Python With Data Science

Introduction to Languages

• What is Language?
• Types of languages
• Introduction to Translators
• Compiler
• Interpreter
• What is Scripting Language?
• Types of Script
• Programming Languages v/s Scripting Languages
• Difference between Scripting and Programming languages
• What is programming paradigm?
• Procedural programming paradigm
• Object Oriented Programming paradigm

• WHY PYTHON?
• History
• Features – Dynamic, Interpreted, Object oriented, Embeddable, Extensible, Large standard libraries, Free and Open source
• Why Python is General Language?
• Limitations of Python
• What is PSF?
• Python implementations
• Python applications
• Python versions
• PYTHON IN REALTIME INDUSTRY
• Difference between Python 2.x and 3.x
• Difference between Python 3.7 and 3.8
• Software Development Architectures

Python Software’s

• Python Distributions
• Download &Python Installation Process in Windows, Unix, Linux and Mac
• Online Python IDLE
• Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc
• Python Language Fundamentals

Operators

• Arithmetic Operators
• Comparison Operators
• Python Assignment Operators
• Logical Operators
• Bitwise Operators
• Shift operators
• Membership Operators
• Identity Operators
• Ternary Operator
• Operator precedence
• Difference between “is” vs “==”
• Input & Output Operators
• Print
• Input
• Command-line arguments

Control Statements

• Conditional control statements
• If
• If-else
• If-elif-else
• Nested-if
• Loop control statements
• for
• while
• Nested loops
• Branching statements
• Break
• Continue
• Pass
• Return
• Case studies

Data Structures or Collections

• Introduction
• Importance of Data structures
• Applications of Data structures
• Types of Collections
• Sequence
• Strings, List, Tuple, range
• Non sequence
• Set, Frozen set, Dictionary
• Strings
• What is string
• Representation of Strings
• Processing elements using indexing
• Processing elements using Iterators
• Manipulation of String using Indexing and Slicing
• String operators
• Methods of String object
• String Formatting
• String functions
• String Immutability
• Case studies

List Collection

• What is List
• Need of List collection
• Different ways of creating List
• List comprehension
• List indices
• Processing elements of List through Indexing and Slicing
• List object methods
• List is Mutable
• Mutable and Immutable elements of List
• Nested Lists
• List_of_lists
• Hardcopy, shallowCopy and DeepCopy
• zip() in Python
• How to unzip?
• Python Arrays:
• Case studies

Tuple Collection

• What is tuple?
• Different ways of creating Tuple
• Method of Tuple object
• Tuple is Immutable
• Mutable and Immutable elements of Tuple
• Process tuple through Indexing and Slicing
• List v/s Tuple
• Case studies

Set Collection

• What is set?
• Different ways of creating set
• Difference between list and set
• Iteration Over Sets
• Accessing elements of set
• Python Set Methods
• Python Set Operations
• Union of sets
• functions and methods of set
• Python Frozen set
• Difference between set and frozenset ?
• Case study

Dictionary Collection

• What is dictionary?
• Difference between list, set and dictionary
• How to create a dictionary?
• PYTHON HASHING?
• Accessing values of dictionary
• Python Dictionary Methods
• Copying dictionary
• Updating Dictionary
• Reading keys from Dictionary
• Reading values from Dictionary
• Reading items from Dictionary
• Delete Keys from the dictionary
• Sorting the Dictionary
• Python Dictionary Functions and methods
• Dictionary comprehension

Functions

• What is Function?
• Advantages of functions
• Syntax and Writing function
• Calling or Invoking function
• Classification of Functions
• No arguments and No return values
• With arguments and No return values
• With arguments and With return values
• No arguments and With return values
• Recursion
• Python argument type functions :
• Default argument functions
• Required(Positional) arguments function
• Keyword arguments function
• Variable arguments functions
• pass’ keyword in functions
• Lambda functions/Anonymous functions
• map()
• filter()
• reduce()
• Nested functions
• Non local variables, global variables
• Closures
• Decorators
• Generators
• Iterators
• Monkey patching

Module: 1

• Importance of modular programming
• What is module
• Types of Modules – Pre defined, User defined.
• User defined modules creation
• Functions based modules
• Class based modules
• Connecting modules
• Import module
• From … import
• Module alias / Renaming module
• Built In properties of module

• Organizing python project into packages
• Types of packages – pre defined, user defined.
• Package v/s Folder
• py file
• Importing package
• PIP
• Introduction to PIP
• Installing PIP
• Installing Python packages
• Un installing Python packages

• Procedural v/s Object oriented programming
• Principles of OOP – Encapsulation , Abstraction (Data Hiding)
• Classes and Objects
• How to define class in python
• Types of variables – instance variables, class variables.
• Types of methods – instance methods, class method, static method
• Object initialization
• ‘self’ reference variable
• ‘cls’ reference variable
• Access modifiers – private(__) , protected(_), public
• AT property class
• Property() object
• Creating object properties using setaltr, getaltr functions
• Encapsulation(Data Binding)
• What is polymorphism?

Module: 4 Overriding

• Method overriding
• Constructor overriding

•   Method Overloading
•   Constructor Overloading

• Class re-usability
• Composition
• Aggregation
• Inheritance – single , multi level, multiple, hierarchical and hybrid inheritance and Diamond inheritance
• Constructors in inheritance
• Object class
• super()
• Runtime polymorphism
• Method overriding
• Method resolution order(MRO)
• Method overriding in Multiple inheritance and Hybrid Inheritance
• Duck typing
• Concrete Methods in Abstract Base Classes
• Difference between Abstraction & Encapsulation
• Inner classes
• Introduction
• Writing inner class
• Accessing class level members of inner class
• Accessing object level members of inner class
• Local inner classes
• Complex inner classes
• Case studies

Regular expressions

• Understanding regular expressions
• String v/s Regular expression string
• “re” module functions
• Match()
• Search()
• Split()
• Findall()
• Compile()
• Sub()
• Subn()
• Expressions using operators and symbols
• Simple character matches
• Special characters
• Character classes
• Mobile number extraction
• Mail extraction
• Different Mail ID patterns
• Data extraction
• Password extraction
• URL extraction
• Vehicle number extraction
• Case study

File &Directory handling

• Introduction to files
• Opening file
• File modes
• Reading data from file
• Writing data into file
• Appending data into file
• Line count in File
• CSV module
• Creating CSV file
• Reading from CSV file
• Writing into CSV file
• Object serialization – pickle module
• XML parsing
• JSON parsing

Date & Time module

• How to use Date & Date Time class
• How to use Time Delta object
• Formatting Date and Time
• Calendar module
• Text calendar
• HTML calendar

OS module

• Shell script commands
• Various OS operations in Python
• Python file system shell methods
• Creating files and directories
• Removing files and directories
• Shutdown and Restart system
• Renaming files and directories
• Executing system commands

Multi-threading & Multi Processing

• Introduction
• Multi-tasking v/s Multi-threading
• Threading module
• Creating thread – inheriting Thread class , Using callable object
• Life cycle of thread
• Single threaded application
• Multi-threaded application
• Can we call run() directly?
• Need to start() method
• Sleep()
• Join()
• Synchronization – Lock class – acquire(), release() functions
• Case studies

Garbage collection

• Introduction
• Importance of Manual garbage collection
• Self-reference objects garbage collection
• ‘gc’ module
• Collect() method
• Threshold function
• Case studies

Python Data Base Communications (PDBC)

• Introduction to DBMS applications
• File system v/s DBMS
• Communicating with MySQL
• Python – MySQL connector
• connector module
• connect() method
• Oracle Database
• Install cx_Oracle
• Cursor Object methods
• execute() method
• executeMany() method
• fetchone()
• fetchmany()
• fetchall()
• Static queries v/s Dynamic queries
• Transaction management
• Case studies

Python – Network Programming

• What is Sockets?
• What is Socket Programming?
• The socket Module
• Server Socket Methods
• Connecting to a server
• A simple server-client program
• Server
• Client

Tkinter & Turtle

• Introduction to GUI programming
• Tkinter module
• Tk class
• Components / Widgets
• Label , Entry , Button , Combo, Radio
• Types of Layouts
• Handling events
• Widgets properties
• Case studies

Module 1: Introduction to SQL

• Duration: 1 Month

1. Overview of Databases

   • Understanding databases and their importance
   • Types of databases: Relational vs. Non-relational

2. Introduction to SQL

   • What is SQL?
   • History and evolution of SQL
   • SQL syntax and conventions

3. Basic SQL Commands

   • SELECT, INSERT, UPDATE, DELETE
   • Understanding data types

4. Database Schema

   • Tables, rows, and columns
   • Primary keys and foreign keys
   • Creating and modifying database schemas

5. Hands-on Practice

   • Setting up a local SQL environment
   • Basic query exercises

Module 2: Intermediate SQL

• Duration: 2 Months

1. Advanced Querying Techniques

   • WHERE clause and filtering data
   • Using aliases for tables and columns
   • Sorting results with ORDER BY

2. Joins and Subqueries

   • INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
   • Subqueries and nested queries
   • UNION, INTERSECT, and EXCEPT

3. Aggregate Functions and Grouping

   • COUNT, SUM, AVG, MIN, MAX
   • GROUP BY and HAVING clauses

4. Data Manipulation

   • Bulk inserts and updates
   • Transactions and rollbacks
   • Indexing for performance optimization

5. Hands-on Practice

   • Complex query exercises
   • Real-world database scenarios

Module 3: Advanced SQL

• Duration: 2 Months

1. Stored Procedures and Functions

   • Creating and using stored procedures
   • User-defined functions

2. Triggers and Views

   • Creating and managing triggers
   • Using views for simplified querying

3. Performance Tuning and Optimization

   • Query optimization techniques
   • Indexing strategies
   • Analyzing query performance with EXPLAIN

4. Database Security

   • User roles and permissions
   • Encryption and data protection
   • Best practices for securing databases

5. Data Warehousing and Analytics

   • Introduction to data warehousing
   • Using SQL for data analysis and reporting

6. Hands-on Practice

   • Creating and using stored procedures and triggers
   • Performance tuning exercises
   • Data analysis projects

Module 4: SQL for Specific Databases

• Duration: 1 Month

1. SQL in Different Database Management Systems

   • Differences in SQL syntax across MySQL, PostgreSQL, SQL Server, Oracle

2. Advanced Features in Specific DBMS

   • MySQL: Advanced indexing, full-text search
   • PostgreSQL: Window functions, JSON support
   • SQL Server: Advanced security features, SQL CLR
   • Oracle: PL/SQL, advanced transaction control

3. Database Migration and Integration

   • Techniques for migrating databases
   • Integrating SQL with other programming languages and tools

4. Hands-on Practice

   • Working with different DBMS
   • Database migration projects
   • Integration exercises

Data Science with Python

• Course Introduction
• Introduction to Data Science
• Essentials of Python Programming
• NumPy
• Linear Algebra
• Statistics Fundamentals
• Probability Distribution
• Advanced Statistics
• Pandas
• Data Analysis
• Data Wrangling
• Data Visualization

Matplotlib

Seaborn