Python
Python is a versatile and powerful language for data analysis. It is becoming increasingly popular with data scientists, researchers, and businesses who are looking for an easy and efficient way to process and manipulate large datasets. Python offers many advantages over other data analysis platforms, such as:
- Flexibility: Python is an open-source language, meaning it can be adapted and customized to meet the specific needs of a data analysis project. This makes it easier to automate tasks and create custom programs.
- Ease of Use: Python has a simple and intuitive syntax, making it easy to learn and use. It is also highly readable and comprehensible, which makes it easier to debug and maintain code.
- Scalability: Python is highly scalable, meaning it can be used to analyze very large datasets. It can also handle complex tasks and can be used to create powerful data visualizations.
- Extensibility: Python has a wide range of libraries and packages that can be used to extend its capabilities. This allows data scientists to build custom tools and applications with ease.
Python is the perfect choice for data analysis projects, as it offers a range of advantages over other platforms. With its flexibility, scalability, and extensibility, it is easy to see why Python is becoming the language of choice for data scientists and businesses alike.
About Courses
Python is an interpreted, object-oriented, high-level programming language with high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as simple for use as a scripting. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. The Python interpreter and the extensive standard library are available in source
Eligible Criteria
Any Degree Holder (B.Sc/BCA/B.E/B.Tech /M.Sc/MCA/M.E/M.Tech…)
Total Hours: 80 Hrs
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Course Syllabus
Part I: Python Basics
- What can Python do?
- Why Python?
- Good to know
- Python Install and Python Environment Setup
- Different IDEs for Python
- The print statement
- Comments
- Python Data Types
- Simple Input & Output
- Sample Output Formatting
- String Operations in Python
- Data Type Conversion
- Operators in python
- Indentation
- The if loop
- The while loop
- The for loop
- The range statement
- Break, Continue and Pass
- Assert
- Examples for looping
- List
- Tuple
- Set
- Dictionary
- Comprehension of List
- Comprehension of Dictionary
- Introduction to Functions
- Function Documentation
- Different Types of Functions
- Create your own functions
- Function Arguments
- Lambda Function
- Recursive Function
- Create a Module
- System Modules
- Errors
- Exception handling with try
- Handling Multiple Exceptions
- Writing your own Exception
- File handling Modes
- Reading Files
- Writing& Appending to Files
- Handling File Exceptions
- The with statement
Part II: Python Advanced
- Creating Classes
- Instance Methods
- Inheritance
- Polymorphism
- Exception Classes & Custom Exceptions
- Iterators
- Generators
- The Functions any and all
- With Statement
- Data Compression
- namedtuple()
- deque
- ChainMap
- Counter
- OrderedDict
- Defaultdict
- UserDict
- UserList
- UserString
- Split
- Working with special characters, date, emails
- Quantifiers
- Match and find all
- character sequence and substitute
- Search method
- Class and threads
- Multi-threading
- Synchronization
- Threads Life cycle
- Use Cases
- Introduction
- A Daytime Server
- Clients and Servers
- The Client Program
- The Server Program
- Introduction
- Installation
- DB Connection
- Creating DB Table
- INSERT, READ, UPDATE, DELETE operations
- COMMIT & ROLLBACK operation
- Handling Errors
- Sleep
- Program execution time
- More methods on date/time
- Filter
- Map
- Reduce
- Decorators
- Frozen set
- Collections
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