandas
✔ Conduct Exploratory Data Analysis (EDA)
✔ Create professional data visualizations
✔ Build predictive machine learning models
✔ Automate repetitive data tasks
✔ Connect Python with SQL databases
✔ Develop end-to-end analytics workflows
Core Python
PYTHON PROGRAMMING
01 – Foundations
- Introduction to Python
- Installation of Python
02 – Data Types & Variables
- Data types
- Variables
- Input
03 – Operators & Conditions
- Operators
- if, elif, else statements
04 – Loops
- for loops
- while loops
05 – Data Structures
- Lists, Tuples
- Dictionaries, Sets
- String Handling
06 – Functions
- Function Definition
- Parameters & Return
07 – Modules & Files
- Date and Time Module
- File Handling
08 – Error Handling
- Errors & Debugging
- Exception Handling
OBJECT-ORIENTED PROGRAMMING
(OOP) – The 4 Pillars
OOPS Principles
Encapsulation
Bundling data and methods together
- Data Hiding
- Private/Public Access
- Controlled Access
Abstraction
Hiding complexity, showing essentials
- Hide Implementation
- Show Interface
- Simplify Complexity
Polymorphism
Objects can take multiple forms
- Method Overloading
- Method Overriding
- Runtime Flexibility
Inheritance
Class hierarchy and reusability
- Parent-Child Classes
- Code Reusability
- IS-A Relationship
Key Benefits: Modularity • Reusability • Maintainability • Scalability • Security
Python Libraries for Data Analytics
Part 1: NumPy, Pandas & EDA
KEY LEARNING OUTCOMES
- Master NumPy for numerical computing and array operations
- Use Pandas for data manipulation and cleaning
- Perform exploratory data analysis with visualization
- Create professional visualizations using Matplotlib
- Create advanced visualizations using Seaborn
- Understand univariate, bivariate, and multivariate analysis
- Prepare data for machine learning applications
- Build complete data analysis pipelines
- Handle real-world messy data effectively
SECTION 1: NUMPY – NUMERICAL COMPUTING
- Introduction to NumPy: Framework and advantages
- NumPy Attributes: Shape, size, dtype, ndim
- Array Creation: zeros, ones, arange, linspace
- Indexing & Slicing: Accessing array elements
- Iteration over Arrays: Looping through elements
- Array Manipulations: Reshape, flatten, transpose
- Mathematical Operators: Addition, subtraction, multiplication
- Relational Operators: Comparison operators
- Mathematical Functions: sin, cos, sqrt, exp, log
- Broadcasting: Operating on arrays of different shapes
SECTION 2: PANDAS – DATA MANIPULATION
- Introduction to Pandas: Data analysis library
- Series: One-dimensional labeled arrays
- DataFrames: Two-dimensional labeled arrays
- Creating DataFrames: From lists, dicts, numpy arrays
- Column Operations: Selection, addition, deletion
- Row Operations: Selection, adding, deletion
- Merging DataFrames: Join, merge, concatenate
- Importing Data: CSV, Excel, SQL, JSON formats
- Basic Dataset Insights: head, tail, info, describe
- Summarizing Data: Aggregation and statistics
SECTION 3: EXPLORATORY DATA ANALYSIS (EDA)
- Univariate Analysis: Analyzing single variables
- Bivariate Analysis: Relationship between two variables
- Multivariate Analysis: Relationship among multiple variables
- Matplotlib Plots: Histogram, Box, Scatter, Line, Pie, Bar
- Matplotlib Subplots: Multiple plots in one figure
- Seaborn Bar Plot: Categorical data visualization
- Seaborn Count Plot: Category frequency visualization
- Seaborn Box Plot: Distribution comparison
Python Libraries for Data Analytics
Part 2: Data Cleaning & AI/ML
KEY LEARNING OUTCOMES (Advanced)
- Clean and prepare messy data for analysis
- Handle missing values and outliers effectively
- Understand machine learning fundamentals
- Build supervised learning models
- Build unsupervised learning models
- Implement linear and logistic regression
- Apply decision trees and clustering
- Evaluate and optimize machine learning models
- Prevent overfitting and underfitting
- Build complete end-to-end ML pipelines
SECTION 4: DATA CLEANING & PREPARATION
- Dealing with Wrong Data Types: Type conversion
- Type Checking: Validating data types
- Type Conversion: Converting between types
- Treating Duplicates: Identifying and removing duplicates
- Handling Missing Values: NaN detection and treatment
- Missing Value Imputation: Fill, forward fill, backward fill
- Handling Outliers: Detection and treatment methods
- Statistical Methods: IQR, z-score for outlier detection
- Drop Unnecessary Columns: Feature selection
- Data Validation: Ensuring data quality
SECTION 5: INTRODUCTION TO AI & MACHINE LEARNING
- Machine Learning Basics: Concepts and types
- Supervised vs Unsupervised Learning: Key differences
- Regression Analysis: Predicting continuous values
- Linear Regression: Simple and multiple regression
- Logistic Regression: Binary and multiclass classification
- Decision Trees: Tree-based classification and regression
- Tree Splitting: Gini index and entropy
- K-Means Clustering: Unsupervised learning
- Model Evaluation Metrics: Accuracy, precision, recall
- Confusion Matrix: Classification performance analysis
- Cross-Validation: Training and validation strategies
- Train-Test Split: Data partitioning for evaluation
SQL Server for Data Analytics – What You Will Learn
KEY LEARNING OUTCOMES
- Master SQL fundamentals, database design, and normalization concepts
- Design and create robust relational databases with proper data modeling
- Write efficient and optimized SQL queries for complex business problems
- Perform advanced data retrieval using subqueries, CTEs, and window functions
- Implement ranking, partitioning, and aggregate window functions
- Create and manage views, stored procedures, and user-defined functions
- Optimize database performance through indexing and query optimization
- Conduct comprehensive exploratory data analysis and perform customer segmentation, RFM analysis, and sales analytics
- Build CI dashboards, performance reports, and business intelligence summaries
SECTION 1: DATABASE FUNDAMENTALS
- Introduction to Databases, DBMS, RDBMS vs NoSQL
- MySQL Installation, Configuration & Environment Setup
- Data Types (INT, VARCHAR, DATE, FLOAT, DECIMAL, TEXT, BLOB, etc.)
- Primary Keys, Foreign Keys, Composite Keys & Candidate Keys
- Constraints: NOT NULL, UNIQUE, CHECK, DEFAULT, AUTO_INCREMENT
- CRUD Operations: (Create, Read, Update, Delete)
- SQL Commands: DDL, DML, DCL, TCL – Understanding Differences
- SELECT, Commands with WHERE clause & Filtering Techniques
- INSERT, UPDATE, DELETE Commands with Examples
- ORDER BY (ASC/DESC), GROUP BY, HAVING Clauses
- Logical, Comparison & Arithmetic Operators
- Aggregate Functions: SUM, AVG, COUNT, MAX, MIN with GROUP BY
SECTION 2: ADVANCED SQL TECHNIQUES
- JOINs: INNER, LEFT, RIGHT, FULL OUTER, CROSS join with real examples
- Self Joins & Multiple Table Joins for Complex Queries
- Subqueries: Scalar, Row, Table Subqueries in SELECT, WHERE, FROM
- Correlated Subqueries & Nested Subqueries for Advanced Filtering
- Common Table Expressions (CTE) with WITH clause
- Recursive CTEs for Hierarchical Data Processing
- Database Normalization (1NF, 2NF, 3NF, BCNF Concepts)
- Data Manipulation Strategies for Performance Optimization
- Entity-Relationship (ER) Modeling & ER Diagram Creation
- Window Functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE
- Aggregate Window Functions: SUM OVER, AVG OVER with PARTITION BY
- Views: Simple & Complex Views, Stored Procedures & User-Defined Functions
Master SQL Server for Advanced Data Analytics
Build a strong SQL foundation and master advanced techniques!
SQL SERVER FOR DATA ANALYTICS
Part 2: Analytics & Applications
SECTION 3: SQL FOR DATA ANALYTICS
(Key Performance Indicator (KPI) Calculations & Dashboard Metrics)
- Revenue Analysis: Total Sales, Growth Rate, Trends, Forecasting
- Sales Performance Reports: Top Products, Sales by Region/Category
- Customer Segmentation: RFM (Recency, Frequency, Monetary) Analysis
- Cohort Analysis: Customer Lifetime Retention, Month-on-Month
- Customer Lifetime Value (CLV): Calculation & Segmentation
- Product Performance: Sales Volume, Margins, Inventory Levels
- Year-over-Year (YoY) Comparisons & Growth Analysis
- Month-over-Month (MoM) Trends & Seasonality
- Pivot Tables & Cross-tabulation for Data Summarization
- Time-Series Data Analysis: Moving Averages, Trends, Seasonality
- Anomaly Detection: Identifying Outliers in Data
SECTION 4: REAL-WORLD APPLICATIONS
- E-commerce Analytics: Order analysis, Customer Behavior, Conversion Rates
- Financial & Banking Analytics: Transaction Analysis, Risk Assessment
- Supply Chain Optimization: Inventory Management, Supplier Performance
- HR Analytics: Employee Performance, Payroll, Retention Analysis
- Marketing Campaign Analysis: ROI, Customer Acquisition, Attribution
- Social Media Analytics: Engagement, Reach, Sentiment Analysis
- Integration with Python: Pandas, SQLAlchemy, Database Connectivity
- Integration with Power BI & Tableau: Query Optimization for BI Tools
- Building Automated Reports & Dashboards with Scheduled Queries
- Database Security: User Permissions, Role Management, Encryption
Master SQL and Transform Data into Actionable Business Intelligence
Build a Career in Data Analytics with Industry-Ready SQL Skills
Excel Basics & Formulas – Part 1
KEY LEARNING OUTCOMES
- Master Excel interface, formatting, and data entry techniques
- Perform complex calculations using advanced formulas and functions
- Create dynamic pivot tables and data summaries
- Build interactive dashboards and KPI reports
- Use what-if analysis for scenario planning
- Analyze data with conditional formatting and validation
- Create professional visualizations and interactive charts
- Integrate Excel with SQL and Power BI for end-to-end analytics
- Develop automated analytical workflows and business reports
SECTION 1: EXCEL BASICS & INTERFACE
- Ribbon Interface & Workbook Navigation
- Excel Interface and Toolbar Customization
- Cell References: Absolute, Relative, Mixed References
- Number Formatting: Currency, Percentage, Date Formats
- Cell Formatting: Borders, Fill Colors, Font Styles
- Data Entry & Input Validation Rules
- Sorting & Filtering Techniques
- Autofill, Flash Fill & Quick Analysis Tools
- Freeze Panes & Splitting Windows
- Creating and Managing Worksheets
SECTION 2: ESSENTIAL FUNCTIONS & FORMULAS
- Basic Math Functions: SUM, AVERAGE, MIN, MAX
- Conditional Functions: IF, Nested IF Statements
- COUNT & SUM IF Conditional Counting/Summing
- COUNTIFS & SUMIFS for Multiple Criteria
- VLOOKUP & HLOOKUP Functions
- XLOOKUP: Modern Lookup Function
- INDEX-MATCH: Advanced Lookup Replacement
- Logical Functions: AND, OR, NOT for Complex Scenarios
- IFERROR & IFNA for Error Handling
- CHOOSE Function for Multiple Value Selection
- INDIRECT Function for Dynamic References
Master Excel Fundamentals for Data Analytics!
Build a strong Excel foundation and grow your analytics skills!
Excel Dashboards & Analysis
Part 2
KEY LEARNING OUTCOMES (Advanced)
- Master text and date functions for data manipulation
- Clean messy data using Text to Columns & Flash Fill
- Create and customize pivot tables for complex analysis
- Build dynamic charts and KPI dashboards
- Implement data validation and conditional formatting
- Perform what-if analysis using Goal Seek & Solver
- Create scenario analysis models for decision making
- Design interactive reports with slicers and filters
- Build professional business intelligence dashboards
SECTION 3: TEXT & DATE FUNCTIONS
- Text Functions: LEFT, RIGHT, MID for String Extraction
- LEN Function for String Length & TRIM for Cleanup
- UPPER, LOWER, PROPER for Text Case Conversion
- CONCAT & CONCATENATE for String Joining
- FIND & SEARCH for Substring Location
- SUBSTITUTE for Text Replacement
- TODAY Function for Current Date
- NOW Function for Current Date & Time
- DATE Function for Creating Specific Dates
- DATEDIF for Date Difference Calculations
- YEAR, MONTH, DAY Extraction Functions
- WEEKDAY & WEEKNUM for Date Analysis
SECTION 4: DATA CLEANING & PIVOT TABLES
- Remove Duplicates from Large Datasets
- Text to Columns: Delimiter-based Separation
- Flash Fill for Pattern Recognition & Auto-fill
- Conditional Formatting: Color Scales, Data Bars, Icons
- Creating Pivot Tables from Raw Data
- Pivot Table Grouping: By Date, Category, Range
- Calculated Fields & Calculated Items in Pivots
- Pivot Slicers for Dynamic Filtering
- Timeline Slicers for Date-based Filtering
- Pivot Charts for Visual Data Representation
SECTION 5: ADVANCED FEATURES & DASHBOARDS
- Data Validation Rules: List, Number Range, Custom Formulas
- What-If Analysis: Goal Seek for Single Cell Optimization
- Solver Add-in for Complex Optimization Problems
- Scenario Manager for Multiple Scenario Planning
- Dynamic Charts: Charts that Update with Data
- KPI Dashboards: Creating Business Intelligence Dashboards
- Interactive Reports with Slicers & Filters
- Sparklines: Mini Charts within Cells
- Chart Types: Line, Bar, Pie, Scatter, Combo Charts
- Excel Integration: SQL Queries, Power Query, Power BI Connection
Master Advanced Excel & Dashboard Development
Create powerful analytics dashboards and business intelligence reports!
What You Will Learn
KEY LEARNING OUTCOMES
- Master descriptive statistics and data summarization techniques
- Conduct hypothesis testing and inferential statistics analysis
- Apply probability concepts and distributions to real-world problems
- Perform correlation and regression analysis for predictive modeling
- Understand sampling techniques and statistical significance
- Apply Bayesian reasoning and conditional probability
- Analyze normal distributions and use Central Limit Theorem
- Build simple and multiple regression models
- Interpret statistical results and communicate findings
- Make data-driven decisions using statistical evidence
SECTION 1: DESCRIPTIVE STATISTICS
- Mean, Median, Mode: Calculating and interpreting central tendency
- Range: Understanding data spread
- Variance & Standard Deviation: Measuring data dispersion
- Coefficient of Variation: Comparing variability across datasets
- Percentiles & Quartiles: Analyzing distribution segments
- Skewness: Understanding distribution asymmetry
- Kurtosis: Analyzing tail behavior and distribution
- Summary Statistics: Creating statistical profiles
- Data Visualization: Histograms, box plots, density plots
- Outlier Detection: Identifying and handling anomalies
- Data Summarization: Aggregate statistics and reporting
- Distribution Shapes: Normal, skewed, bimodal distributions
SECTION 2: INFERENTIAL STATISTICS & HYPOTHESIS TESTING
- Sampling Techniques: Random, stratified, systematic, cluster sampling
- Population vs Sample: Understanding parameters and statistics
- Sampling Distribution: Behavior of sample statistics
- Standard Error: Measuring sampling uncertainty
- Confidence Intervals: Constructing confidence bounds
- Hypothesis Testing Framework: Null vs alternative hypotheses
- Type I & II Errors: Understanding statistical errors
- p-value: Interpretation and significance testing
- Z-test: Testing means with known population variance
- t-test: One-sample, two-sample, and paired comparisons
- Chi-Square Test: Testing categorical associations
- ANOVA: Analyzing variance across multiple groups
Power BI for Business Intelligence – Part 1: Fundamentals & Data Modeling
KEY LEARNING OUTCOMES
- Master Power BI Desktop and Power BI Service platforms
- Connect to and import data from multiple sources
- Clean and transform data using Power Query effectively
- Design star schema and snowflake schema models
- Create relationships between tables for analysis
- Optimize data models for performance and clarity
- Build foundation for advanced BI solutions
- Understand Power BI architecture and components
- Implement best practices in data preparation
- Prepare data for advanced DAX calculations and visualizations
SECTION 1: POWER BI INTRODUCTION & DATA SOURCES
- Power BI Desktop: Installation, interface, workspace
- Power BI Service: Cloud platform and collaboration
- Power BI Architecture: Desktop, Service, Mobile
- Data Sources: Excel, SQL, Azure, Cloud Services
- Importing Data: Various import methods and options
- Data Connectors: Native and custom sources
- Query Settings: Loading and refresh options
- Power BI Licensing: Pro, Premium, Per User
- Workspace Management: Organization and structure
- Gateway Configuration: On-premises connectivity
SECTION 2: POWER QUERY – DATA CLEANING
- Power Query Editor: Interface and functionality
- Data Cleaning: Removing duplicates and errors
- Data Type Conversion: Changing data types
- Text Functions: Extracting and splitting text
- Date Functions: Formatting and calculations
- Conditional Columns: Creating new columns
- Merge Queries: Joining tables with join types
- Append Queries: Combining rows from tables
- Pivot & Unpivot: Reshaping data structures
- Replace Values: Finding and replacing data
Master Power BI Foundations and Data Preparation
Build a strong foundation for advanced BI solutions!
Power BI for Business Intelligence
Part 2: DAX, Visualizations & Advanced
KEY LEARNING OUTCOMES (Advanced)
- Master DAX (Data Analysis Expressions) language
- Write complex formulas and calculated columns
- Create advanced measures with time intelligence
- Design professional data visualizations
- Build interactive dashboards with drill-through
- Implement row-level security for data access
- Use AI visuals for predictive analytics
- Deploy and publish reports to Power BI Service
- Optimize performance and identify bottlenecks
- Implement collaboration and sharing strategies
SECTION 3: DATA MODELING – SCHEMAS & RELATIONSHIPS
- Star Schema Design: Fact tables with dimensions
- Snowflake Schema: Multi-level normalized hierarchies
- Fact Tables: Transactional and measure data
- Dimension Tables: Descriptive attributes
- Creating Relationships: One-to-many, many-to-many
- Primary & Foreign Keys: Data integrity
- Bridge Tables: Handling complex relationships
- Model Validation: Ensuring consistency
- Calendar Tables: Time dimension creation
- Role-Playing Dimensions: Multiple relationships
SECTION 4: DAX (DATA ANALYSIS EXPRESSIONS)
- DAX Syntax: Functions, operators, expressions
- Calculated Columns: Adding computed columns
- Measures: Creating aggregations and KPIs
- Context Functions: Row and filter context
- Aggregation Functions: SUM, AVERAGE, COUNT
- CALCULATE Function: Complex calculations
- SUMX & Similar Functions: Iterative calculations
- RELATED & RELATEDTABLE: Cross-table relationships
- Time Intelligence: YTD, MTD, QTD, YoY, MoM
- Advanced DAX: Complex nested formulas
SECTION 5: VISUALIZATIONS, DASHBOARDS & ADVANCED
- Chart Types: Bar, column, line, area, scatter
- KPI Cards: Key metric displays and gauges
- Table & Matrix Visuals: Detailed data representation
- Maps: Geographic visualization
- Treemaps & Waterfalls: Hierarchical and sequential
- Slicers: Single and multi-select filters
- Drill-Through: Navigation between pages
- Tooltips: Custom information on hover
- Row-Level Security: Data access control
- AI Visuals & Forecasting: Predictive analytics
SECTION 6: PUBLISHING, SHARING & COLLABORATION
- Publishing to Power BI Service: Desktop to cloud
- Report Scheduling: Automated refresh settings
- Alert Configuration: Data threshold notifications
- Sharing Reports: Permissions and access control
- Applications: Packaged content distribution
- Dashboards: Pinning visuals and metrics
- Collaboration: Comments and sharing notes
- Power BI Mobile: Mobile app deployment
- Email Subscriptions: Scheduled report delivery
- Performance Analyzer: Identifying bottlenecks
Basic Module
- Introduction to Tableau
- Connections
- Visual Analytics
- Basic Charts
- Sorting
- Filtering
- Grouping
- Sets
- Built-in Functions (Number, String, Date, Logical and Aggregate)
- Operators and Syntax Conventions
- Table Calculations
Advance Module
- Types of Calculations
- Trend lines
- Reference lines
- Forecasting
- Advance Plots
- Dashboard
GIT HUB
Integration with Microsoft Fabric
MINI PROJECTS
✔ Retail Sales Dashboard
✔ HR Analytics Dashboard
✔ SQL Customer Database Project
✔ Excel KPI Dashboard
✔ Python Sales Prediction Model
MAJOR PROJECTS
- End-to-End Retail Analytics
- Banking Loan Prediction Model
- Customer Churn Analysis
- E-commerce Business Dashboard
