Python for Data Science: From Zero to Data Analysis

Python Foundations for Data Science"

This course is your ticket to becoming a master in data analysis using Python. Even if you are a fresher, we make sure your journey is sound in the basics of Python and bring you up to advanced levels gradually, slowly towards the depth of data science.

I would like to particularly clarify here that no background in programming and, indeed no prior knowledge of Python in particular is needed for this session.

Introduction to Python: Understand data types, strings, slicing, f-strings, and other basic aspects of Python, which is an important aspect of data manipulation.

• Control and Conditional Statements: Understand how to use if-else statements for decision-making in Python using logical operators.
• Loops: Automate repetitive tasks by using for and while loops, thus making your code much more efficient.
• Capstone Project - Turtle Graphics: Implement your foundational learning in a fun, creative project with Python's turtle graphics.
• Functions: Create reusable code using functions, including arguments, return values, and scope.
• Lists: Work with collections of data using Python lists, including list comprehension
• Equality vs. Identity: Really learn how Python works with data by covering shallow vs. deep copy, type vs. isinstance, etc.
• Error-Handling: Master exception handling and error management to write robust code.
• Recursive Programming: Solve complex problems elegantly with recursion and learn how it differs from iteration.
• Searching and Sorting Algorithms: Learn basic algorithms to speed up the processing of data.
• Advanced Data Structures: Learn about data structures beyond lists, such as dictionaries, sets, and tuples, that are important for efficient data management.
• Object-Oriented Programming: Create scalable and maintainable code with classes, inheritance, polymorphism, and more, including an in-depth look at dunder methods.
• Unit Testing with pytest: Ensure your code's reliability with automated tests using pytest, a critical skill for any developer.
• Files and Modules: Handling input and output files, and structuring your code, will be done using modules.
• NumPy: Learning to use basic numerical computing, NumPy is the backbone of any data scientist.
• Pandas: Data Manipulation and Analysis using pandas - must know of the world of data science
• Matplotlib-Graphing and Statistics: Generating plots, and performing statistical analysis of data with Matplotlib
• Matplotlib - Image Processing: Basic techniques of image processing with Matplotlib.
• Seaborn: Learn how to build informative and attractive statistical graphics using Seaborn.
• Plotly: Learn interactive data visualization with Plotly. Interactive plots engage users in exploring the data.

Why Take This Course?

• Learn from step-by-step tutorials and clear explanations from an expert.
• Get prompt, helpful feedback from the instructor. Questions get addressed quickly in the course Q&A.
• Study at your own pace. Lifetime access to regularly updated course materials.
• Positive Learning Environment: Join a supportive and inspiring learning environment in which learners and trainers collaborate to promote problem-solving and knowledge sharing.

Who this course is for?

• Python Beginners: Ideal for first-time programmers interested in using Python to learn data science.
• Data Analysis Newcomers: Suitable for a beginner with minimal or no background in data analysis.
• Designed to be of use to a wide variety of audiences in the form of aspiring Data Scientists who wish to look and make the transition into such a field, and more importantly arm them with sufficient skills and knowledge that lead to success, in an industry professional in most parts of the industry who have a desire to utilize python for data-driven decision-making, to students and also academics in general for academic projects, research, studies, etc.