Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
23 Hour(s) 26 Minute(s)
Language
English
Taught by
Dr. Ron Erez
Rating
Course Overview
Master Python for Data Manipulation, Visualization, and Introductory Machine Learning
Welcome to "Python Foundations for Data Science"!
This course is your gateway to mastering Python for data analysis, whether you’re just getting started or looking to expand your skills. We begin with the basics, ensuring you build a solid foundation, then gradually move into data science applications.
I'd like to stress that we do not assume a programming background and no background in Python is required.
What You'll Learn:
Python Foundations: Grasp the essentials of Python, including data types, strings, slicing, f-strings, and more, laying a solid base for data manipulation.
Control and Conditional Statements: Master decision-making in Python using if-else statements and logical operators.
Loops: Automate repetitive tasks with for and while loops, enhancing your coding efficiency.
Capstone Project - Turtle Graphics: Apply your foundational knowledge in a fun, creative project using Python’s turtle graphics.
Functions: Build reusable code with functions, understanding arguments, return values, and scope.
Lists: Manage and manipulate collections of data with Python lists, including list comprehension.
Equality vs. Identity: Dive deep into how Python handles data with topics like shallow vs. deep copy, and understanding type vs. isinstance.
Error-Handling: Write robust code by mastering exception handling and error management.
Recursive Programming: Solve complex problems elegantly with recursion and understand how it contrasts with iteration.
Searching and Sorting Algorithms: Learn fundamental algorithms to optimize data processing.
Advanced Data Structures: Explore data structures beyond lists, such as dictionaries, sets, and tuples, crucial for efficient data management.
Object-Oriented Programming: Build 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: Handle file input/output and organize your code effectively with modules.
NumPy: Dive into numerical computing with NumPy, the backbone of data science in Python.
Pandas: Master data manipulation and analysis with pandas, a must-know tool for data science.
Matplotlib - Graphing and Statistics: Visualize data and perform statistical analysis using Matplotlib.
Matplotlib - Image Processing: Explore basic image processing techniques using Matplotlib.
Seaborn: Enhance your data visualization skills with Seaborn, creating more informative and attractive statistical graphics.
Plotly: Learn interactive data visualization with Plotly, producing interactive plots that engage users.
PyTorch Fundamentals: Get started with deep learning using PyTorch, understanding tensors and neural networks.
Why Enroll?
Expert Guidance: Benefit from step-by-step tutorials and clear explanations.
Responsive Support: Get prompt, helpful feedback from the instructor, with questions quickly addressed in the course Q&A.
Flexible Learning: Study at your own pace with lifetime access to regularly updated course materials.
Positive Learning Environment: Join a supportive and encouraging space where students and instructors collaboratively discuss and solve problems.
Who This Course is For:
Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.
Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.
Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.
Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.
Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.
Enroll now and start your journey to mastering Python for data science and data analysis!
Course Content
- 10 section(s)
- 231 lecture(s)
- Section 1 Introduction
- Section 2 Foundations
- Section 3 Control Flow and Conditional Statements
- Section 4 Loops
- Section 5 Capstone Project using Turtle Graphics
- Section 6 Functions
- Section 7 Lists
- Section 8 Exercises - Functions, Loops, Lists
- Section 9 Equality vs Identity
- Section 10 Exception and Error Handling
What You’ll Learn
- Foundational Python Programming: Acquire a strong grasp of Python basics, including data types, control structures, functions, and object-oriented programming.
- Data Analysis and Manipulation: Master the use of Python libraries like NumPy and pandas to clean, manipulate, and analyze datasets.
- Advanced Data Visualization: Learn to create visualizations using Matplotlib and Plotly to effectively communicate data-driven insights and trends.
- Gain hands-on experience with PyTorch to build and evaluate machine learning models, including classification and regression tasks.
- Develop robust and reliable code using error handling techniques and performing unit testing with pytest, ensuring your data analysis scripts run smoothly
- As a bonus, explore Python fundamentals while having fun with turtle graphics, making the course accessible for both parents and children learning together
Skills covered in this course
Reviews
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JJulia Ki
I mean overall it's ok if not a bit drawn out in some parts. Sorry, but why complicate the foundational understanding of an already complex topic such as programming with unnecessarily complex maths examples? We don't all have PhD's in mathematics and it is assumed by the course creator that complex math examples is going to make each and every single topic in Python magically more understandable. Why are we doing square roots of anything when trying to understand what a command does when we can use our human language or simple numbers, maybe 4 most basic mathematical operations to make the concept easy to grasp first and foremost? Now it's not just the various commands in python that need to be understood but also why we got the result that we did in the output which makes learning a lot more difficult and time consuming. Also what is up with the quiet sound? Even the AC needs to be turned off in order to hear 90% of what is being said...
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VVaseekaran R
Needs to increase audio volume/sound in video lectures. the staff has soft spoken voice(which is soothing and good, but need to increase the volums.) Getting more information and learning useful things and gimmicks. Solid course structure as far as I know. other thing which I was taken aback was, when the video is about one topic, he includes 2 or 3 other topics and those other topics have their own video in upcoming sections. eg: when teaching indentation and blocks, for loops and if statements are coming as example. I know about those, so it was not confusing for me. But what about those who have not learned /familiar with those topics? they might get overloaded with unfamiliar concepts. Down the line, I might get overwhelmed if I come across topics I have not learned as examples for a new topic/concept I'm learning.
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DDebargho Banerji
course was good but i kind of felt a little lost at a fw places because the course was very lengthy but a very very informative innovative and creative course and the course instructor did an amazing job too
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RRam Sai Saka
yes, really good
