Duration: 11 days – 77 hrs.
Overview
This intensive course is designed to take participants from Python beginners to Python experts. The course covers essential Python programming concepts, libraries (NumPy, Pandas, Matplotlib), and data manipulation techniques. Participants will learn how to create, analyze, and visualize data using Python. Each module consists of morning and afternoon sessions, with hands-on exercises and practical examples to reinforce learning.
Objectives
- Fundamental Python Skills: Gain a strong foundation in Python programming, covering variables, data types, and basic syntax.
- Control Structures: Learn how to use loops and conditional statements to control program flow.
- Functions and Modules: Understand how to define, call, and organize functions, as well as import and use modules for code modularity.
- Data Structures: Explore lists, tuples, dictionaries, and sets to manage and manipulate data efficiently.
- File Handling: Master file input and output operations, allowing you to work with external data files.
- Error Handling: Learn to handle exceptions and errors gracefully in your Python programs.
- Basic Algorithm Design: Develop problem-solving skills and learn to implement simple algorithms using Python.
- Hands-On Projects: Apply your knowledge through practical exercises and projects to reinforce learning.
Audience
- Absolute Beginners: Individuals with little to no prior programming experience who want to learn Python as their first programming language.
- Career Changers: Professionals from diverse backgrounds who are looking to transition into the field of programming and need a strong foundation in Python.
- Students: High school or college students who want to supplement their academic studies with practical programming skills.
- Data Enthusiasts: Those interested in data analysis, data science, or data visualization who require Python as a prerequisite skill.
- Professionals in Non-Technical Roles: Individuals working in roles such as marketing, finance, or project management who want to acquire Python skills to enhance their job performance.
- Self-Learners: Anyone with a keen interest in coding and self-improvement, irrespective of their current occupation.
- Entrepreneurs and Small Business Owners: Those looking to automate tasks, analyze data, or create prototypes for their businesses.
Pre- requisites
- Basic Computer Literacy: Participants should have a comfortable working knowledge of using a computer, including basic file operations and software installation.
- No Prior Coding Experience Required: This course is designed for beginners, so no prior programming experience is necessary. However, a willingness to learn and engage with the material is essential.
- Access to a Computer: Access to a computer or laptop with a reliable internet connection is necessary to complete course exercises and assignments.
- Software Installation: Participants should be able to install the required software (Python and a code editor) as per the provided instructions.
- Desire to Learn: Enthusiasm and a genuine interest in learning Python programming are highly recommended, as this course will require active participation and practice.
Course Content
Module 1: Basic Python
Topic 1-2: Module 1 – Basic Python Morning Session:
- Introduction to the Course
- Installation, Configuration, and Setup (Anaconda, Python, Jupyter Notebook)
- Overview of Essential Libraries (NumPy, Pandas, Matplotlib)
Afternoon Session:
- Introduction to Programming and Python
- Reserved Words
- Basic Data Types
- Variables, Constants, and Literals
- Operators
- Operator Precedence
- Comments
Topic 3-4: Module 1 – Basic Python Morning Session:
- Functions
- Basic Mathematical Functions
- User-Defined/Custom Functions
Afternoon Session:
- Code Blocks and Coding Practices
- Conditional Statements (If, Elif, Else)
- Topic 5-6: Module 1 – Basic Python Morning Session:
- Loops (While, For, Infinite Loops)
Afternoon Session:
- Classes
- Object-Oriented Programming (OOP) Concepts
- String Methods
Topic 7-8: Module 1 – Basic Python Morning Session:
- Modules
- Date Module
Afternoon Session:
- Errors and Exceptions
- Built-in Data Structures in Python (List, Tuple)
Topic 9-10: Module 1 – Basic Python Morning Session:
- Built-in Data Structures in Python (Dictionaries)
Afternoon Session:
- Built-in Data Structures in Python (Set)
- Module 2: NumPy
Topic 11-12: Module 2 – NumPy Morning Session:
- Built-in Data Structures in Python (Recap)
- Ndarray
Afternoon Session:
- Manipulating Data Structures in Python (Indexing, Slicing)
- Module 3: Pandas
Topic 13-14: Module 3 – Pandas Morning Session:
- Built-in Data Structures in Python (Recap)
- Series
- Dataframes
Afternoon Session:
- Manipulations (Part 1) – Indexing, Merging, Groupby
Topic 15-16: Module 3 – Pandas Morning Session:
- Manipulations (Part 2) – Applying Functions on Dataframes, Extracting Rows and Columns, Working with Date and Time
Afternoon Session:
- Manipulations (Part 3) – Mapping Dictionaries on Dataframe, Dropna, Fillna, Reset_index
Topic 17-18: Module 3 – Pandas Morning Session:
- Manipulations (Part 4) – Apply Lambda, Sorting, Counting, Renaming Rows and Columns
Afternoon Session:
- Working with External Data Files
- Module 4: Matplotlib
Topic 19-20: Module 4 – Matplotlib Morning Session:
- Introduction to Matplotlib
- Creating Basic Plots (Bar Charts, Line Charts, Time Series)
Afternoon Session:
- Creating Advanced Plots (Area Charts, Pie Charts, Boxplots)
- Applying Matplotlib Functions Directly from Dataframe
