IBM Python Data Science Program

This is my GitHub repository for a program of courses offered by IBM through www.edx.org on learning to code in Python.
Program Name: Python Data Science

The program consists of the following 9 courses:

Course Order	Course ID	Course Name
01	IBM PY0101EN	Python Basics for Data Science
02	IBM DA0101EN	Analyzing Data with Python
03	IBM DS0101EN	Introduction to Data Science
04	IBM TBC	Data Science Tools
05	IBM TBC	The Data Science Method
06	IBM TBC	SQL for Data Science
07	IBM TBC	Visualizing Data with Python
08	IBM TBC	Machine Learning with Python: A Practical Introduction
09	IBM TBC	Data Science and Machine Learning Capstone Project

Course Descriptions

The contents and descriptions of the IBM courses are as below.

Python Basics for Data Science

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 is it useful
• The application of Python
• How to define variables
• Sets and conditional statements in Python
• The purpose of having functions in Python
• The purpose of having classes and objects 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
• How to use Numpy arrays
• APIs and web scraping in Python

Analyzing Data with Python

Welcome to Data science with python, in this course, you will go over everything from how to load data, to building basic machine learning models. Each level in the pyramid summarises concepts you will learn in each module. The outer labels give a general summary, as you can see this course will start you off with the basics, the libraries you need and how to work with data. You will learn how to analyze data using summary statistics; this section is useful if you would like to make an argument with data. Finally, you will learn how to build Machine Learning Models and see how these models work in the real world.

This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

Topics covered:

1. Importing Data Sets

2. Cleaning the Data

3. Data Frame Manipulation

4. Summarizing the Data

5. Building Machine Learning Regression Models

6. Building Data Pipelines

Analyzing Data with Python will be delivered through lecture, lab, and assignments and includes the following parts:

Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample data set. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool data sets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

Introduction to Data Science

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

In this course you will:

• Meet people who work in data science.
• Explore definitions of data science.
• Learn about data science in a business context.
• Discover some use cases and applications of data science.

Data Science Tools

The Data Science Method

SQL for Data Science

Visualizing Data with Python

Machine Learning with Python: A practical Introduction

Data Science and Machine Learning Capstone Project