Post Graduate Program inPre-Graduate Program in Data Analytics and Data Science

• Features and uses of Python
• Program execution
• Installation of IDE
• Identifiers and keywords
• Types of comments
• Data types
• Variables
• Arithmetic operators
• Assignment operators
• Input and print statements

• Definition of string
• Operations accessing string elements
• Relational operators
• Logical operators
• Conditional expressions
• If, If..else, If..elif

• Use of while and for
• Break and continue
• Pass and else statements
• Formatted input and output

• Use of while and for
• Break and continue
• Pass and else statements
• Formatted input and output

• Defining a function
• Types of arguments
• Global and local variables
• Functions as arguments
• Implementing Lambda functions
• Map, Reduce, and Filter functions

• Read-write operations
• With the keyword
• File opening modes
• Moving within a file
• Serialization
• File and directory operations
• Importing a module
• Variations of import
• Third-party packages

• Class variables
• Methods
• Operator overloading
• Reuse
• Containership
• Inheritance

• Iterables and iterators
• Syntax errors and exceptions for:
• try-except
• else
• finally blocks

• Installing Pandas
• Loading files
• CSV files
• JSON files
• Dataframes

• NumPy: Introduction
• OpenCV
• Images and NumPy Arrays

• Introduction to Tkinter
• Setting up a GUI with widgets
• Connecting GUI widgets with callback functions

• SQLite: Introduction
• Connecting and inserting data to SQLite via Python
• Selecting, deleting, and updating SQLite records

In this week, students will learn about

• Data Science: Introduction
• Data Science Applications
• Why SQL is required for Data Science
• Database Management System (DBMS)
• Relational Database Management System (RDBMS)
• Basic terminology in RDBMS
• Data Constraints
• Entity Relationship Model
• What SQL is
• Categories of SQL Commands
• Hands-on execution of simple SQL statements on RDBMS tool

In this week, students will learn about

• Detailed SQL Data types
• Creating databases
• Create Tables
• Using Constraints
• Inserting Table
• Altering Table structure
• Dropping Database and Table
• Deleting and Updating
• Hands-on importing of sample database schema

In this week, you will learn about

• Select statements
• Removing Duplicate use of Alias
• Use of Where
• Use of Wildcards
• Limit clause
• Arithmetic Operators
• Mathematical Functions
• Hands-on creating of backups and restore for large database

In this week, students will learn about

• Generating Strings
• String Functions
• Date Functions
• Conversion Functions

In this week, students will learn about

• Comparison Operators
• Logical Operators
• Order By
• Group By
• Aggregate Functions
• Using aggregate functions with Group by clause
• Union Operator
• Sub-query

In this week, students will learn about

• The need to Join Multiple Tables
• Cartesian Product
• Inner Join
• Left Join
• Right Join
• Self Join
• Delete Join
• Update Join
• Hands-on demonstration of joining more than two tables in a sample database

In this week, students will learn about

• What Data Exploration is
• Structure of Data
• Understanding the E-R Diagram
• How to Use SQL for Data Exploration
• Significance of
• Joins
• Sub queries
• Inbuilt functions
• Other important capabilities of SQL for data exploration
• Hands on demonstration
• Working with NULL values
• Making trends in Data
• Identifying Outliers
• Creating Data Summary

In this week, students will learn about

• Creating Index
• Use of Index
• Type of Index and Ine
• X Strategies
• Views
• Views for Data Analysis
• Multi-user database
• What is Transaction
• Save points
• Hands-on working on Multi user database environment

In this week, students will learn about

• Querying with Conditions
• The Searched Case Expression
• The Simple Case Expression
• Applications of Case Expression
• Common Error Codes
• Hands-on working with Json type data

In this week, students will learn about

• Stored Procedures for Data Analysis
• Creating Stored Procedures
• Removing Stored Procedures
• Altering Stored Procedures
• Conditional Statements
• Loops
• Hands-on working with cursors

In this week, you will get a hands-on understanding of the following

• Accessing MySQL data with MS Excel
• Running SQL statements with Excel
• Combining Excel and SQL statements for data representation

In this week, you will get a hands-on understanding of the following

• Working with Python
• Accessing SQL data with Python
• Running basic SQL statements with Python
• Running inbuilt python functions on SQL data

• This week we will learn about
  • Introduction to Artificial Intelligence
  • Introduction to Machine Learning
  • Supervised, Unsupervised, and Reinforced Learning
  • Introduction to Deep Learning
  • Modules needed to implement a Machine Learning model

• This week we will learn about
  • Set Theory
  • Algebra of Sets
  • Venn Diagrams

• This week we will learn about
  • Introduction to Probability
  • Axioms of Probability
  • Independent events
  • Mutually exclusive events
  • Conditional Probability
  • Bayes Theorem

• This week we will learn about
  • Measures of Central Tendence
  • Measures of Dispersion
  • Measures of Symmetry

• This week we will learn about
  • Concept of Random variable
  • Bernoulli distribution
  • Binomial distribution
  • Negative Binomial distribution
  • Geometric distribution
  • Hypergeometric distribution
  • Poisson distribution
  • Uniform distribution
  • Probability mass function and cumulative distribution function
  • Brief intro to Gamma exponential and normal distribution

• In this week, we will learn
  • Continuous distributions
  • Normal Distribution
  • Gamma Distribution
  • Exponential Distribution
  • Lognormal Distribution
  • Weibull Distribution
  • F Distribution
  • T Distribution
  • chi square Distribution
  • Probabiltiy Density Function
  • Cumulative Distribution Function

• This week we will learn about
  
  • Sampling
  • Probabilistic and Nonprobabilistic methods of Sampling Estimation
  • Estimation
  • Sample size estimation

• This week we will learn about
  • Introduction to hypothesis testing
  • Rejection region
  • Critical value
  • p-value

• This week we will learn about,
  
  • z - test
  • f - test
  • t - test
  • Anova - test

• This week we will learn about
  • Chi square test
  • Mann Whitney U test
  • Kruskal Wallis test
  • Sign test
  • Correlation
  • Chi square
  • Karl Pearson
  • Spearman Coefficient
  • Regression between variables
  • Implementation of statistical functions in Jupyter notebook

Tableau software allows businesses to clearly visualize, analyze, and report large data sets. In the industry, Tableau is a business intelligence tool used to make decisions supported by data. In order to develop effective dashboards and data visuals that can be studied and interpreted, learners must understand the UI and basic features.

This week will cover

• Basics and Installation of Tableau
• Types of Products and Roles
• Interface and Workflow
• Tableau Dashboards vs PPT
• Connecting to Data
• Data Source & its Types
• Live and Extracts Connections

The Tableau workspace has features such as shelves, cards, the Data Pane, Analytics Pane, toolbars, and menus. Each of these features has a specific function that learners must understand how to use when working with Tableau.

This week will cover

• Basics of Data Pane
• Quick Visualisations
• Marks and its Properties
• Pages Shelf
• Menu and Toolbar
• Analytics Pane

As not all types of data are the same, Tableau supports 7 different data types, which can be sorted and grouped according to requirements. In order to present and organize data visually, learners should understand the features of Tableau related to data types and sorting.

This week will cover

• Sheet Interface
• Data Types
• Dimensions and Measures
• Sorting & Custom Sort and Grouping
• Sets & Combines Sets and Hierarchies
• Highlight Data
• Split & Custom Split

There are six different filters available for users on Tableau, each with their own unique function. By using filters, datasets can be condensed to illustrate concise information, thus improving the efficiency of dashboards. Data in a table can also be analyzed through performing table calculations. These features are essential for learners, as they can be used to develop efficient and concise dashboards.

This week will cover

• Filters, Filter Types, and Order Process
• Highlighted Data
• Context Filter and its Uses
• Calculated Fields
• Aggregations and their Uses
• Table Calculations
• Level of Detail Expression (LODs)
• Bins and its Uses

Spreadsheets with data can be transformed into visually interactive dashboards that present the information. Dashboards can be combined with spreadsheets to tell a story and narrate performance through measured parameters. By learning the best practices, learners can design and develop effective dashboards and stories for businesses.

This week will cover

• Parameters
• Action Filters
• Dashboard Layout
• Sizing and Object
• Formatting
• Stories
• Best Practices

Tableau workbooks can be shared, exported, and published as required. As many individuals may work on a dashboard together, it is important for learners to understand how distribution and publishing work.

This week will cover

• Distribution
• Exporting
• Workbook File Types
• Publishing
• Replacing Data Sources

Tableau is also used to perform data blending and data joining. Through joining, data in tables and sheets from the same source can be combined. Data blending lets users combine data from different sources. It is important for learners to understand joins and blending as both are often required in the industry.

This week will cover

• Joins
• Inner and Outer Join
• Left and Right Join
• Blending
• Union

When analyzing data on Tableau, Relationships let you combine data from different tables. It is important for learners to understand how to work with multiple sets of data for effective analysis and visualization.

This week will cover

• Relationships
• Data Models
• Relationships vs Joins
• Creating and Optimizing a Relationship
• Types of Relationships
• Smart Aggregations and their Types

Before data is used in business intelligence, it goes through data preparation. This involves collecting, combining, and systematically organizing the dataset before it gets processed. It is important for learners to understand the parameters considered when preparing data.

This week will cover

• Pivot
• Custom Split
• Hide & Unhide Fields
• Sampling the Data
• Interactivity
• Parameter Actions
• Set Actions

When working with large sets of data, Tableau lets users perform advanced calculations and analytics to draw accurate inferences. It is important for learners to understand how to perform advanced calculations using Tableau to meet industry standards.

This week will cover

• Advanced Calculations
• Regular Expressions
• Table Calculations
• Use Cases
• Trend Lines
• Clustering and Forecasting

Nested LOD is an advanced topic in Tableau that lets users assess data sets in-depth. Users can also build maps using Tableau, and understand the related data. As certain data sets may require mapping functions, and analysis in-depth, it is important to know about these concepts.

This week will cover

• Nested LOD and Use Cases
• Geo Mapping
• Geospatial Data and Use Cases
• Standard Maps
• Map Hierarchies
• Map Layers
• Background Maps
• Common Issues

Through dynamic designs, values in dashboards can automatically change when related data is modified. As it is common for data sets to have changing values, knowing how to work with dynamic designs is an essential skill for learners.

This week will cover

• Dynamic Designs
• Ways to Create
• Interactivity
• Extensions
• Tooltip in Visualizations
• Performance Recording
• Design Principles