25 Sections
331 Lessons
34 Weeks

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DATA ANALYSIS IN EXCEL
51
- 1.1
  Quick review on MS Excel Options, Ribbon, Sheets
- 1.2
  Saving Excel File as PDF, CSV and Older versions
- 1.3
  Using Excel Shortcuts with Full List of Shortcuts
- 1.4
  Copy, Cut, Paste, Hide, Unhide, and Link the Data in Rows, Columns and Sheet
- 1.5
  Using Paste Special Options
- 1.6
  Formatting Cells, Rows, Columns and Sheets
- 1.7
  Data Validation in Excel
- 1.8
  Grouping & Subtotal in Excel
- 1.9
  Protecting & Unprotecting Cells, Rows, Columns and Sheets with or without Password
- 1.10
  Page Layout and Printer Properties
- 1.11
  Working with Formulas / function
- 1.12
  Logical Function: IF / ELSE, AND, OR, NOT, TRUE, NESTED IF/ELSE etc
- 1.13
  Database Functions
- 1.14
  Date & Time Functions: DATE, DATEVALUE, DAY, DAY360, SECOND, MINUTES, HOURS, NOW, TODAY, MONTH, YEAR, YEARFRAC, TIME, WEEKDAY, WORKDAY
- 1.15
  Information Functions
- 1.16
  Math & Trigonometry Functions: RAND, ROUND, CEILING, FLOOR, INT, LCM, EVEN, SUMIF,
- 1.17
  Statistical Functions: AVEDEV, AVERAGE, AVERAGEA, AVERAGEIF, COUNT, COUNTA, COUNTBLANK, COUNTIF, MAX, MAXA,MIN, MINA, STDEVA
- 1.18
  Text Functions: LEFT, RIGHT, TEXT, TRIM, MID, LOWER,UPPER, PROPER, REPLACE, REPT, FIND, SEARCH,SUBSTITUTE, TRIM, TRUNC, CONVERT, CONCATENATE.
- 1.19
  Conditional Formatting
- 1.20
  Using Conditional Formatting
- 1.21
  Using Conditional Formatting with Multiple Cell Rules
- 1.22
  Using Color Scales and Icon Sets in Conditional Formatting
- 1.23
  Creating New Rules and Managing Existing Rules
- 1.24
  Data Lock & Protection
- 1.25
  Advance Charts in Excel
- 1.26
  Area Charts and Surface Charts
- 1.27
  Trend line Charts and Candle Stick
- 1.28
  Charts and Pie Charts
- 1.29
  XY (Scatter Charts)
- 1.30
  Time Series Charts and Bubble Charts
- 1.31
  Radar Charts and Doughnut Charts
- 1.32
  Rotating 3D Excel Charts
- 1.33
  Working with Fill Series & Go to Special
- 1.34
  Consolidation in Excel
- 1.35
  What if Analysis in Excel
- 1.36
  Goal Seek
- 1.37
  Scenario Manager
- 1.38
  Data Table
- 1.39
  Working with Histogram
- 1.40
  Helps in summarize discrete or continuous data
- 1.41
  Helps in identifying the most efficient pricing plans in sales & marketing
- 1.42
  Regression Analysis in Excel
- 1.43
  Used To Analyse Categorical Data
- 1.44
  Commonly Used In Understanding Customer Behaviour
- 1.45
  To Understand the Relationship between a Company’s Stock Price & The Company’s Quarterly Earnings
- 1.46
  Working With Forecasting
- 1.47
  Sales Forecasting
- 1.48
  Demand Forecasting
- 1.49
  Forecasting For Decision Making
- 1.50
  Know How To Capture Information About Significant Market Events.
- 1.51
  Solving Complex Data Using The Power Query In Excel
Professional Dashboards In Excel
5
- 2.1
- 2.2
- 2.3
  Interactive HR Dashboard In Excel
- 2.4
  Project Management Dashboard In Excel
- 2.5
  Call Centre Dashboard In Excel
Database Design in SQL
27
- 3.1
  Introduction
- 3.2
  What is a Data Warehouse?
- 3.3
  Structure of a Data Warehouse
- 3.4
  Star Schema
- 3.5
  OLAP vs. OLTP
- 3.6
  Creating 1 dimensional arrays
- 3.7
  Creating 2 dimensional arrays
- 3.8
  Array indexing
- 3.9
  Accessing array elements
- 3.10
  Concatenating Numpy arrays
- 3.11
  Arithmetic operations with arrays
- 3.12
  Covariance
- 3.13
  Correlation
- 3.14
  Linear Regression
- 3.15
  Overview of various methods and attributes of Pandas
- 3.16
  Introduction
- 3.17
  Working with various Series attributes
- 3.18
  Introduction
- 3.19
  Pivoting
- 3.20
  Sorting
- 3.21
  Aggregation
- 3.22
  Descriptive statistical analysis with Pandas
- 3.23
  Introduction to Matplotlib
- 3.24
  Various Matplotlib methods
- 3.25
  Creating Line, Scatter, Bar, etc. charts
- 3.26
  Customising charts using: X and Y labels, Limits, Ticks, Legends
- 3.27
  Introduction to Pyplot
Querying in MySQL
16
- 4.1
  Introduction
- 4.2
  Creating New Tables
- 4.3
  SQL SELECT Statements
- 4.4
  Manipulating Data(Insert, Delete, Update)
- 4.5
  Distinct, Order By, Join clauses and Aggregate Functions
- 4.6
  Using Primary keys, Foreign keys
- 4.7
  Get Data from Multiple Tables
- 4.8
  Using DDL Statements
- 4.9
  Restricting and Sorting Data
- 4.10
  Using Single-row Functions
- 4.11
  Conversion Functions
- 4.12
  Conditional Expressions
- 4.13
  Using the Group Functions
- 4.14
  Subqueries to solve queries
- 4.15
  Linking SQL file with Python
- 4.16
  Accessing database
Joins and Set Operations in SQL
7
- 5.1
  Set Theory
- 5.2
  Types of Joins
- 5.3
  Types of Joins: A Demonstration
- 5.4
  Outer Joins: A Demonstration
- 5.5
  Views with Joins
- 5.6
  Set Operations with SQL
- 5.7
  Assignment Case Study
Basic Python
26
- 6.1
  Introduction to Python
- 6.2
  Python – The Universal Language
- 6.3
  Installing Python
- 6.4
  iPython – a better Python interpreter
- 6.5
  Types – Dynamic v/s Static Typing – tstrong v/s Weak Typing
- 6.6
  Numbers
- 6.7
  Strings
- 6.8
  Unicode
- 6.9
  Complex Types
- 6.10
  Operators – Operator Overloading
- 6.11
  Variables
- 6.12
  Scopping And Expressions
- 6.13
  Use of tabs and whitespaces as indent
- 6.14
  Conditionals – for…else
- 6.15
  The general syntax
- 6.16
  Default values for arguments
- 6.17
  Returning and receiving multiple values
- 6.18
  Variable number of arguments – args, kwargs
- 6.19
  Scope revisited
- 6.20
  Primitive v/s Composite Types
- 6.21
  Lists
- 6.22
  Tuples
- 6.23
  Maps (or Dictionaries)
- 6.24
  Sets
- 6.25
  Enums
- 6.26
  Looping Techniques
Python Numpy
9
- 7.1
  Introduction to NumPy
- 7.2
  Basics of NumPy
- 7.3
  Operations Over 1-D Arrays
- 7.4
  Multidimensional Arrays
- 7.5
  Creating NumPy Arrays
- 7.6
  Mathematical Operations on NumPy
- 7.7
  Mathematical Operations on NumPy II
- 7.8
  Computation Times in NumPy vs Python Lists
- 7.9
  Case Study
Python Pandas
11
- 8.1
  Introduction to Pandas
- 8.2
  Basics of Pandas
- 8.3
  Pandas – Rows and Columns
- 8.4
  Describing Data
- 8.5
  Indexing and Slicing
- 8.6
  Operations on Data frames
- 8.7
  Group and Aggregate Functions
- 8.8
  Merging Data Frames
- 8.9
  Pivot Tables
- 8.10
  Practice Exercise
- 8.11
  Case Study
Python Matplotlib
11
- 9.1
  Introduction to Data Visualisation with Matplotlib
- 9.2
  Introduction to Matplotlib
- 9.3
  The Necessity of Data Visualisation
- 9.4
  Visualisations – Some Examples Facts and Dimensions
- 9.5
  Bar Graph
- 9.6
  Scatter Plot
- 9.7
  Line Graph and Histogram
- 9.8
  Outliers Analysis with Boxplots
- 9.9
  Subplots
- 9.10
  Choosing Plot Types
- 9.11
  Project
Python Seaborn
13
- 10.1
  Introduction
- 10.2
  Distribution Plots & Styling Options
- 10.3
  Pie – Chart and Bar Chart
- 10.4
  Scatter Plots & Pair Plots
- 10.5
  Revisiting Bar Graphs and Box Plots
- 10.6
  Heatmaps
- 10.7
  Line Charts
- 10.8
  Stacked Bar
- 10.9
  Charts Case Study Summary
- 10.10
  Plotly Practice Questions
- 10.11
  Practice Questions Solution
- 10.12
  Data Visualisation Practice Questions
- 10.13
  Case Study
Statistics Essentials
11
- 11.1
  Mean, Median, Mode
- 11.2
  Standard Deviation, variance,
- 11.3
  Standard Error Co-Relation ,Coefficient
- 11.4
  Probability
- 11.5
  Regression analysis
- 11.6
  Multiple Regression
- 11.7
  Logistic Regression
- 11.8
  Time Series
- 11.9
  Normal Distribution
- 11.10
  Binomial distribution
- 11.11
  Assignment
Microsoft Power BI
31
- 12.1
  COMPONENTS OF POWER BI: DESKTOP, SERVICE, AND MOBILE APPS
- 12.2
  BENEFITS AND APPLICATIONS OF POWER BI
- 12.3
  POWER BI ARCHITECTURE OVERVIEW
- 12.4
  INSTALLING AND SETTING UP POWER BI DESKTOP
- 12.5
  CONNECTING TO DATA SOURCES (EXCEL, CSV, ONLINE SERVICES, ETC.)
- 12.6
  CLEANING AND TRANSFORMING DATA
- 12.7
  HANDLING MISSING DATA SPLITTING AND MERGING COLUMNS
- 12.8
  DATA FORMATTING AND STANDARDIZATION WORKING WITH RELATIONSHIPS BETWEEN TABLES
- 12.9
  CREATING CUSTOM COLUMNS AND MEASURES WITH DAX (DATA ANALYSIS EXPRESSIONS)
- 12.10
  CREATING RELATIONSHIPS BETWEEN TABLES UNDERSTANDING
- 12.11
  CALCULATED COLUMNS AND MEASURES
- 12.12
  MANAGING MODEL PERFORMANCE WITH OPTIMIZATION TECHNIQUES
- 12.13
  CREATING BASIC CHARTS: BAR, LINE, PIE, AND COLUMN CHARTS
- 12.14
  ADVANCED VISUALS: SCATTER PLOTS, MAPS, FUNNEL CHARTS, & GAUGES USING SLICERS, FILTERS, & DRILL-THROUGHS.
- 12.15
  CREATING AND CUSTOMIZING DASHBOARDS FORMATTING AND STYLING REPORTS FOR BETTER USER EXPERIENCE USING POWER BI MARKETPLACE FOR CUSTOM VISUALS
- 12.16
  CALCULATED COLUMNS AND MEASURES
- 12.17
  AGGREGATION FUNCTIONS (SUM, AVERAGE, COUNT)
- 12.18
  TIME INTELLIGENCE FUNCTIONS (YTD, MTD, QTD)
- 12.19
  PUBLISHING REPORTS TO POWER BI SERVICE
- 12.20
  CREATING DASHBOARDS IN POWER BI SERVICE
- 12.21
  SHARING REPORTS AND DASHBOARDS WITH OTHERS
- 12.22
  EXPORTING REPORTS TO PDF
- 12.23
  POWER BI FOR BUSINESS USE CASES
- 12.24
  SALES ANALYSIS
- 12.25
  FINANCIAL REPORTING
- 12.26
  CONNECTING POWER BI DESKTOP WITH MOBILE FOR REAL TIME ANALYSIS
- 12.27
  CASE STUDIES AND LIVE PROJECTS
- 12.28
  RETAIL ANALYSIS DASHBOARDS
- 12.29
  TELECOM CHURN RATE DASHBOARD
- 12.30
  OLA COMPANY DASHBOARD
- 12.31
  BLINKIT ANALYSIS DASHBOARD
Git and GitHub
3
- 13.1
  Why Version Control System?
- 13.2
  Background of Version Control
- 13.3
  Difference Between Git and GitHub
R Programming
43
- 14.1
  R–OVERVIEW
- 14.2
  R – ENVIRONMENT SETUP
- 14.3
  R – BASIC SYNTAX
- 14.4
  R – DATA TYPES
- 14.5
  R – VARIABLES
- 14.6
  R – OPERATORS
- 14.7
  R – DECISION MAKING
- 14.8
  R – LOOPS
- 14.9
  R – FUNCTION
- 14.10
  R – STRINGS
- 14.11
  R – LISTS
- 14.12
  13.R – MATRICES
- 14.13
  R – ARRAYS
- 14.14
  R – FACTORS
- 14.15
  R – DATA FRAMES
- 14.16
  R – PACKAGES
- 14.17
  R – DATA RESHAPING Melt
- 14.18
  The Data
- 14.19
  R – CSV FILES
- 14.20
  R – EXCEL FILE Input as xlsx
- 14.21
  File
- 14.22
  R – BINARY FILES
- 14.23
  R – XML FILES
- 14.24
  R – JSON FILE
- 14.25
  R – WEB DATA
- 14.26
  R – DATABASES
- 14.27
  R – PIE CHARTS
- 14.28
  R – BOXPLOTS
- 14.29
  R – HISTOGRAMS
- 14.30
  R – LINE GRAPHS
- 14.31
  R – SCATTERPLOTS
- 14.32
  R – LINEAR REGRESSION
- 14.33
  R – MULTIPLE REGRESSION
- 14.34
  R – LOGISTIC REGRESSION
- 14.35
  R – NORMAL DISTRIBUTION
- 14.36
  R – BINOMIAL DISTRIBUTION
- 14.37
  R – POISSON REGRESSION
- 14.38
  R – ANALYSIS OF COVARIANCE
- 14.39
  R – TIME SERIES ANALYSIS
- 14.40
  R – NONLINEAR LEAST SQUARE
- 14.41
  R – DECISION TREE
- 14.42
  R – SURVIVAL ANALYSIS
- 14.43
  R – CHI SQUARE TEST
Understanding EDA
36
- 15.1
  The CRISP-DM Framework
- 15.2
  DEFINE THE BUSINESS PROBLEM – BUSINESS UNDERSTANDING
- 15.3
  OWNING AN IPL TEAM – BUSINESS UNDERSTANDING
- 15.4
  UNDERSTANDING RAW DATA
- 15.5
  PREPARING DATA FOR ANALYSIS
- 15.6
  DATA ANALYSIS: MODELLING
- 15.7
  MODEL EVALUATION
- 15.8
  MODEL DEPLOYMENT
- 15.9
  DATA SOURCING
- 15.10
  PUBLIC AND PRIVATE DATA
- 15.11
  DATA CLEANING
- 15.12
  FIXING ROWS AND COLUMNS
- 15.13
  MISSING VALUES
- 15.14
  STANDARDISING VALUES
- 15.15
  FILTERING DATA
- 15.16
  INVALID VALUES
- 15.17
  DATA DESCRIPTION
- 15.18
  UNIVARIATE ANALYSIS
- 15.19
  UNORDERED CATEGORICAL VARIABLES
- 15.20
  QUANTITATIVE VARIABLES – SUMMARY METRICS
- 15.21
  QUANTITATIVE VARIABLES – UNIVARIATE ANALYSIS
- 15.22
  SEGMENTED UNIVARIATE
- 15.23
  INTRODUCTION TO SEGMENTED UNIVARIATE ANALYSIS
- 15.24
  BASIS OF SEGMENTATION
- 15.25
  QUICK WAY OF SEGMENTATION
- 15.26
  COMPARISON OF AVERAGES
- 15.27
  COMPARISON OF OTHER METRICS
- 15.28
  BIVARIATE ANALYSIS
- 15.29
  BIVARIATE ANALYSIS ON CONTINUOUS VARIABLES
- 15.30
  BUSINESS PROBLEMS INVOLVING CORRELATION
- 15.31
  BIVARIATE ANALYSIS ON CATEGORICAL VARIABLES
- 15.32
  DERIVED METRICS
- 15.33
  WHAT ARE DERIVED METRICS?
- 15.34
  TYPE DRIVEN METRICS
- 15.35
  BUSINESS DRIVEN METRICS
- 15.36
  TYPES OF DERIVED METRICS: DATA DRIVEN
AI for Data-Driven Decision-Making
2
- 16.1
  Understanding the role of AI in analytics workflows
- 16.2
  Leveraging AI for smarter business insights
From Data to Decisions: Data Cleaning and Analysis With AI
3
- 17.1
  Generating quick reports and structured outputs
- 17.2
  Automating data cleaning and preprocessing
- 17.3
  Activity 1: Data Cleaning Dash – Hands-on cleaning exercise
Data Detective: Exploratory Data Analysis (EDA) With AI
2
- 18.1
  Identifying patterns, correlations, and anomalies
- 18.2
  Generating descriptive statistics and visual summaries
From Data to Story: Tailoring Your Narrative
2
- 19.1
  Translating raw insights into meaningful interpretations
- 19.2
  Crafting compelling data-driven narratives
From Data to Story: Visual Storytelling
2
- 20.1
  Designing impactful charts and dashboards
- 20.2
  Enhancing clarity and engagement with visualization techniques
Presenting Your Data Story: From Report to Impact
2
- 21.1
  Converting analysis into executive-level reports
- 21.2
  Structuring presentations for decision-making impact
AI for Coding Assistance
3
- 22.1
  Automating repetitive coding tasks
- 22.2
  Writing analytics scripts in Python/R with AI support
- 22.3
  Generating statistical models and visualizations
Learning Outcomes
By the end of this course, learners will be able to:
1. Apply AI techniques to clean, analyze, and explore data efficiently.
2. Identify patterns, trends, and insights through exploratory analysis.
3. Transform raw data into compelling stories and visualizations.
4. Create impactful reports and presentations for decision-making.
5. Use AI to enhance coding, automation, and advanced analytics tasks.
0
CASE STUDIES
8
- 24.1
  INDUSTRIAL CASE STUDIES
- 24.2
  CASE STUDY ON FORMATTING THE DATA
- 24.3
  COST CALCULATION CASE STUDY
- 24.4
  CASE STUDY ON SHOE COMPANY
- 24.5
  ADVANCED CHARTS CASE STUDY
- 24.6
  ADVANCE PIVOT BASED REPORT GENERATION
- 24.7
  SALES CASE STUDY FOR A TOY COMPANY
- 24.8
EDA PROJECTS
7
- 25.1
  PROJECT ON DRUG REVIEWS DATASET
- 25.2
  PROJECT ON IPL / FOOTBALL DATA ANALYSES
- 25.3
  PROJECT ON EXPLORING FACTORS OF LIFE EXPECTANCY
- 25.4
  PROJECT ON TIME SERIES FORECAST ON ENERGY CONSUMPTION
- 25.5
  PROJECT ON LOAN PREDICTION
- 25.6
  PROJECT ON HOME PRICE PREDICTION
- 25.7
  PROJECT ON OTT ( NETFLIX / HOTSTAR DISNEY )

Advanced Data Analytics

Curriculum

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