Python

Python Environment Setup and Essentials

• Introduction to Python Language
• the advantages of Python over other programming languages
• Python installation (Windows & Linux distribution for Anaconda Python)
• deploying Python IDE
• basic Python commands
• data types
• variables
• keywords

Python language Basic Constructs

• Built-in data types in Python
• tabs and spaces indentation
• code comment Pound # character
• variables and names
• Python built-in data types (Numeric, int, float, complex, list tuple, set dict)
• containers
• text sequence
• exceptions
• instances
• classes
• modules
• Str(String)
• Ellipsis Object
• Null Object
• Ellipsis
• Debug
• basic operators (comparison, arithmetic, slicing and slice operator, logical, bitwise)
• loop and control statements (while, for, if, break, else, continue)

OOP concepts in Python and database connection

• How to write OOP concepts program in Python
• connecting to a database
• classes and objects in Python
• OOPs paradigm
• important concepts in OOP (polymorphism, inheritance, encapsulation)
• Python functions, return types and parameters
• Lambda expressions
• connecting to database and pulling the data

Operators and Keywords for Sequences

• The xrange() function
• List Comprehensions
• Generator Expressions
• Dictionaries and Sets.

Working with missing data

• Reading Data from CSV, Excel, JSON
• Writing Data to CSV, Excel, JSON, HTML
• Reading data from database and storing in data frame
• Writing data frame to database
• Handling PDF files - tabula-py

Python Important Libraries

NumPy

• Introduction to arrays and matrices
• indexing of array
• data types
• broadcasting of array math
• standard deviation
• conditional probability
• correlation and covariance

Pandas

• Pandas
• i. Series
• Constructing from dictionaries
• Custom index
• Data filtering
• ii. Data Frames
• Constructing from a dictionary
• with values as lists
• Custom indexing
• Rearranging the columns
• Accessing values loc(), iloc(), at()&iat()
• Setting values
• Sum
• cumulative sum
• Assigning a column to the data frame
• Adding a new column
• Deleting a column
• Slicing
• Indexing and Advanced indexing
• Boolean indexing
• Transposing
• Sort by
• Concatenate
• Merge
• 1. Inner join
• 2. Outer join
• 3. Left outer join
• 4. Right outer join
• 5. Merge on columns
• Join
• Group By- Aggregation
• Data Munging

SciPy

• Introduction to SciPy and its functions
• building on top of NumPy
• cluster
• linalg
• signal
• optimize
• integrate
• subpackages
• SciPy with Bayes Theorem

Matplotlib

• How to plot graph and chart with Python
• various aspects of line
• scatter
• bar
• histogram
• subplots

Math

• Math function

Regex

Scraping using BS4

Statistics and Probability

Statistics

• What is Statistics
• Descriptive Statistics
• Central Tendency Measures
• The Story of Average
• Dispersion Measures
• Data Distributions
• Central Limit Theorem
• What is Sampling
• Why Sampling
• Sampling Methods
• Inferential Statistics
• What is Hypothesis testing
• Confidence Level & Interval
• Degrees of freedom
• what is pValue
• What is ANOVA
• Correlation vs Regression
• Uses of Correlation & Regression

Linear & logistic Regression

• Case study
• Linear Regression Overview
• Simple Linear Regression
• Multiple Linear Regression
• logistic regression

Probability

• Probability
• Naïve Bayes theorem

How to handle missing data

• Missing Data

Reading and using different Charts

• Box & scatter plot
• Histograms
• Heat map
• correlation chart

R Programming

• Why R and Importance of R in Analytics
• Installation R and R-studio
• Data Types
• R Matrix : Create, Print, add Column, Slice
• Operators
• Decision Making
• Loops
• Lists
• Sets
• Vectors
• Strings
• Matrices
• Arrays
• Factors
• Functions( Built in and user defined)
• Data manipulation
• Handling different types of data
• Reshaping the data

Web scraping

• Exploratory data analysis using R
• Data visualization
• Descriptive statistics using R
• Exploratory Data analysis

SQL & Data Warehousing

Introduction to SQL

• Data & Database etc
• SQL Server Management Studio
• Creating Databases : Files [MDF, LDF]
• DDL, DML operations

Creating Tables in SQL Databases

• Data Types for its usage
• Single Row Inserts, Multi Row Inserts
• Data Retrieval for analysis (select etc)
• SELECT with WHERE Conditions
• CHAR Versus VARCHAR Data Types
• VARCHAR & NVARCHAR Data Types

AND and OR Operators Usage

• IN Operator and NOT IN Operator
• BETWEEN, NOT BETWEEN Operators
• LIKE and NOT LIKE Operators
• Using Wild Card Characters
• IS and IS NOT Operator, NULLs
• Using DISTINCT, TOP Keywords

UPDATE Statement & Conditions

• DELETE Statement & Conditions
• TRUNCATE & DELETE Differences
• Table Data / Content Modification
• Table Structure Modifications (DDL)
• ALTER, ADD and DROP Statements
• Removing Tables and Databases

Schemas : Usage, Creation

• Table Migrations across Schemas
• Import / Export Wizard From SSMS
• GO Statement, SQL BATCH Concept

JOINS - Table Comparisons Queries

• INNER JOIN - Examples
• Left Outer Joins with Example Queries
• Right Outer Joins with Example Queries
• FULL Outer Joins with real time scenario
• CROSS join

Stored procedures

• functions

Using GROUP BY with WHERE, ON

• Joins with GROUP BY and HAVING
• Joins with Sub Queries, IS NULL
• Date and Time Functions: Getdate()
• Cast and Convert with Getdate()
• Data conversion

Date & Time Styles, Data Formatting

• DateAdd and DateDiff Functions
• String Functions: Left and Right
• SubString, Replace and CharIndex
• Reverse, Len, LTrim and RTrim

Joining 3 and 4 Tables in T-SQL

• Using Joins with Subqueries
• Using Joins with Nested Sub Queries
• IIF ( ) and CASE Statement Usage
• Using IIF and CASE in Joins
• Joins in Group By, Rollup, Cube
• Replacing Nulls: Isnull, Coalesce

ROW_NUMBER() and RANK() Queries

• DENSE_RANK, Sequence Identification
• PARTITION BY and RowNumber ( )
• CTE : Common Table Expressions
• Using CTEs for Avoiding Self Joins
• Using CTEs for Avoiding Subqueries
• PIVOT versus UNPIVOT Queries

Exercise with real time data

Visualization & Dashboard

Introduction to Data Visualization

Theories of Human Perception

Basics of Data Visualization

Designing your Visualizations

Visualization Toolbox

Tableau Desktop

• Scenario
• Objectives
• Application Terminology and Definitions
• Opening and Closing Tableau
• Data Source Page
• Tableau Workspace
• Files and Folders
• Getting Started with Tableau
• View Terminology and Definitions
• View Sections
• Data Terminology and Definitions
• Data Types
• Data Roles: Dimension vs. Measure
• Data Roles: Continuous vs. Discrete
• Changing Data Roles

Data Connections in the Tableau

• Connecting to Tableau Data Server
• What is a Join
• Types of Joins
• When to Use Joining
• Enabling Right Outer Join
• Right Outer Join and Custom SQL Enabled
• What is Data Blending
• When to Use Data Blending
• Data Blending in Tableau
• Differences Between Joining and Blending
• Joining vs. Blending
• Writing Custom SQL
• Prepare your Data for Analysis

Organizing and Simplifying Data

• Organizing and Simplifying Data
• Objectives
• Filtering Data
• What is a Filter
• Applying a Filter to a View
• Filtering on Dimensions
• Filtering on Dimensions Functions
• Aggregating Measures
• Filtering on Measures
• Filtering on Dates
• Quick Filters
• Sorting of Data
• What is Sorting
• Sorting Data in Tableau
• Types of Sorting
• Creating Combined Fields
• Combined Fields
• Creating Groups and Defining Aliases
• What is a Group
• What are Aliases
• Defining an Alias
• Working with Sets and Combined Sets
• Sets
• Combined Sets
• Working with Groups and Sets
• Drill to Other Levels in a Hierarchy
• Grand totals and Subtotals
• Adding Totals
• Adding Totals
• How to Define Aggregations
• Changing Aggregation Function
• Tableau Bins
• Bins
• Fixed Sized Bins
• Variable Sized Bins

Formatting and Annotations

• Formatting and Annotations
• Adding Caption to Views
• Click Interaction Adding Title to View
• Click Interaction2 Adding Captions to View
• Using Titles Captions and Tool tips
• Adding Tooltips to Views
• Using Title Caption and Tooltip
• Formatting the Axes
• Edit Axis Option
• Formatting Views with Labels and Annotations
• Format Window
• Format Mark Labels

Special Field Types

• Date Hierarchies
• Drilling in the Time Hierarchy
• Pivoting Date Parts on Shelves
• Differentiate Between Discrete and Continuous Dates
• Using Continuous Dates
• Using Discrete Dates
• Working with Discrete and Continuous dates
• What are Custom Dates
• Creating and Using Custom Dates
• Fiscal Year
• Define a Date Field on a Fiscal Year
• Relative Date Filters
• Importing Date Dimensions in Tableau from a Cube
• Work with Date Hierarchies on Cubes

Chart Types

• Chart Types
• Working with Combined Axis
• Working with Combination Charts
• Understanding geocoding and geographic mapping in tableau
• Combined Axis Graph and Scatter Plot
• Describe text and highlight tables
• Work with Pages Shelf and Create Motion Charts
• Heat Maps
• Using Bins and Histograms
• Using Histograms
• Using Pie Charts
• Compare Measures Using Bullet Charts
• Using Bar in Bar Charts

Define Advanced Chart Types

• Using Pareto Charts
• Creating Pareto Charts
• Using Waterfall Charts
• Using Gantt Charts
• Working with box plots
• Using Sparkline Charts

Calculations

• Calculations
• Objectives
• Strings Date Logical and Arithmetic Calculation
• Working with Strings Date Logical and Arithmetic Calculations
• Using Strings Date Logical and Arithmetic Calculations
• Working with Arithmetic Calculations
• Aggregation Options
• Working with Aggregation Options
• Grand Totals and Sub-Totals
• Quick Table Calculations
• Creating Quick Table Calculations
• Working with Quick Table Calculations
• Automatic and Custom Split
• Ad-hoc Analytics
• LOD Calculations

Creating and using Parameters

• Creating and using Parameters
• Objectives
• What is a Parameter
• Creating a Parameter
• Exploring Parameter Controls
• Work with Parameters
• Click Interaction Working with Parameters

Dashboards

• Dashboards
• Objectives
• Build Interactive Dashboards
• What is a Dashboard
• Building Dashboards
• Best practices for creating effective dashboards
• Comprehending Best Practices
• Creating a Dashboard and Importing Sheets
• Interaction Exploring Dashboard Actions
• Use of Running Actions
• Using Dashboard Actions
• Sharing your Work
• How to Share your Reports
• Exporting your Work

Machine Learning Basic's

Machine Learning

Supervised Learning

• What is supervised Learning
• Algorithms in Supervised Learning
• Steps in Supervised learning
• Regression and Classification
• Regression Vs Classification
• Accuracy Metrics
• Classification
• Decision Trees and Model Tuning
• Pros and Cons
• Evaluation Metrics
• Random Forest and Model Tuning
• Evaluation Metrics
• Pros and Cons
• KNN Algorithm
• Evaluation Metrics
• Pros and cons
• Support Vector Machines
• Kernel models
• Evaluation Metrics
• Pros and cons

Unsupervised Learning

• Dimension Reduction
• Principal Component Analysis
• Singular Value Decomposition

Clustering

• K-means Clustering

Text Mining

Intro to Text Mining

• Applications
• Extracting text from website, URLs,PDF
• Text Cleaning
• Text clustering
• Word cloud
• N-grams
• Sentiment Analysis
• NLP

Forecasting

• Introduction to forecasting
• Data cleaning and manipulation
• Time series
• Components of Time series
• Trend Analysis
• Forecasting Methods
• Smoothing methods
• Auto regressive model
• ETS model
• ARIMA Model
• Anomaly detection

Association Rules

• Introduction
• Metrics lift, support, Confidence and conviction
• Apriori Model

Analytical Thinking

What is analytics

• What is analytics
• Linear programming (LP)
• How to solve using Excel
• Python library- PuLP

Analytics Using Python

• Supply chain Analytics
• Marketing Analytics - RFM (and/or CLV, touch upon conjoint analysis)
• Social media Analytics – Twitter (or facebook)
• Pricing analytics

How to build analytical thinking

• How to view data
• Feature Selection and Pre-processing
• How to select the right data
• Which are the best features to use
• Additional feature selection techniques
• A feature selection case study

Business & Process Understanding

• Connecting the dots
• How to understand a process
• How to take KT
• Questions to ask to understand any process
• Business understanding
• Problem Statement and Analysis
• Various approaches to solve a Business & Data Analytics Problem
• Pros and Cons of different approaches
• How to scale analytics

Employability Skill & Interview

• Mock interviews
• Group Discussions
• Practical Examination