Curriculum
17 Sections
315 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
2.0
2.1
2.2
Interactive HR Dashboard In Excel
2.3
Project Management Dashboard In Excel
2.4
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
5.0
Set Theory
5.1
Types of Joins
5.2
Types of Joins: A Demonstration
5.3
Outer Joins: A Demonstration
5.4
Views with Joins
5.5
Set Operations with SQL
5.6
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
7.0
Introduction to NumPy
7.1
Basics of NumPy
7.2
Operations Over 1-D Arrays
7.3
Multidimensional Arrays
7.4
Creating NumPy Arrays
7.5
Mathematical Operations on NumPy
7.6
Mathematical Operations on NumPy II
7.7
Computation Times in NumPy vs Python Lists
7.8
Case Study
Python Pandas
11
8.0
Introduction to Pandas
8.1
Basics of Pandas
8.2
Pandas – Rows and Columns
8.3
Describing Data
8.4
Indexing and Slicing
8.5
Operations on Data frames
8.6
Group and Aggregate Functions
8.7
Merging Data Frames
8.8
Pivot Tables
8.9
Practice Exercise
8.10
Case Study
Python Matplotlib
11
9.0
Introduction to Data Visualisation with Matplotlib
9.1
Introduction to Matplotlib
9.2
The Necessity of Data Visualisation
9.3
Visualisations – Some Examples Facts and Dimensions
9.4
Bar Graph
9.5
Scatter Plot
9.6
Line Graph and Histogram
9.7
Outliers Analysis with Boxplots
9.8
Subplots
9.9
Choosing Plot Types
9.10
Project
Python Seaborn
13
10.0
Introduction
10.1
Distribution Plots & Styling Options
10.2
Pie – Chart and Bar Chart
10.3
Scatter Plots & Pair Plots
10.4
Revisiting Bar Graphs and Box Plots
10.5
Heatmaps
10.6
Line Charts
10.7
Stacked Bar
10.8
Charts Case Study Summary
10.9
Plotly Practice Questions
10.10
Practice Questions Solution
10.11
Data Visualisation Practice Questions
10.12
Case Study
Statistics Essentials
11
11.0
Mean, Median, Mode
11.1
Standard Deviation, variance,
11.2
Standard Error Co-Relation ,Coefficient
11.3
Probability
11.4
Regression analysis
11.5
Multiple Regression
11.6
Logistic Regression
11.7
Time Series
11.8
Normal Distribution
11.9
Binomial distribution
11.10
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
13.0
Why Version Control System?
13.1
Background of Version Control
13.2
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
CASE STUDIES
8
16.1
INDUSTRIAL CASE STUDIES
16.2
CASE STUDY ON FORMATTING THE DATA
16.3
COST CALCULATION CASE STUDY
16.4
CASE STUDY ON SHOE COMPANY
16.5
ADVANCED CHARTS CASE STUDY
16.6
ADVANCE PIVOT BASED REPORT GENERATION
16.7
SALES CASE STUDY FOR A TOY COMPANY
16.8
EDA PROJECTS
7
17.1
PROJECT ON DRUG REVIEWS DATASET
17.2
PROJECT ON IPL / FOOTBALL DATA ANALYSES
17.3
PROJECT ON EXPLORING FACTORS OF LIFE EXPECTANCY
17.4
PROJECT ON TIME SERIES FORECAST ON ENERGY CONSUMPTION
17.5
PROJECT ON LOAN PREDICTION
17.6
PROJECT ON HOME PRICE PREDICTION
17.7
PROJECT ON OTT ( NETFLIX / HOTSTAR DISNEY )
Advanced Data Analytics with AI
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