Will the software be provided to learners?

Yes, learners will be provided with software access.

Who can I access in case of doubts?

You can seek aid from the technical support engineers in case of any doubts or queries of the Post Graduate Program in Data Analytics and Data Science Course.

What is the duration of this Post Graduate Program in Data Analytics and Data Science Training course?

The Post Graduate Program in Data Analytics and Data Science Online Course has a total duration of 6 months.

Post Graduate Program in Data Analytics and Data Science by Skill Lync: Fee, Duration, How to Apply

Post Graduate Program in Data Analytics and Data Science

Skill Lync

Learn about the fundamental concepts of data analytics and data science through the Post Graduate Program in Data Analytics and Data Science Course.

Online

24 Weeks

₹ 275000

Inclusive of GST

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study, Virtual Classroom

                                    Mode of Delivery
                                    Video and Text Based

Course 1: Core and Advanced Python Programming

Week 1: Introduction to Python, Python Basics

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

Week 2: Strings, Decision Control Statements

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

Week 3: Repetition Statements and Console Input-Output

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

Week 4: Lists, Tuples, Sets, Dictionary

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

Week 5: Functions and Recursion, Functional Programming and Lambda Functions

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

Week 6: File Input-Output and Modules

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

Week 7: Classes and Objects

Class variables
Methods
Operator overloading
Reuse
Containership
Inheritance

Week 8: Exception Handling, Iterators and Generators

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

Week 9: Data Analysis with Pandas

Installing Pandas
Loading files
CSV files
JSON files
Dataframes

Week 10: Numeric and Scientific Computing using NumPy

NumPy: Introduction
OpenCV
Images and NumPy Arrays

Week 11: Graphical User Interfaces with Tkinter

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

Week 12: Interacting with Databases

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

Course 2: SQL for Data Science

Week 1: Introduction

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

Week 2: Database Creation and Manipulation

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

Week 3: Database Selection

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

Week 4: Database Selection

Generating Strings
String Functions
Date Functions
Conversion Functions

Week 5: Database Selection

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

Week 6: Querying Multiple Tables

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

Week 7: Data Exploration

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

Week 8: Index, View, Transaction

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

Week 9: Querying with Conditions

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

Week 10: Stored Procedures

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

Week 11: Integrating SQL with Excel

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

Week 12: Integrating SQL with Python

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

Course 3: Data Analysis and Visualization with Tableau

Week 1: Introduction to Tableau

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

Week 2: Shelves, Cards, and Analytics Pane

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

Week 3: Data Types, Sorting, Grouping

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

Week 4: Filtering & Table Calculations

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

Week 5: Complete Dashboard Building & Best Practices

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

Week 6: Distributing & Publishing

Distribution
Exporting
Workbook File Types
Publishing
Replacing Data Sources

Week 7: Joins and Blending

Joins
Inner and Outer Join
Left and Right Join
Blending
Union

Week 8: Relationships (2020.2 and Later Versions)

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

Week 9: Data Preparations, Parameter & Set Actions

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

Week 10: Advanced Calculations and Analytics

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

Week 11: Nested LODs and Mapping Functions

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

Week 12: Dynamic Designs, Extensions, and Tooltip Visualizations

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

Course 4: Data Analysis and Visualization using Excel

Week 1: Course Introduction and Fundamentals

Launching Excel and Version Check
Basics of the Interface
Saving an Excel Document
Common Shortcuts
Entering and Editing Data
Relative and Absolute Cell References
Formatting
Customizing the Quick Access Toolbar
Excel Self Help

Week 2: Basic Excel Functions and Modification of Worksheets

Basic Functions
Design of Excel Functions
Understanding SUM(), MIN() & MAX()
Understanding AVERAGE()
Understanding COUNT()
AutoSum and Autofill/Flashfill
Modification
Inserting New Sheets, Rows and Columns
Deleting Sheets, Rows and Columns
Moving or Copying Sheets
Sheet formatting
Excel Options
Customize Ribbon

Week 3: Data Formatting, Working with Shapes & Images, Creating your First Chart

Data Formatting
Cell formatting – Merge, Alignment
Borders
Currencies and Percentages
Format Painter
Conditional Formatting
Find & Search
Shapes & Images
Inserting Shapes & Images
Formatting Excel Shapes
SmartArt
Creating your First Chart
Creating Chart
Chart Options
Types of Charts
Chart formatting
Date Functions
DATE() & DATEVALUE()
NETWORKDAYS()
EOMONTH() & EDATE()

Week 4: Excel Templates, Excel Options and Printing your Excel Worksheets, Introduction to Tables

Excel Templates
Importing Excel Template
Custom Template
Printing Worksheets
Print Preview
Margins and Scaling
Page Layout
Headers and Footers
Specific Range
Tables
Table Options
Table Formatting
Table References
Working with Formulas
Benefits of Tables

Week 5: Conditional Functions and Other Functions

Conditional Functions
Understanding IF()
Understanding SUMIF() & SUMIFS()
Understanding COUNTIF()
Use of AND & OR with IF()
Understanding IFERROR()
Other Functions and Text Functions
Understanding COLUMN() & ROW()
Understanding TEXT()
Understanding LEFT(), RIGHT() & MID()
Understanding LEN(), CONCAT()
Understanding TRIM(), PROPER(), FIND()

Week 6: Pivot Tables and Lookup Functions

Pivot Tables
Data source
Pivot Table Structuring
Pivot Table Options
Pivot Table Formatting
Lookup Functions
Understanding VLOOKUP()
Understanding HLOOKUP()
Understanding INDEX & MATCH
Understanding XLOOKUP()
Understanding OFFSET()

Week 7: New Functions, Data Tab, and Introduction to Power Query

New Functions
Understanding UNIQUE()
Understanding FILTER()
Understanding IFS()
Understanding SORT(), SORTBY()
Understanding SWITCH()
Data Tab
Data Import
Data Cleaning and Transform
What Power Query is
Use of Power Query

Week 8: Advanced Pivot Table Functions and PowerPivot Tools

Advanced Pivot Table Functions
Fields, Items & Sets
Pivot Charts
Slicers and Timeline
Group & Ungroup
GETPIVOTDATA()
PowerPivot Tools
Basics of Excel Power Pivot
More Tables and Relationships
Creating Data Models
KPIs

Week 9: Large Data Sets, File Protection, Named Ranges, and More in Data Tab

Working with Large Data Sets
Freeze Panes
Grouping Data
Consolidating Data from Multiple Worksheets
File Protection
Protecting Specific Cells in a Worksheet
Protecting the Structure of a Workbook
Adding a Workbook Password
Data Tab
Custom Sorting
What-If Analysis
Text to Columns
Data Validation
Understanding Data Validation
Custom Data Validation Error
Dynamic Formulas in Validation

Week 10: List Functions and Excel Automation using Macros

List Functions
Understanding DSUM()
Understanding DSUM() with AND/OR()
Understanding DAVERAGE()
Understanding DCOUNT()
Understanding SUBTOTAL()
Excel Automation using Macros
Understanding Macros
Creating Macro with Macro Recorder
Editing Macros with VBA
Creating Buttonsa

Week 11: VBA Basics

Basics of VBA
VBA Editor
Adding Code to VBA procedure
VBA variables
IF Statements
Loops
Automating Tasks
Input and User Messages
Handling Errors

Week 12: Email Automation and Industry Application

Email Automation
Basics of Emailing Section
Understanding Email Routine
Email loop
Excel Tips and tricks
Industry Standards
How to Apply your Learning

Course 5: Statistics and Probability for Data Sciences

Week 1: Introduction to Machine Learning

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

Week 2: Set Theory

Set Theory
Algebra of Sets
Venn Diagrams

Week 3: Probability

Introduction to Probability
Axioms of Probability
Independent events
Mutually exclusive events
Conditional Probability
Bayes Theorem

Week 4: Statistics

Measures of Central Tendence
Measures of Dispersion
Measures of Symmetry

Week 5: Probability Distribution

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

Week 6: Continuous Probability Distribution

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

Week 7: Inferential Statistics

Sampling
Probabilistic and Nonprobabilistic methods of Sampling Estimation
Estimation
Sample size estimation

Week 8: Hypothesis Testing

Introduction to hypothesis testing
Rejection region
Critical value
p-value

Week 9: Hypothesis Testing

z - test
f - test
t - test
Anova - test

Week 10: Non-Parametric Tests

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

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