Mastering Data Science with Python [2023]

What you’ll learn
Data Manipulation: Learn how to effectively manipulate and transform data using Python libraries such as Pandas, NumPy, and SciPy.
Data Analysis: Develop the ability to explore and analyze datasets using Python’s powerful data visualization libraries like Matplotlib and Seaborn.
Gain hands-on experience in conducting EDA, including using tools like Pandas Profiling, DABL, and Sweetviz to analyze and visualize datasets.
Capstone Project: Apply the knowledge and skills acquired throughout the course to tackle a real-world business problem, performing end-to-end analysis.

Requirements
Basic Programming Knowledge: A fundamental understanding of programming concepts and logic is necessary. Students should be familiar with variables, data types, control flow statements (if/else, loops), and functions.
Python Fundamentals: Proficiency in Python programming is essential. Students should have a solid understanding of variables, data types, operators, basic data structures (lists, tuples, dictionaries), and control flow.
Mathematics and Statistics Basics: A foundational understanding of mathematics and statistics is important for data analysis and modeling. Concepts such as algebra, calculus, probability, and descriptive statistics will be utilized.

Description
The “Data Science with Python” course is designed to equip you with the essential skills and knowledge to embark on a successful journey in the field of data science. In this course, you will explore the powerful capabilities of Python and its rich ecosystem of libraries to analyze, visualize, and draw meaningful insights from data.Through a hands-on and practical approach, you will gain a deep understanding of key data science concepts and techniques, while mastering the tools and methodologies used by industry professionals. The course begins with an introduction to Python programming, covering fundamental concepts, data structures, control flow, and functions. You will quickly progress to more advanced topics, such as object-oriented programming (OOPs) and working with modules and packages.Once you have built a solid foundation in Python, the course delves into the realm of data science. You will discover how to efficiently manipulate and clean datasets, handle missing values and outliers and prepare data for analysis. By leveraging libraries such as NumPy and Pandas, you will perform exploratory data analysis, extract meaningful insights, and create visualizations that effectively communicate your findings.The course also introduces you to statistical analysis, hypothesis testing, and advanced data visualization techniques using libraries like Matplotlib, Seaborn, and Plotly. You will develop the skills to uncover patterns, identify relationships, and make data-driven decisions.To solidify your understanding and apply the acquired knowledge, the course includes a capstone project. You will tackle a real-world business problem, conducting end-to-end analysis and creating a comprehensive report showcasing your data science skills.Whether you are a beginner or already have some experience with Python, this course will empower you to confidently work with data, explore complex datasets, and extract valuable insights. By the end of the course, you will be equipped with the tools and expertise to embark on data-driven endeavors, making a significant impact in your professional and academic pursuits.Resource Files are added to the First Lecture of every section for the entire section

Who this course is for
This course is designed for individuals who are interested in learning and applying data science techniques using the Python programming language.
Aspiring Data Scientists: Individuals who want to pursue a career in data science and want to gain practical skills in using Python for data analysis, modeling, and visualization.
Python Programmers: Programmers who are already familiar with Python and want to expand their knowledge to the field of data science. This course will help them apply their programming skills to solve real-world data problems.
Data Analysts: Analysts who work with data and want to enhance their skills by incorporating Python into their data analysis workflows. This course will enable them to perform more advanced data manipulation, statistical analysis, and visualization using Python.