Course HighlightsCOURSE
Python for Data Science, AI & Development

Python for Data Science, AI & Development

Python for Data Science, AI & Development Highlights

Course enrollment

Starts on

08 June 2021

Enrollment closes on
31 December 2021

  Course Fee

Fee

US$99 - US$199

Course enrollment

Starts on

08 June 2021

Enrollment closes on
31 December 2021

Course Fee

Fee

US$99 - US$199

About this course

Kickstart your learning of Python for data science, as well as programming in general with this introduction to Python course. This beginner-friendly Python course will quickly take you from zero to programming in Python in a matter of hours and give you a taste of how to start working with data in Python.

Upon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.

You can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you will receive free access to Watson Studio. Start now and take advantage of this platform and learn the basics of programming, machine learning, and data visualization with this introductory course.

What You Will Learn

The objectives of this course is to get you started with Python as the programming language and give you a taste of how to start working with data in Python.

In this course you will learn about:

  • What Python is and why it is useful
  • The application of Python to Data Science
  • How to define variables in Python
  • Sets and conditional statements in Python
  • The purpose of having functions in Python
  • How to operate on files to read and write data in Python
  • How to use pandas, a must have package for anyone attempting data analysis in Python

Course Syllabus

Module 1 - Python Basics

  • Your first program
  • Types
  • Expressions and Variables
  • String Operations

Module 2 - Python Data Structures

  • Lists and Tuples
  • Sets
  • Dictionaries

Module 3 - Python Programming Fundamentals

  • Conditions and Branching
  • Loops
  • Functions
  • Objects and Classes

Module 4 - Working with Data in Python

  • Reading files with open
  • Writing files with open
  • Loading data with Pandas
  • Working with and Saving data with Pandas

Module 5 - Working with Numpy Arrays

  • Numpy 1D Arrays
  • Numpy 2D Arrays
  • Simple APIs

Meet Your Instructor

Course Staff Image #1
Joseph Santarcangelo

PhD., Data Scientist at IBM
Joseph Santarcangelo is currently working as a Data Scientist at IBM. Joseph has a Ph.D. in Electrical Engineering. His research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition.

Course Outline

General Information
Learning Objectives
Syllabus
Grading Scheme
Change Log
Copyrights and Trademarks
Learning Objectives
Your First Program (1:15)
Quick Practice Lab
Use the lab to answer these questions
Types (3:02)
Quick Practice Lab
Use the lab to answer these questions
Expressions and Variables (3:55)
Lab - Write your first Python code!
String Operations (3:58)
Quick Practice Lab
Use the lab to answer these questions
Lab - String Operations
Review Questions
Learning Objectives
Lists and Tuples (8:51)
Quick Practice Lab
Use the lab to answer these questions
Sets (5:17)
Quick Practice Lab
Use the lab to answer these questions
Lab-Sets
Dictionaries (2:24)
Quick Practice Lab
Use the lab to answer these questions
Labs-Dictionaries
Review Questions
Learning Objectives
Conditions and Branching (10:17)
Quick Practice Lab
Use the lab to answer these questions
Lab-Conditions and Branching
Loops (6:45)
Quick Practice Lab
Use the lab to answer these questions
Lab-Loops
Functions (13:32)
Quick Practice Lab
Use the lab to answer these questions
Lab-Functions
Objects and Classes (10:52)
Quick Practice Lab
Use the lab to answer these questions
Labs-Objects and Classes
Lab - Exception Handling
Review Questions
Learning Objectives
Reading files with open (3:43)
Using loc, iloc, and ix
Lab Reading Files
Writing files with open (2:54)
Lab: Writing files
Loading data with Pandas (3:02)
Quick Practice Lab
Reading: Using loc, iloc and ix (10 min)
Use the lab to answer these questions
Working with and Saving data with Pandas (2:06)
Quick Practice Lab
Use the lab to answer these questions
Loading Data and Viewing Data Pandas and IBM Watson Studio
Lab- Loading Data and Viewing Data
Review Questions
Learning Objectives
Numpy 1D Arrays (11:23)
Quick Practice Lab
Use the lab to answer these questions
Lab - Working with 1 D-Numpy Arrays
Numpy 2D Arrays (7:13)
Quick Practice Lab (Numpy 2D Arrays)
Use the lab to answer these questions
Lab - Working with 2 D-Numpy Arrays
Review Questions
Video: Simple APIs - Part 1 (5:49)
Video: Simple APIs - Part 2 (5:06)
Instructions for Speech to Text and Language Translator API Keys
Lab: Introduction to API (1 Hr)
Lab: Watson Speech to Text and Language Translator API
Lab: HTTP and Requests
Introduction
Guidelines for Submission
Final Assignment: Peer Review Questions
Download your Certificate
Course Certificate

Earn your certificate

Once you have completed this course, you will earn your certificate.

FAQs

Python is actually used a lot in both AI and data science. It’s a very popular language for those who work in both fields. The reason for this is that it’s both easy to learn and easy to use, and many of the data analysis and visualization tasks carried out by data scientists utilize coding in Python. This means that often vacancies advertised stipulate Python as a required skill. With this in mind, therefore, if you’re starting out in programming, then Python is an excellent first language to learn to begin your journey.

This is an interesting question for there is a hot debate in data science about two key languages; R and Python. IBM highlights on their website that both languages have their strengths and their weaknesses. However, they also have their similarities, and interestingly, both languages work well for many data science tasks, such as data manipulation and big data exploration. In general, though, where Python is considered a general-purpose programming language, R has grown directly from statistical analysis. The question that experienced data scientists are asking, therefore, is not which is best for data science, but when is it best to use each language.

You can discover more on this topic in this article written by IBM: www.ibm.com/cloud/blog/python-vs-r

By the end of taking this course, Python for Data Science, AI & Development, you will have the ability to build a program in Python, you will have knowledge of Python fundamentals, and you will have an understanding of Python data structures.

The program is run entirely online. You will not be required to attend any classes in person. To enable this, therefore, you do need to have appropriate access to the internet plus the required technology to be able to use the course materials, which are in the form of articles, videos, and knowledge checks. You will also be encouraged to connect with others on the course through the discussion space... so you need never feel alone.

Yes, if you successfully complete this course, you will earn an IBM Professional Certificate in the subject. However, it's worth noting that it is part of the IBM Applied AI Professional Certificate Program, and thus you will also be one step closer to obtaining IBM Applied AI Professional Certification too if that is a route you are planning to take.

Yes. As long as you have access to the internet and the necessary technology to use the course materials and complete the assignments e.g., articles, videos, and Q&A knowledge checks, you be able to start looking at the course material as soon as you enroll. You will access them through your Dashboard. Thus, with the internet, you will be able to complete the course wherever you live in the world.