This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Key Features
Some common concepts taught in these courses are:
- Different types of information systems
- Project management techniques
- Modeling different data systems
- Developing programs related to data processing
- Testing and maintaining systems of data
Overview
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization.
This course will introduce you to the field of data science and prepare you for the next three courses in the Specialization: Design Thinking and Predictive Analytics for Data Products, Meaningful Predictive Modeling, and Deploying Machine Learning Models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Outline
Week 1: Introduction to Data Products
This week, we will go over the syllabus and set you up with the course materials and software. We will introduce you to data products and refresh your memory on Python and Jupyter notebooks.
Week 2: Reading Data in Python
This week, we will learn how to load in datasets from CSV and JSON files. We will also practice manipulating data from these datasets with basic Python commands.
Week 3: Data Processing in Python
This week, our goal is to understand how to clean up a dataset before analyzing it. We will go over how to work with different types of data, such as strings and dates.
Week 4: Python Libraries and Toolkits
In this last week, we will get a sense of common libraries in Python and how they can be useful. We will cover data visualization with numpy and MatPlotLib, and also introduce you to the basics of webscraping with urllib and BeautifulSoup.
Final Project
Create your own Jupyter notebook with a dataset of your own choosing and practice data manipulation. Show off the skills you’ve learned and the libraries you know about in this project. We hope you enjoyed the course, and best of luck in your future learning!
Benefits
WHAT YOU WILL LEARN
- Develop data strategy and process for how data will be generated, collected, and consumed
- Load and process formatted datasets such as CSV and JSON.
- Deal with data in various formats (e.g. timestamps, strings) and filter and “clean” datasets by removing outliers etc.
- Basic experience with data processing libraries such as numpy and data ingestion with urllib, requests
