Data Analytics Overview
Introduction
Data Analytics: Importance
Digital Analytics: Impact on Accounting
Data Analytics Overview
Types of Data Analytics
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
Data Analytics: Amazon Example
Data Analytics Benefits: Decision-making
Data Analytics Benefits: Cost Reduction
Data Analytics Benefits: Amazon Example
Data Analytics: Other Benefits
Key Takeaways
Dealing with Different Types of Data
Introduction
Terminologies in Data Analytics – Part One
Terminologies in Data Analytics – Part Two
Types of Data
Qualitative and Quantitative Data
Data Levels of Measurement
Normal Distribution of Data
Statistical Parameters
Key Takeaways
Business Analytics with Excel
1.Functions and Formulas
Formulas with Multiple Operators
Inserting and Editing a Function
Auto Calculate and Manual Calculation
Defining Names
Using and Managing Defined Names
Displaying and Tracing Formulas
Understanding Formula Errors
Using Logical Functions (IF)
Using Financial Functions (PMT)
Using Database Functions (DSUM)
Using Lookup Functions (VLOOKUP)
User Defined and Compatibility Functions
Financial Functions
Date & Time Functions
Math & Trig Functions
Statistical Functions
Lookup & Reference Functions
Database Functions
Text Functions
Logical Functions
Information Functions
Engineering and Cube Functions
2. Working with Data Ranges
Sorting by One Column
Sorting by Colors or Icons
Sorting by Multiple Columns
Sorting by a Custom List
Filtering Data
Creating a Custom AutoFilter
Using an Advanced Filter
3. Working with PivotTables
Creating a PivotTable
Specifying PivotTable Data
Changing a PivotTable’s Calculation
Filtering and Sorting a PivotTable
Working with PivotTable Layout
Grouping PivotTable Items
Updating a PivotTable
Formatting a PivotTable
Creating a PivotChart
Using Slicers
Sharing Slicers Between PivotTables
4. Analyzing and Organizing Data
Creating Scenarios
Creating a Scenario Report
Working with Data Tables
Using Goal Seek
Using Solver
Using Text to Columns
Grouping and Outlining Data
Using Subtotals
Consolidating Data by Position or Category
Consolidating Data Using Formulas
5. Working with the Web and External Data
Inserting a Hyperlink
Importing Data from an Access Database or Text File
Importing Data from the Web and Other Sources
Working with Existing Data Connection
6. Customizing Excel
Customizing the Ribbon
Customizing the Quick Access Toolbar
Using and Customizing AutoCorrect
Changing Excel’s Default Options
Creating a Custom AutoFill List
Creating a Custom Number Format
7. Working on Live Data and Dashboards
Creating dashboards on company specific data
Working on Live data
Dashboards with the help of Developer Ribbon.
Working with critical & Complex formulas
Tableau
Understand how Tableau Desktop fits within the Tableau family of products
Combine data sources for use by Tableau
Connect to a variety of sources including flat files and databases
Understand data types and roles
Use key operations in Tableau – filtering, sorting, grouping and creating sets
Work with extracts (file formats used by Tableau)
Build and format data visualizations
Work with maps and location-based data
Create interactive dashboards by using parameters, calculations and actions
Publish dashboards and visualizations
Working with bins, groups and parameters
Working with folders
Creating story
SPSS
Making data visualizations
Creating regression variable plots
Importing data and recoding variables
Computing frequencies and correlations
Reliability analysis
k-means clustering
Decision tree classification
Analyzing data
Building predictive models
Exporting your work
Programming Basics and Data Analytics with Python
1. Introduction
Variables
Data Types with Python
Assisted Practice: Data Types in Python
Keywords and Identifiers
Expressions
Basic Operators
Operators in Python
Functions
Search for a Specific Element from a Sorted List
Create a Banking System Using Functions
String Operations
String Operations in Python
Tuples
Tuples in Python
Lists
Lists in Python
Sets
Sets in Python
Dictionaries
Dictionary in Python
Dictionary and its Operations
Conditions and Branching
Check the Scores of a Course
While Loop
Find Even Digit Numbers
Fibonacci Series Using While Loop
For Loop
Calculate the Number of Letters and Digits
Create a Pyramid of Stars
Break and Continue Statements
2. File handling, Exception handling, and Package handling
Learning Objectives
File Handling
File Opening and Closing
Reading and Writing Files
Directories in File Handling
Assisted Practice: File Handling
Errors and Exceptions
Assisted Practice: Exception Handling
Modules and Packages
Assisted Practice: Package Handling
3. Mathematical Computing using NumPy
Learning objectives
NumPy
Create and Print Numpy Arrays
Operations
Executing Basic Operations in Numpy Array
Performing Operations Using Numpy Array
Demonstrate the Use of Copy and Use
Manipulate the Shape of an Array
4. Data Manipulation with Pandas
Learning Objectives
Introduction to Pandas
Data Structures
Create Pandas Series
Dataframe
Create Pandas DataFrames
Create Pandas DataFrames
Missing Values
Handle Missing Values
Data Operation
Data Operations in Pandas DataFrame
Data Operations in Pandas DataFrame
Data Standardization
Pandas SQL Operations
Pandas SQL Operations
5. Data visualization with Python
Learning objectives
Data Visualization
Considerations of Data Visualization
Factors of Data Visualization
Python Libraries
Create Your First Plot Using Matplotlib
Line Properties
Create a Line Plot for Football Analytics
Multiple Plots and Subplots
Create a Plot with Annotation
Create Multiple Plots to Analyze the Skills of the Players
Create Multiple Subplots Using plt.subplots
Types of plots
Create a Stacked Histogram
Create a Scatter Plot of Pretest scores and Posttest Scores
Create a Heat Map to Analyze the Sepal Width, Petal Length, and Petal Width of an Iris Dataset
Create a Pie Chart
Create an Error Bar
Area Chart to Display the Skills of the Players
Create a Word Cloud of a Random Data
Create a Bar Chart
Create Box Plots
Create a Waffle Chart
Seaborn and Regression Plots
Introduction to Folium
Maps with Markers
Kernel Density Estimate Plots
Analyzing Variables Individually
Key Takeaways
Visualize the Sales Data
Data Science, Data Analytics and Machine Learning
Introduction
The Data Science Domain
Data Science, Data Analytics, and Machine Learning – Overlaps
Data Science Demystified
Data Science and Business Strategy
Successful Companies Using Data Science
Travel Industry
Retail
E-commerce and Crime Agencies
Analytical Platforms Across Industries
Key Takeaways.
Data Science Methodology
Introduction
Data Science Methodology
From Business Understanding to Analytic Approach
From Requirements to Collection
From Understanding to Preparation
From Modeling to Evaluation
From Deployment
Key Takeaways.
Data Analytics in Different Sectors
Introduction
Analytics for Products or Services
How Google Uses Analytics
How Linkedin Uses Analytics
How Amazon Uses Analytics
Netflix: Using Analytics to Drive Engagement
Netflix: Using Analytics to Drive Success
Media and Entertainment Industry
Education Industry
Healthcare Industry
Government
Weather Forecasting
Key Takeaways
Analytics Framework and Latest trends
Introduction
Case Study: EY
Customer Analytics Framework
Data Understanding
Data Preparation
Modelling
Model Monitoring
Latest Trends in Data Analytics
Graph Analytics
Automated Machine Learning
Open Source AI
Key Takeaways
Introduction to SAS (Statistics Analysis System)
Program Structure, Various screens, PDV process
Automatic Variables
Input of the Data, Handling dates in SAS
Types of Text file inputs in SAS
Proc Import and Export procedures
Various data handling options
Subsetting of Data
Controlling input and outputs
Functions in SAS – Arithmetic, Character/String Functions and Date Functions
Retain Usages, Conditional Statements and Looping in SAS
Procedures in SAS
Merging the data, Working with Multiple data files
Handling the ODS – Output Delivery System
Debugging in SAS
Introduction to PROC SQL
Advance SAS
MACRO Programming
Few Advance Statistical Procedures
