Python for Data Science, AI & Development

IBM via Coursera

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Overview

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Kickstart your Python journey with this beginner-friendly, self-paced course taught by an expert. Python is one of the most popular programming languages, and the demand for individuals with Python skills continues to grow. This course takes you from zero to programming in Python in a matter of hours—no prior programming experience is necessary! You’ll begin with Python basics, including data types, expressions, variables, and string operations. You will explore essential data structures such as lists, tuples, dictionaries, and sets, learning how to create, access, and manipulate them. Next, you will delve into logic concepts like conditions and branching, learning how to use loops and functions, along with important programming principles like exception handling and object-oriented programming. As you progress, you will gain practical experience reading from and writing to files and working with common file formats. You’ll also use powerful Python libraries like NumPy and Pandas for data manipulation and analysis. The course also covers APIs and web scraping, teaching you how to interact with REST APIs using libraries like requests and extract data from websites using BeautifulSoup. You will practice and apply what you learn through hands-on labs using Jupyter Notebooks. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and automating real-world tasks using Python. This course is suitable for individuals interested in pursuing careers in Data Science, Data Analytics, Software Development, Data Engineering, AI, and DevOps and a variety of other technology-related roles.

Syllabus

Python Basics

In this module, you will begin by exploring the fundamentals of Python programming. You will identify the users and benefits of Python and understand the diversity and inclusion efforts of the Python community. Next, you will be introduced to the Jupyter Notebook environment, where you will learn how to create, run, and manage code cells, as well as present and shut down notebooks. You will then learn to write your first Python program and work with different data types such as integers, floats, and strings. As you progress, you will use expressions and variables to perform basic operations and practice manipulating strings using indexing, escape sequences, and formatting techniques. Throughout the module, you will apply your learning through hands-on labs and interactive exercises.

Python Data Structures

In this module, you will explore essential Python data structures including lists, tuples, dictionaries, and sets. Starting with lists and tuples, you will learn how to store and manipulate collections of data using indexing, slicing, and sorting techniques. Through hands-on labs, you’ll practice key operations such as cloning lists and performing tuple manipulations. The module then introduces dictionaries, where data is stored in key-value pairs, and you will gain practical experience in creating and working with them. Finally, you will examine sets, an unordered collection that contains only unique elements, and learn how to perform set operations and logic-based tasks. By the end of this module, you’ll have a strong foundational understanding of these core Python data structures.

Python Programming Fundamentals

In this module, you will build a strong foundation in core Python programming concepts essential for applied data science. The module begins with conditions and branching, where you’ll learn to use comparison and logical operators to control the flow of your program. You will then move on to loops, including for and while loops, to iterate over sequences and perform repetitive tasks efficiently. Next, the module covers functions, teaching you how to use built-in Python functions and define your own function to structure and reuse code effectively. You’ll also explore exception handling, a critical concept that enables your program to handle errors gracefully and maintain robustness. Finally, you’ll be introduced to objects and classes, the foundation of object-oriented programming in Python. You’ll learn how to define your own classes and create objects, understand attributes and methods, and see how real-world problems can be modeled using OOP principles.

Working with Data in Python

In this module, you’ll begin by understanding the fundamentals of working with data in Python, focusing on how to read and write data to files in various formats such as text, CSV, and JSON. You’ll learn how to open, read, write, and manipulate files efficiently, which is essential for handling real-world data. As you progress, you’ll explore essential Python libraries for data manipulation and mathematical operations. Key libraries such as Pandas help you work with structured data in tabular formats, whereas NumPy supports numerical operations on arrays and matrices. By the end of the module, you’ll be equipped with the skills to efficiently handle, manipulate, and perform mathematical operations on data using Python, setting a strong foundation for more advanced data analysis techniques.

APIs and Data Collection

This module explores various techniques for collecting data, focusing on the use of APIs, web scraping, and working with different file formats. By the end of this module, you will be equipped with the necessary tools and knowledge to collect data from different sources, both structured and unstructured. The module includes hands-on labs, optional content for further exploration, and a final comprehensive exam to test your overall understanding of the course.

Taught by

Joseph Santarcangelo

Reviews

2.3 rating, based on 3 Class Central reviews

4.6 rating at Coursera based on 43390 ratings

Start your review of Python for Data Science, AI & Development

Ronny De Winter @RonnyDeWinter
367

This is a basic introduction to Python for whoever wants to start with data science and AI. If you are completely new to Python you will probably need more exercise than just this course. For people with Python experience, this can be a good rehearsal if your skills are a bit rusty.

The Coursera certificate is not worth the money in my opinion. If you are happy with an IBM certificate instead you can follow the same course for free at https://cognitiveclass.ai/courses/python-for-data-science including the certificate
Trang Le
8

Sometimes I got freaked out when I completed a course with flying colors and felt like I didn't get anything out of it. Unfortunately, that's exactly my experience with this course, Python for Data Science and AI offered by IBM. If you are consider…

Sometimes I got freaked out when I completed a course with flying colors and felt like I didn't get anything out of it. Unfortunately, that's exactly my experience with this course, Python for Data Science and AI offered by IBM.

If you are considering the course because of its spot-on title, or by the name IBM, then I advise you to stay away from it. There is nothing remotely data science or AI in this course. It should have been called Introduction to Python ,or Python for Beginners, but there are much better courses on this topic, such as those offered by the University of Michigan.

Here is why it is bad: All the videos feature the same robotic voice rushing through basic concepts as if someone is just reading from a textbook. I wonder if the narrator is an AI, not a real human. I could have overlooked that if the content is actually good. In contrast, its scope is very narrow, even for a beginner course. There is no walk through of common Python challenges and mistakes to solidify the concepts. and again, you are going to pick up more Python knowledge from other courses.

Worse, the exercises just promote rote learning and the ability to use IBM's products. There is just not enough practice exercises to help learners understanding. Most of my time spent on this course is actually dedicated to figuring out how to set up IBM Studio, or whatever it is called. I doubt anyone is going to benefit anything from this course.

I've always admired IBM's achievement, but it takes a different skill set to deliver a good online course.

Tldr:Don't take this course if you are a beginner, because there are better, more affordable beginner Python courses elsewhere. Don't take this course if you are intermediate, because your time is precious. Don't take the course at all, unless your future employers are attracted by the IBM brand name.
Khooshi Bhadiyadra @khooshisb
1

"Python for Data Science, AI & Development is an excellent course. It provides a comprehensive and practical introduction to Python's applications in data science and AI. The content is well-structured, and the hands-on exercises are valuable for real-world proficiency. Whether you're a beginner or have some programming experience, this course offers valuable insights and skills. Highly recommended!"