- What is Data Analytics & Data Science
- Different types of Data Analytics (Descriptive, Predictive, Prescriptive)
- What is Artificial Intelligence
- What is IOT
- Machine Learning (Supervised & Unsupervised Learning)
- Deep Learning (Artificial Neural Networks, CNN)
- Overview of Banking, Healthcare, Telecom domain
- Working with multiple data sources – RDBMS (SQL Server, Oracle, My SQL, DB2), NOSQL (MongoDB, Cassandra, CouchDB)
- Real world Applications of Machine Learning & Deep Learning
- What to expect from this course (Salary, Market trends, job roles, Domain)

-Excel Tables
 Sparklines
 Conditional Formatting(Functions: Logical, Text, Aggregate)
 Excel Data Tables
-Slicers
 Introduction
 Slicers to filter your table data
-Pivot Tables
 Creating Pivot Tables
 Use multiple ways to filter data in Pivot Tables
 Slicers in Pivot Tables
-Power Pivot
 Introduction to Power Pivot and it’s uses
 Pivot Tables v/s Power Pivot Tables
 Creating Power Pivot Tables
 Tables & Charts Combination
 Power Pivot Hierarchies
 Power Pivot using Slicers

-Flexible data aggregation using pivot tables and pivot charts to create Dashboards
 Introduction to Dashboards
 Types of Dashboards
 Analytical Dashboards
 Dynamic Dashboards with Live Data
 Excel Charts for Dashboards
-(Pie Chart, Stacked Bar Chart, Scatter Chart, Bubble Chart, Stock Chart, Radar Chart)
-Data Filter in Pivot to get Subsets of data & Advance Charts
 Dashboard Data & Formats
 Waterfall Chart
 Band Chart
 Gantt chart
 Thermometer Chart
 Histogram
 Pareto Chart
 Funnel Chart
 Box and Whisker Chart
 Waffle Chart
-Reporting Layouts using formulas to aggregate the data as an alternative to Pivot Tables
 Power view visualisation
 Combination of power view visualisation
 Interactive Nature of Charts in Power View Visualizations
 Slicers & Tiles in Power View

-Data Gathering & Transformation from multiple sources
 Combining Multiple worksheet (Vlookup/Hlookup/Index/Match)
 Get and Transform data
 Concatenate Formatting
-Discover and combine data in mashups
 Append Data
 Merging Data
 Data Blending
-Data model creation
-Data Exploration, Analysis and Visualisation

-What is RDBMS?
-What is SQL?
 What is SQL
 SQL Process
 SQL Architecture
-Why SQL?
-SQL Commands
 Data Definition Language
 Data Manipulation Language
 Data Control Language
 Data Query Language
-Fundamental of SQL
-SQL Syntax
 Select Statement
 Distinct Clause
 Where Clause
 AND/OR Clause
 IN Clause
 Between Clause
 Like Clause
 Order by Clause
 Group by Clause
 Count Clause
 Having Clause
 Create Index Statement
 Drop Table Statement
 Drop Index Statement
 Truncate Table
 Alter Table
 Update Statement
 Delete Statement
 Commit Statement
 Roll back Statement
-SQL Data Types
 Exact Numeric Data Types
 Approximate Numeric Data Types
 Date and Time Data Types
 Character Strings Data Types

-SQL Operators
 Arithmetic Operators
 Comparison Operators
 Logical Operators
-SQL Sorting
-SQL Joins
 Relationships in SQL
 Understanding Joins
 Types of Joins
• Inner Join
• Outer Join
• Right Join
• Left Join
• Full Join
• Self Join
• Cartesian Join
 Concept of Keys
-Functions
 Brief about Function
-SQL Union Clause  The Union All Clause
-SQL Alias Syntax -SQL Truncate Table -SQL Using Views

 Performing dataset with MYSQL

-R Programming
 Introduction to R Programming
 Data Types in R
 Functions in R
 Summarizing data by using various functions
 Indulge into a class activity to summarize the data
 Various subsetting methods

-Data Importing
 Data import technique in R
 Import data from spreadsheets and text files into R
 Install packages used for data import
 Connect to RDBMS from R using ODBC and basic SQL queries in R
 Perform basic web scrapping
-Data Manipulation in R
 Know the various steps involved in data cleaning
 Functions used for data inspection
 Tacking the problem faced during data cleaning
 How and when to use functions like grep, grepl, sub, gsub,regexpr, gregexpr, strsplit
 How to coerce the data

-Data Exploration in R
 What is data exploration
 Data exploring using Summary(), mean(), var(), sd(), unique()
 Using Hmisc package and using summarize, aggregatefunction
 Learning correlation and cor() function and visualizing the same using corrgram
 Visualizing data using plot and its different flavours
 Boxplots
 Dist function

-Data Visualisation in R
 Gain understanding on data visualization
 Learn the various graphical functions present in R
 Plot various graph like tableplot, histogram, boxplot etc
 Customize graphical parameters to improvise the plots
 Understand GUIs like Deducer and R commander
 Introduction to spatial analysis

-Fundamentals of Statistics
 Basic statistics; descriptive and summary
 Inferential statistics
 Statistical tests
-Data Prep and Reduction techniques
 Need for data preparation
 Outlier treatment
 Flat-liners treatment
 Missing values treatment
 Factor Analysis

-Basic Analytics
 Statistics Basics Introduction to Data Analytics and Statistical Techniques
 Types of Variables, measures of central tendency and dispersion
 Variable Distributions and Probability Distributions
 Normal Distribution and Properties
 Central Limit Theorem and Application
 Hypothesis Testing Null/Alternative Hypothesis formulation
 One Sample, two sample (Paired and Independent) T/Z Test
 P Value Interpretation
 Analysis of Variance (ANOVA)
 Chi Square Test
 Non Parametric Tests (Kruskal-Wallis, Mann-Whitney, KS)
 Correlation
-Introduction to Business Analytics
 Relevance in industry and need of the hour
 Types of analytics – Marketing, Risk, Operations etc
 Future of analytics and critical requirement

-Basics of regression analysis
 Linear regression
• Simple Linear Regression
• Multiple Regression
 Logistic regression
-Interpretation of results
-Multivariate Regression modeling

-Classification Modeling
 K-Nearest Neighbours (K-NN)
 Support Vector Machines
 Decision Tree Classification
• Understand what is Decision Tree
• Algorithms for Decision Tree
• Greedy approach: Entropy and information gain
• A perfect decision tree
 Random Forest Classification
• Understand the concept of random forest
• How random forest work
• Features of random forest

-Time series modeling
-Cross-sell and Up-sell opportunities and modeling
-Churn prediction models and management

-Basics clustering
-Deciles analysis
-K-Means Clustering
-Hierarchical Clustering
-Cluster evaluation and profiling
-Interpretation of results

-Introduction to Data Visualization with Tableau
-Data Import and Management
-Data Type and Operation
-Visualizations Deep Dive
-Data Organization and Scripting
-Playing with Time Dimension
-What is Your Location?
-Incremental Loading and Blending
-The World is Your Visualization
-Statistical Analysis with Tableau & R
-Sharing Insights with Enterprise Dashboards
-Analyse Project data and extract meaningful information from it
-Create Dashboards and Stories from data sets

-Project: A dashboard should be created that would contain
 A list of Orders returned by the Customers (in terms of refund)
 Top 10 Countries mapped on the World Map that had most of the refunds
 Predict the refund for next 1 year category-wise
 Actions to be Performed to Create the Dashboard
 Create hierarchies and folders in the dataset provided
 Generate a list of order returned from customers and compare it to the original sales, sort the visualization in ascending
 Order in terms of returned order, for top 10 countries in terms of refunds
 Map top 10 countries, with most of the refunds on the world map
 Predict the returned sale with lowest and actual forecast of the data
 Add a URL action to represent details of the countries
 Finally publish your work to Tableau Server/ Online

-Live Project to get hands on tools with one of these specified domains
 Specialization in Banking
 Specialization in Healthcare
 Specialization in Telecom
 Specialization in Insurance