In a rapidly progressing data-driven world, data science has become an integral part of nearly every field in the world. This is due to the availability of enormous records of data and our ability to find useful information with it. Although data science/analytics requires a general understanding of mathematics, the tools present in the current era make data analysis and data wrangling easy to learn and implement. In this course, we will be covering and using some essential tools so anyone interested can get started. For this course each week we will be going through multiple datasets. Each dataset will focus on specific parts of the data science toolkit.
The Bit community is powered by collaboration. We believe learning is more fun — and more effective — when we put our heads together.
We are passionate students from all walks of life who come together to making tech education accessible.
Our mission is to provide students the tools and connections they need to unleash their potential in tech.
We believe in democratizing tech education. Everyone should have access to quality resources, opportunities, and networks regardless of their background.
At Bit Project, we strive to create an environment where all people are welcomed, members are engaged, and backgrounds are celebrated.
We welcome everyone, regardless of age, race, class, ethnicity, gender identity or expression, sexual identity, ability, size, nationality, culture, faith, neurotype and background.
We will teach the materials in Google colab. Students will be provided a link to the tutorial page in Google Colab. They will save a copy in their Google Drive, write codes, play around with it, and submit their work by downloading a ipynb from Google colab.
Our curriculum is centered around two types of categories:
Tutorials are guided, step-by-step tutorials to teach concepts and technical skills. These will be video recordings with open Q&A, and they will cover the technical skills needed for the labs. These will be provided with an accompanying python notebook that should have the "Now Try This" sections completed.
Labs are self-directed assignments given between each set of tutorials to cement and test your knowledge, as well as give you some more flexibility in what you're doing with the data. For example, we will provide a dataset and perform some operations during the tutorial, but you may have another question about the data that you can answer yourself with what you've learned that week.
| Week | Name | Datasets |
|---|---|---|
| 1 | Introduction to Python & NumPy | - |
| 2 | Introduction to open data, importing data and basic data wrangling | Titanic & US Census Demographic Data |
| 3 | Introduction to data visualization and graphs with matplotlib | California Housing |
| 4 | Final project | - |