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 fundamental understanding of mathematics, the tools present in the current era make data analysis data wrangling easy to learn and implement. In this course, we will be using the basic essential tools with which anyone interested in the field 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 assigned to build the technical skills needed for the labs. At the end of each week, we have provided homework in order for students to be able to practice their skills on provided datasets.
Practicums are the practical, hands-on projects that empower students to use the skills they have learned in lectures and tutorials to work on a bigger project. In this course, we will be working with three labs with the last lab being a 2 week final Project.
- Python
- Pandas
- Matplotlib
- NumPy
- Seaborn
| Week | Category | Name | Datasets |
|---|---|---|---|
| 1 | Introduction | Welcome to DigitalHistory | - |
| 2 | Tutorial | Introduction to Python & NumPy | - |
| 3 | Tutorial | Introduction to open data, importing data and basic data wrangling | Titanic & US Census Demographic Data |
| 4 | Tutorial | Introduction to data visualization and graphs with matplotlib | California Housing |
| 5 | Lab | Visualizing the Translatlantic Slave Trade | Trans Atlantic Slave Trade |
| 6 | Tutorial | Advanced data wrangling using Pandas | January Flight Delays |
| 7 | Tutorial | Visualizations and Exploratory Analysis using Seaborn and Pandas | Recent Graduates |
| 8 | Tutorial | Intro to statistical analysis and methods | Campus Recruitment |
| 9 | Lab | Statistical Analysis on the Runaway Slave Dataset | Freedom On the Move |
| 10 | Tutorial | Introduction to modeling and the analysis pipeline | - |
| 11 | Tutorial | Guest speaker session | - |
| 12 | Lab | Final project | - |
| 13 | Lab | Final project | - |