100% practical Basic Python for Data Science
The Basic Python for Data Science course at the Vocational Training Institute of Creative Computer Specialists is designed to equip students with the essential Python programming skills needed to kickstart their journey in data science. This beginner-friendly course introduces students to Python's core concepts, tools, and libraries, focusing on applications in data manipulation, analysis, and visualization. With Python as the foundation, students will develop the technical competence and confidence to advance into more complex areas of data science and analytics. Key Learning Outcomes: By the end of this course, students will: Master Python Basics for Data Science: Understand the Python language's core syntax, data types, control structures, and functions essential for data science applications. Work with Data Structures: Learn to use Python’s built-in data structures (lists, tuples, dictionaries, sets) and understand how to select the right structure for various data-related tasks. Manipulate Data with Pandas: Gain hands-on experience with the Pandas library, a powerful tool for data manipulation, allowing students to load, clean, filter, and transform datasets effectively. Perform Data Analysis with NumPy: Utilize the NumPy library to perform mathematical and statistical operations on large datasets, working with arrays and matrices for faster data computations. Visualize Data with Matplotlib and Seaborn: Learn to create compelling data visualizations, including charts, graphs, and histograms, using Matplotlib and Seaborn to communicate insights. Understand Basic Data Science Workflow: Develop an understanding of the essential steps in a data science project, from data collection and preprocessing to analysis and visualization. Apply Python Skills in Data Science Projects: Put theory into practice through interactive exercises, projects, and assignments that involve real-world datasets, allowing students to solve practical data science problems.
100% practical Basic Python for Data Science
- 1. An Introduction to Python Programming for Data Analysis Basic Python for Data Science
- 2. Introduction This presentation introduces Python as a versatile programming language utilized in data science. It covers essential Python concepts crucial for data analysis and prepares learners for practical application in the field.
- 4. Data types (integers, floats, strings, booleans) Python supports various data types, including integers for whole numbers, floats for decimal values, strings for text data, and booleans representing True or False. Understanding these types is essential for effective data manipulation and programming logic.
- 5. Variables and assignments In Python, variables store data values, allowing for flexible data management. Assigning values to variables involves using the equals sign, enabling programmers to reuse and manipulate data throughout their code. Variable naming conventions and scope are also critical to efficient coding.
- 6. Control structures (if statements, loops) Control structures are fundamental in programming for directing the flow of execution. 'If statements' allow conditional execution based on Boolean expressions, enabling decisions within code. Loops, such as 'for' and 'while', facilitate repetitive actions—crucial for processing collections or iterating through data—enhancing efficiency and readability in programming.
- 8. Lists and list operations Lists in Python are versatile data structures that can hold multiple items in a single variable. They support various operations including appending, removing, and slicing elements. Lists are mutable, meaning they can be altered after creation, making them ideal for dynamic data manipulation. Understanding list operations enhances the ability to work with collections of data effectively.
- 9. Tuples and sets Tuples are similar to lists but are immutable, meaning their content cannot be changed after creation. This property makes tuples useful for fixed collections of items. Sets, on the other hand, are unordered collections of unique elements, combining the functionalities of lists and dictionaries. Mastering tuples and sets is essential for effective data organization and storage in Python.
- 10. Dictionaries and their applications Dictionaries are key-value pairs that enable fast data retrieval and storage. This data structure allows for flexible data organization, making it ideal for scenarios requiring associative mapping. Understanding how to define, manipulate, and utilize dictionaries is crucial for efficient data handling and retrieval in Python applications.
- 11. Conclusions In summary, mastering Python basics including data types, control structures, and data structures is essential for effective data analysis. These foundational concepts equip learners with the necessary skills to tackle real-world data science challenges and enhance their programming proficiency.
- 12. CREDITS: This presentation template was created by Slidesgo, and includes icons, infographics & images by Freepik Do you have any questions? Thank you!