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Python for Data Science, AI and Development Coursera

Highlights
Tuition fee
Free
Free
Unknown
Tuition fee
Free
Free
Unknown
Duration
2 days
Duration
2 days
Apply date
Anytime
Unknown
Apply date
Anytime
Unknown
Start date
Anytime
Unknown
Start date
Anytime
Unknown
Taught in
English
Taught in
English

About

This Python for Data Science, AI and Development course offered by Coursera in partnership with IBM will develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.

Overview

This Python for Data Science, AI and Development course offered by Coursera in partnership with IBM will demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks.

Key facts

  • 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 programmes, 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.

Programme Structure

Courses include:

  • Python Basics
  • Python Data Structures
  • Python Programming 
  • Working with Data in Python
  • APIs, and Data Collection

Key information

Duration

  • Part-time
    • 2 days

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online
  • Self-paced

Campus Location

  • Mountain View, United States

What students do after studying

Join for free or log in to access our complete career info list.

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

  • Beginner Level
  • No prior programming experience required!
  • To obtain additional information about the programme, we kindly suggest that you visit the programme website, where you can find further details and relevant resources.  

Tuition Fees

Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
  • International

    Non-residents
    Free
  • Out-of-State
    Free

Additional Details

  • Audit: free access to course materials except graded items
  • Certificate: a trusted way to showcase your skills
  • A year of unlimited access with Coursera Plus $399

Funding

Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.

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