Python Programming Fundamentals with Data Science and AI-ML Unleashed

• Gain a comprehensive understanding of Python, including its history, features, and applications in diverse industries.
• Develop proficiency in Python syntax, data types, and control flow structures for effective programming.
• Master memory management techniques and optimization strategies to enhance code performance.
• Learn to work with various data structures such as lists, tuples, sets, and dictionaries, and manipulate them efficiently.
• Explore advanced concepts in Python programming, including functions, iterators, generators, and decorators.
• Understand module, package, and library management to organize and reuse code effectively.
• Acquire skills in file handling, including reading, writing, and manipulation of data, as well as working with date and time modules.
• Compare Python with other programming languages to understand its strengths and weaknesses.
• Familiarize yourself with common Python libraries and their applications in areas like mathematics, file handling, and data manipulation.
• Develop practical coding skills through hands-on exercises and projects to solve real-world problems.
• Classifying Python, covering syntax, data structures, and basic programming concepts.
• Explaining GUI development using Tkinter and PyQt5, mastering the creation of interactive graphical interfaces.
• Classify and demonstrate effective exception handling strategies, including customized handling and logging techniques.
• Assemble skills in working with JSON data, exploring serialization, and utilizing pickling for data manipulation.
• Examine the concepts of multithreading, multitasking, and synchronization, distinguishing between multiprocessing and multithreading.
• identify Python’s Collections module for efficient data manipulation, employing various collection types in practical scenarios.
• Summarize OOP principles, including inheritance, polymorphism, encapsulation, and abstraction, for effective code organization.
• Operate learned concepts through mini projects, including a calculator, password generator, and others, enhancing practical programming skills.
• Explain SQL basics, advanced concepts like subqueries and joins, and integrate Python with MySQL for effective database interaction.
• Examine a comprehensive understanding of GUI development with Tkinter and PyQt5, creating practical projects like a text editor and calculator.
• Examining of data science principles and applications.
• Operate different tools like Anaconda, Jupyter Notebooks, and PyPI for data science.
• Summerize mathematical concepts, including vectors, matrices, probability, and statistics, to data science tasks.
• Operate efficient numerical operations and data manipulation using Numpy in Python.
• Identify Pandas for effective data manipulation, analysis, and exploration.
• Summarizing aspects of data preprocessing, including handling null values, reshaping data, and conditional selection.
• Assemble meaningful visualizations using Matplotlib for exploratory data analysis.
• Examine real-world case studies and applications in data science, analysing scenarios in companies like J.P. Morgan and Netflix.
• Identify the fundamentals of artificial intelligence, its history, development, and applications.
• Recognize foundational concepts in machine learning, including supervised and unsupervised learning.

• By the end of the course, learners will be able to:
• Demonstrate a thorough understanding of Python, including its history, features, and diverse applications across various industries.
• Achieve proficiency in Python syntax, data types, and control flow structures, enabling the writing of efficient and effective Python code.
• Implement memory management techniques and optimization strategies to improve code performance and resource utilization.
• Work with various data structures such as lists, tuples, sets, and dictionaries, effectively manipulating them to solve programming problems.
• Master advanced concepts in Python programming, including functions, iterators, generators, and decorators, to write sophisticated and efficient code.
• Gain proficiency in module, package, and library management, enabling efficient organization and reuse of code in Python projects.
• Possess skills for effective file handling, including reading, writing, and manipulation of data, as well as working with date and time modules.
• Compare Python with other programming languages, understanding its strengths and weaknesses in relation to others.
• Become familiar with common Python libraries and their applications in areas such as mathematics, file handling, and data manipulation, enhancing the ability to leverage existing resources.
• Develop practical coding skills through hands-on exercises and projects, applying knowledge to solve real-world problems effectively.
• Discover proficiency in Python, enabling the writing of well-structured and efficient code.
• Make use of GUI development using Tkinter and PyQt5, creating user-friendly and interactive applications.
• Build effective exception handling, logging, and debugging strategies, ensuring robust and error-free code.
• Utilize JSON data, utilizing pickling for serialization, and manipulating data effectively.
• Utilize the concepts of multithreading, multitasking, and synchronization, demonstrating expertise in concurrent programming.
• Build Python’s Collections module to manipulate data efficiently, showcasing skills in handling diverse data structures.
• Make use of OOP principles effectively, organizing code using inheritance, polymorphism, encapsulation, and abstraction.
• Construct mini projects, demonstrating the application of learned concepts in real-world scenarios.
• Develop proficiency in SQL basics, advanced concepts, and Python’s integration with MySQL for effective database interaction.
• Discover advanced skills in GUI development with Tkinter and PyQt5, showcasing the ability to create complex and functional applications.
• Build proficiency in data science concepts, tools, and applications.
• Effectively utilize tools like Anaconda, Jupyter Notebooks, and PyPI in real-world data science projects.
• Make use of mathematical concepts in practical data science scenarios, enhancing analytical skills.
• Construct efficient numerical operations and data manipulation tasks using NumPy.
• Examine and Manipulate data effectively using Pandas for insightful decision-making.
• Build data preprocessing techniques to handle null values, reshape data, and perform conditional selections.
• Produce clear and meaningful visualizations using Matplotlib for effective exploratory data analysis.
• Compare real-world case studies and apply data science techniques to address complex business challenges.
• Distinguish the Foundation of artificial intelligence and its ethical implications in societal contexts.
• Develop a solid understanding of machine learning fundamentals, preparing for advanced applications and scenarios.

The academic approach of the course focuses on ‘work-centric’ education. With this hands-on approach, derive knowledge from and while working to make it more wholesome, delightful and useful. The ultimate objective is to empower learners to also engage in socially useful and productive work. It aims at bringing learners closer to their rewarding careers as well as to the development of the community.

• Step 1: Learners are given an overview of the course and its connection to life and work
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!