Meet Our Leadership Team

Innovative Leadership: Guiding Our Vision

Vivek K Mishra

Co Founder

The Scholar Vivek is a dynamic leader with extensive experience of around 25 Years in business development, corporate relations, education, and manpower consultancy. As Co-Founder of The Scholar, He drives talent development, builds strategic partnerships, and bridges the gap between education and employability. Passionate about empowering individuals and organizations, He continues to lead impactful initiatives in skill development and career growth.

Rahul Tiwari

Co Founder

Rahul Tiwari is an accomplished Data Scientist and Stock Market Analyst with 18+ years of experience working with leading firms like Accenture, Capgemini, Tech Mahindra, Deloitte, and Raw Mind. As Co-Founder of The Scholar , he leverages his expertise in data science and financial analytics to drive career transformation and deliver data-driven solutions for individuals and businesses.

Ranjay Vishen

Advisor

Ranjay Vishen is a seasoned professional with over 18 years of rich experience in the education domain. He has held leadership positions in top institutions, including serving as Director at IIBS, where he played a pivotal role in driving growth and academic excellence. Known for his strategic vision and passion for education, Ranjay has consistently delivered impactful results throughout his career. He is also the Founder of a disruptive technology education start-up offering both B2B and B2C solutions eflecting his commitment to innovation and digital transformation in learning.

Punit kumar

Customer Success Leader

Punit is a customer-focused leader with over 15 years Experience . Committed to building strong client relationships, driving retention, and ensuring satisfaction. With a focus on solving challenges and delivering value, He helps clients achieve long-term success and growth.

Dikshita Mago

Program Head

Dikshita is an experienced Program Manager with over 16 years in the education industry. Skilled in managing end-to-end program operations, training delivery, and stakeholder engagement, She is dedicated to driving learning outcomes and ensuring seamless execution. She is passionate about creating impactful educational experiences that empower learners and support organizational goals.

Manoj Kumar

Manager- Talent & People Success

Manoj Kumar is a passionate people leader with over 18 years in Different Industries with focus on talent growth, employee engagement, and workforce development. With expertise in talent management and people strategies, he helps attract, develop, and retain top talent while fostering a positive, growth-driven workplace culture.

Suyash

Next-Gen Business Associate

A driven and dynamic young professional, Suyash brings fresh energy and modern thinking to The Scholar. As he has grown alongside the vision of the organization and now actively contributes to its sales and marketing strategies. With a keen understanding of youth trends, digital behavior, and growth-focused ideas, Suyash plays a vital role in shaping The Scholar’s outreach and brand visibility while learning the ropes of business leadership from the ground up.

Assignments, Videos & Case Studies

Course Roadmap – Built Just for You

  • Data Warehouse Basics
  • OLTP vs OLAP
  • What is a Data Warehouse?
  • ETL vs ELT Concepts
  • Data Lake vs Data Warehouse
  • Data Modeling
  • Dimensional Modeling
  • ER Modeling vs Star Schema vs Snowflake Schema
  • Fact Table & Dimension Table
  • Schemas
  • Star Schema
  • Snowflake Schema
  • Advantages and Disadvantages
  • Normalization & Denormalization
  • 1NF
  • 2NF
  • 3NF
  • Slowly Changing Dimensions (SCD)
  • Fact Table Types
  • Additive Facts
  • Semi Additive Facts
  • Factless Facts
  • Additive Facts
  • Semi Additive Facts
  • Factless Facts
  • Dimension Types
  • Conformed Dimension
  • Junk Dimension
  • Degenerate Dimension
  • Role Playing Dimension
  • ETL Concepts
  • ETL Overview
  • Extraction, Transformation, Loading Steps
  • ETL Pipeline Design Principles
  • Introduction to MySQL
  • MySQL Architecture
  • Client
  • Server Model
  • Storage Engines (InnoDB vs MyISAM)
  • Installation & Setup
  • Database Basics
  • Database & Table Creation
  • Datatypes
  • Constraints (Primary Key, Foreign Key, Unique, Not Null, Default, Check)
  • Basic SQL Queries
  • SELECT Statement
  • Filtering with WHERE
  • ORDER BY
  • DISTINCT
  • LIMIT / OFFSET
  • Functions and Operators
  • Aggregate Functions (SUM, AVG, COUNT, MIN, MAX)
  • String Functions
  • Date & Time Functions
  • Mathematical Functions
  • CASE WHEN Logic
  • Joins and Set Operations
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL OUTER JOIN
  • CROSS JOIN
  • UNION
  • UNION ALL
  • INTERSECT / EXCEPT
  • Subqueries & Nested Queries
  • Scalar Subqueries
  • Correlated Subqueries
  • EXISTS vs IN vs JOIN
  • Advanced SQL Queries
  • Common Table Expressions (CTE)
  • Window Functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, NTILE)
  • Recursive CTE
  • Derived Tables
  • Framing
  • Partition By
  • Data Manipulation (DML)
  • INSERT
  • INSERT INTO SELECT
  • UPDATE
  • DELETE
  • Data Definition (DDL)
  • CREATE
  • ALTER
  • DROP
  • TRUNCATE
  • Transaction Management
  • COMMIT
  • ROLLBACK
  • Views
  • CREATE VIEW
  • CREATE OR REPLACE VIEW
  • Indexes
  • Clustered Index (Primary Key)
  • Non-Clustered Index (Secondary Index)
  • Composite Indexes
  • BTree
  • Hash Index
  • Stored Procedures (PL/SQL equivalent)
  • CREATE PROCEDURE
  • IN, OUT, INOUT Parameters
  • Control Flow (IF, CASE, WHILE, LOOP, REPEAT)
  • Error Handling
  • Functions
  • CREATE FUNCTION
  • RETURN Statement
  • Deterministic vs Non-Deterministic Functions
  • Triggers
  • BEFORE INSERT/UPDATE/DELETE
  • AFTER INSERT/UPDATE/DELETE
  • Use Cases and Best Practices
  • Cursors
  • DECLARE Cursor
  • OPEN, FETCH, CLOSE
  • Data Preparation using Tableau Prep
  • Data Visualization
  • Business Intelligence tools
  • Introduction to Tableau
  • Tableau Architecture
  • VizQL
  • Introduction to Tableau Prep
  • Tableau Prep Builder User Interface
  • Data Preparation techniques using Tableau Prep Builder tool
  • Data Connection with Tableau Desktop
  • Features of Tableau Desktop
  • Data Connection with Tableau Desktop
  • Connect to data from File and Database
  • Types of Connections
  • Joins and Unions
  • Data Blending
  • Tableau Desktop User Interface
  • Basic project: Create a workbook and publish it on Tableau Online
  • Basic Visual Analytics
  • Visual Analytics
  • Basic Charts: Bar Chart, Line Chart, and Pie Chart
  • Hierarchies
  • Data Granularity
  • Highlighting
  • Sorting
  • Filtering
  • Grouping
  • Sets
  • Calculations in Tableau
  • Types of Calculations
  • Built-in Functions (Number, String, Date, Logical and Aggregate)
  • Operators and Syntax Conventions
  • Table Calculations
  • Level Of Detail (LOD) Calculations
  • Using Python within Tableau for Calculations
  • Advanced Visual Analytics
  • Tool tips
  • Trend lines
  • Reference lines
  • Forecasting
  • Clustering
  • Level Of Detail (LOD) Expressions in Tableau
  • Use Case I - Count Customer by Order
  • Use Case II - Profit per Business Day
  • Use Case III - Comparative Sales
  • Use Case IV - Profit Vs Target
  • Use Case V - Finding the second order date
  • Use Case VI - Cohort Analysis
  • Geographic Visualizations in Tableau
  • Introduction to Geographic Visualizations
  • Manually assigning Geographical Locations
  • Types of Maps
  • Spatial Files
  • Custom Geocoding
  • Polygon Maps
  • Web Map Services
  • Background Images
  • Advanced Charts in Tableau
  • Box and Whisker’s Plot
  • Waterfall Chart
  • Bump Chart
  • Word Cloud
  • Donut Chart
  • Dashboards and Stories
  • Introduction to Dashboards
  • The Dashboard Interface
  • Dashboard Objects
  • Building a Dashboard
  • Dashboard Layouts and Formatting
  • Interactive Dashboards with actions
  • Designing Dashboards for devices
  • Story Points
  • Get Industry Ready
  • Tableau Tips and Tricks
  • Choosing the right type of Chart
  • Format Style
  • Data Visualization best practices
  • Prepare for Tableau Interview
  • Exploring Tableau Online
  • Publishing Workbooks to Tableau Online
  • Interacting with Content on Tableau Online
  • Understand Scheduling
  • Managing Permissions on Tableau Online
  • Data Security with Filters in Tableau Online
  • Overview of Data Analytics
  • Overview of Data Analytics
  • Power BI Installation
  • Power BI Installation
  • Explore the Dynamic User Interface of PowerBI
  • Explore the Dynamic User Interface of PowerBI
  • Power Query
  • Power Query
  • Functions
  • Introduction to Basic Functions
  • Text Functions
  • Date Functions
  • Number Functions
  • Conditional Column and Column From Examples
  • Introduction to Conditional Columns and Column from Examples
  • Conditional Column in Power BI Power Query
  • Column from Examples in Power BI Power Query
  • Appending or Combining Files
  • Combining CSV Files
  • Consolidate Files with Different Structures
  • Replacing VLOOKUP with Merge Queries
  • Introduction to Replacing VLOOKUP
  • Introduction to Merge Queries
  • Advanced Merge Queries
  • Challenge with Merge Queries
  • Powerful Cleaning Features in Power Query
  • Introduction to Powerful Cleaning Features in Power BI Power Query
  • Data Cleaning in Power BI Power Query
  • Changing Structure of Data in Power BI Power Query (Unpivot)
  • Working with Rows and Columns in Power Query
  • Introduction to Working with Columns & Rows
  • Working with Rows & Columns in Power BI Power Query
  • Various Data Sources in Power BI
  • Get SQL Data in Power BI
  • Get Live Website Data in Power BI
  • Get Email Data in Power BI
  • Get Google Forms (Google Sheets) Data in Power BI
  • Get Power BI Datasets
  • Share Point Data in Power BI
  • Get JSON Files in Power BI
  • Working with Filters and Parameters
  • Introduction to working with Filters, Sorting and Parameters
  • Working with Filter, Parameters and Sorting in Power BI Power Query
  • Custom Function Feature to Clean and Combine Files
  • Introduction to Custom Function to Clean & Combine Files
  • Clean & Combine CSV Files In Power BI Power Query
  • Clean & Combine Excel Files In Power BI Power Query
  • Finding Out Postal Codes from States and Uts
  • Finding Out Postal Codes from States and Uts
  • M Functions
  • Introduction to M Language
  • M Functions Date - IsIn
  • M Functions Date_ Add and Subtract
  • M Functions - Date - Basic
  • M Functions - Text
  • Write a Simple M Code
  • Trick to get help for 800+ M Functions
  • Power BI Report View
  • Introduction to PowerBI Report View
  • Working with Basic Visualization in PowerBI
  • Introduction to Basic Visualization in Power BI
  • Basic Charts in Power BI
  • Formatting of Visuals in Power BI
  • Visualize Spatial Data by Maps
  • Introduction to Visualize Spatial Data by Maps
  • Working With Maps In Power BI
  • Geo Styling
  • Formatting of Pages and Report
  • Introduction to Formatting of Pages & Reports
  • Page Perfection
  • Working With Themes
  • Textualize Your Data in Power BI
  • Introduction to Textualize Your Data in Power BI
  • Matrix Mastery
  • Conditional Formatting In Textual Data
  • Hierarchies In Matrix
  • Filters and Slicers in Power BI
  • Introduction to Filters & Slicers in Power BI
  • Filters On Different Data Types
  • Introduction To Filters
  • Slicers In Power BI
  • Formatting of Slicers
  • Cards and KPIs
  • Introduction to Cards & KPIs in Power BI
  • Numeric Cards
  • Textual Cards In Power BI
  • Using Actions to Enhance Data Visualization in Power BI
  • Introduction to Using Actions to Enhance Data Visualization
  • Elements in Power BI
  • Actions in Power BI
  • Bonus in Power BI
  • Artificial Intelligence in Business Intelligence
  • Q & A in Power BI
  • Decomposition Tree in Power BI
  • Smart Narrative in Power BI
  • Explain the increase & decrease feature in Power BI
  • Get Quick Insights in Power BI
  • Misc Features
  • Clear all Slicers & Apply all Slicers
  • On Object Interaction
  • Customize Pane Switcher Option
  • New Card in Power BI
  • Power BI Services
  • Introduction to PowerBI Services
  • Introduction to Publishing The Report To Power BI Service
  • Publishing Report To Power BI Online
  • Sharing & Collaboration of Power BI Reports
  • Exporting the Report
  • Introduction to Exporting the report
  • Creating a Dashboard in Power BI
  • Introduction to Creating a Dashboard in Power BI Service
  • Creating a Dashboard In Power BI Service
  • Dashboard Options
  • Capstone Projects
  • FMCG Sales Report
  • Banking Report
  • Healthcare Report
  • HR Report
  • Introduction to Power Pivot
  • Introduction to Power Pivot
  • Power BI DAX
  • Introduction
  • Text Function
  • Data Function
  • Logical Functions
  • Calculated Column Vs. Measures
  • Implicit Measures Vs. Explicit Measures
  • Calculate Function
  • PREVIOUSDAY - Calculate Time Intelligence Functions
  • NEXTDAY - Calculate Time Intelligence Functions
  • Percentage Difference
  • SAMEPERIODLASTYEAR
  • DATEADD
  • MTD, QTD and YTD using Quick Measures
  • Concatenated List of Values
  • Star Rating using Quick Measures
  • Filter Function
  • All Function
  • SUMMARIZE
  • Top N
  • CALENDARAUTO
  • Python Installation and Introduction
  • Python Installation - Various ways
  • Introduction To Python
  • Features of Python
  • Python Identifiers
  • Reserved Words
  • Python Data Types
  • Slice operator
  • Type casting
  • Mutable and Immutable concepts
  • Detailed discussion on Python Collection Datatypes
  • Operators in Python
  • Arithmatic Operators
  • Relational or Comparision Operators
  • Logical Operators
  • Equality Operator
  • Assignment Operator
  • Special Operator
  • Input and Output
  • Input Streams
  • Command Line Arguments
  • Output Streams
  • Flow Control
  • Conditional Statements
  • Iterative Statements
  • Transfer Statements
  • Pass Statement
  • String Functions
  • String Slice Operator
  • Mathematical Operators in String
  • String Functions
  • Functions and Lambda Functions
  • Functions
  • Recursive Functions
  • Lambda Functions
  • Function Decorator
  • Function Generator
  • Modules
  • Random Modules
  • Packages
  • Exception Handling
  • Exception Handling
  • Loggin Exceptions
  • Assertions in Exception
  • File Handling
  • File Handling
  • Pickling and Unpickling
  • Regular Expression
  • Regular Expression
  • Regular Expression using web scraping
  • Database Connectivity
  • Database Connectivity
  • PyMySQL with MySQL
  • PyMySQL with MySQL
  • Numpy
  • Numpy
  • Pandas
  • Pandas
  • Data Visualization
  • Matplotlib
  • Seaborn
  • Data Preprocessing
  • Missing Value Treatment
  • Outlier Treatment
  • Feature Scaling
  • Encoding
  • Streamlit
  • Frontend using Streamlit
  • Introduction to Statistics
  • Why Statistics in Data Analytics?
  • Types of Data (Qualitative vs Quantitative)
  • Descriptive Statistics
  • Measures of Central Tendency (Mean, Median, Mode)
  • Measures of Dispersion (Range, Variance, Standard Deviation, IQR)
  • Coefficient of Variation
  • Skewness & Kurtosis
  • Exploratory Data Analysis
  • Univariate Analysis
  • Bivariate Analysis
  • Correlation (Pearson, Spearman)
  • Covariance
  • Outlier Detection (Z-score, IQR method)
  • Missing Value Analysis
  • Correlation and Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Assumptions of Regression
  • R-squared & Adjusted R-squared
  • Multicollinearity (VIF)
  • Introduction to Machine Learning
  • What is Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Business Applications of Machine Learning
  • Machine Learning Lifecycle
  • Problem Framing
  • Data Collection
  • Data Preparation
  • Model Building
  • Model Evaluation
  • Deployment & Monitoring
  • Linear Regression (Supervised Regression)
  • Business Use Cases (Sales Forecasting, Price Prediction, etc.)
  • Model Assumptions
  • Model Formula (Y = β0 + β1X + … + ε)
  • Coefficients Interpretation
  • Evaluation Metrics (R², MAE, MSE, RMSE)
  • Overfitting & Underfitting
  • Regularization (L1, L2) (Short Overview)
  • Full Python Code Walkthrough (sklearn and Statsmodels)
  • End to End Production Ready application building
  • Logistic Regression (Supervised Classification)
  • Business Use Cases (Churn, Fraud, Lead Conversion)
  • Sigmoid Function & Probability Output
  • Odds Ratio & Log-Odds Interpretation
  • Evaluation Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
  • Confusion Matrix
  • Threshold Tuning
  • Multicollinearity (VIF)
  • Full Python Code Walkthrough (sklearn and Statsmodels)
  • End to End Production Ready application building
  • Business Use Cases (Customer Segmentation, Market Segmentation)
  • K-Means Clustering (Unsupervised Learning)
  • Clustering Objective & Centroid Logic
  • Choosing K (Elbow Method, Silhouette Score)
  • Standardization before Clustering
  • Interpretation of Clusters
  • Limitations of K-Means
  • Full Python Code Walkthrough (sklearn)
  • End to End Production Ready application building
  • Model Comparison & Selection
  • Regression vs Classification vs Clustering
  • When to use what?
  • Business scenario based model selection
  • Common Machine Learning Mistakes and Best Practices
  • Overfitting
  • Underfitting
  • Importance of validation
  • Introduction to ChatGPT for Data Analytics
  • Why use ChatGPT for Data Analytics?
  • Capabilities & Limitations
  • Types of Tasks it can handle
  • Prompt Engineering for Analysts
  • What is a Prompt?
  • Zero-shot, One-shot, Few-shot prompting
  • How to write better prompts for data analysis
  • Iterative prompting
  • SQL Code Generation
  • Generating SQL queries from business questions
  • Query optimization suggestions
  • Complex join creation
  • Data cleaning queries
  • Python Code Generation (Pandas/Numpy)
  • Generating Pandas code
  • Data cleaning using ChatGPT
  • Feature engineering suggestions
  • Regex pattern generation for cleaning
  • Data Visualization Assistance
  • Chart recommendations
  • Matplotlib, Seaborn, Plotly code generation
  • Insight summarization from charts
  • Documentation & Reporting Automation
  • Auto-generating data dictionaries
  • Writing executive summaries
  • Auto-generating business insights from data
  • Business Communication Support
  • Email drafts for reporting findings
  • Presentation script generation
  • Simplifying technical outputs for business users
  • ChatGPT Integration with Tools
  • ChatGPT with Python (OpenAI API)
  • Auto-generation of dashboard summaries
  • Brand Yourself
  • Resume Building
  • LinkedIn Posts
  • Introduction to Excel
  • Overview of the Excel interface
  • Workbook and worksheet basics
  • Cell basics: selecting, editing, and formatting
  • Basic formulas and functions (SUM, AVERAGE, MIN, MAX)
  • Data Entry and Basic Formatting
  • Entering and editing data
  • Formatting cells (number, text, date formats)
  • Using the Format Painter
  • Conditional formatting basics
  • Basic Formulas and Functions
  • Arithmetic operations in Excel
  • Introduction to functions (SUM, AVERAGE, COUNT, COUNTA)
  • Relative, absolute, and mixed cell references
  • Using the AutoFill feature
  • Data Management
  • Sorting and filtering data
  • Using tables in Excel
  • Data validation (dropdown lists, data types)
  • Removing duplicates
  • Advanced Formulas and Functions
  • Logical functions (IF, AND, OR, NOT)
  • Lookup functions (VLOOKUP, HLOOKUP)
  • Text functions (LEFT, RIGHT, MID, CONCATENATE)
  • Date and time functions (TODAY, NOW, DATE, TIME)
  • Working with Charts and Graphs
  • Creating and customizing charts (bar, line, pie, etc.)
  • Using Sparklines
  • Formatting chart elements
  • Using trendlines and error bars
  • PivotTables and PivotCharts
  • Introduction to PivotTables
  • Creating and formatting PivotTables
  • Using PivotCharts
  • Grouping and filtering data in PivotTables
  • Advanced Data Analysis Tools
  • Introduction to What-If Analysis (Goal Seek, Data Tables, Scenario Manager)
  • Using Solver for optimization
  • Creating and using Data Models
  • Using Power Query for data import and transformation
  • Advanced Lookup Functions
  • INDEX and MATCH functions
  • Using XLOOKUP for more powerful lookups
  • Combining functions for complex lookups
  • Using INDIRECT for dynamic ranges
  • Working with Large Datasets
  • Techniques for handling large datasets
  • Using Excel’s built-in data tools (Remove Duplicates, Text to Columns)
  • Advanced filtering techniques
  • Introduction to Excel's Power Pivot
  • Macros and VBA Introduction
  • Recording and running Macros
  • Understanding the VBA editor
  • Writing simple VBA code
  • Automating tasks with VBA
  • Data Cleaning Techniques
  • Identifying and correcting data errors
  • Using text functions for data cleaning
  • Advanced Find and Replace techniques
  • Data normalization techniques
  • Data Visualization Techniques
  • Advanced chart types (Combo Charts, Waterfall, Funnel, etc.)
  • Using conditional formatting for data visualization
  • Creating and using Excel Dashboards
  • Using Power BI with Excel for advanced visualization
  • Statistical Analysis in Excel
  • Descriptive statistics (mean, median, mode, standard deviation)
  • Using Analysis ToolPak for statistical analysis
  • Hypothesis testing
  • Regression analysis
  • Working with External Data
  • Importing data from different sources (CSV, databases, web)
  • Using Power Query for data connection and transformation
  • Linking and embedding objects
  • Data consolidation techniques
  • Collaboration and Sharing
  • Protecting worksheets and workbooks
  • Sharing and co-authoring workbooks
  • Tracking changes and comments
  • Using Excel Online and OneDrive for collaboration
  • Advanced Formulas and Arrays
  • Using array formulas
  • Introduction to dynamic arrays and spill functions
  • Using SEQUENCE, SORT, FILTER, UNIQUE functions
  • Advanced conditional formatting with formulas
  • Time-saving Tips and Tricks
  • Keyboard shortcuts
  • Using templates
  • Customizing the ribbon and Quick Access Toolbar
  • Best practices for efficient Excel use
  • Project Management with Excel
  • Creating Gantt charts
  • Using Excel for task management
  • Resource allocation and tracking
  • Project tracking and reporting
  • Capstone Project
  • Applying learned skills to a real-world data analysis project
  • Data cleaning and preparation
  • Data analysis and visualization
  • Presenting findings and insights

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