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Essential Tools for Data Scientists | Spring 2020
Welcome to Data Science Society at Berkeley’s very own DeCal: Essential Tools for Data Scientists! This course is geared towards exposing students to essential data science skills that are demanded in industry and are meant to be taken as a follow-up or alongside Data 8. The course covers the bits of data science and machine learning that aren’t traditionally taught in the classroom like advanced Pandas and Seaborn, and visualization dashboards that will challenge you, sharpen your skills and elevate you in the internship game. In this course, you will learn everything you need to know from the ground up from an introduction on Python, to software like Excel and Tableau, to other essential skills through a personalized data science project that includes data cleaning, visualization, statistical analysis, and machine learning.
PrerequisitesThere are no formal prerequisites for this course. It is recommended to have some level of basic programming experience in Python, but not required. We want you to learn as much as possible and will help you get up to speed quickly!
TextsThere is no textbook. Content is created by instructors and TAs.
Late Submission Policy10% of the total assignment grade will be deducted for every day the project is turned in late. No late lab submissions are allowed.
The first module of the final project will be due on April 10th at 11:59 PM. There is no submission for the checkpoint but we expect that you will have cleaned your data set and checked in with a TA via either office hours or email. The attached file can be found below. Please download the file as a Python notebook and import it into Datahub so you are able to work on the project in a Jupyter notebook. To import the file into Datahub, simply go to datahub.berkeley.edu, click "Upload" in the top right corner and upload the file! Once you are done, please re-download your completed file as a PDF (File --> Download as PDF), then submit it to us (instructions for how to submit the module are at the end of the Jupyter notebook).Who's my TA?
||Monday, 2/10/20||Welcome, Logistics & Python Bootcamp||Slides||None||All|
||Monday, 2/14/20||Data Manipulation and Wrangling: Pandas Part 1||Slides||DataCamp (Chapter 1), Quiz||Marta, Luke|
||Monday, 3/2/20||Data Manipulation and Wrangling: Pandas Part 2||Slides||DataCamp (Chapter 2), Quiz||Avik|
||Monday, 3/9/20||SQL||Slides||DataCamp (Chapter 1)||Naman, Kanu|
||Monday, 3/16/20||Statistical Models: NumPy for Linear and Logistic Regression||Slides, YouTube||None||Nikhil, Elton, Jae|
||Monday, 3/30/20||Data Visualization and Exploratory Data Analysis||Slides, YouTube||DataCamp (Optional) (Chapter 1), Quiz||Dhruv, Uma|
||Monday, 4/6/20||Speadsheets||Slides, YouTube||DataCamp (Optional) (Chapters 1 and 2)||Avik, Gayatri, Varun, Kate, Naman|
||Monday, 4/13/20||Tableau||Slides, YouTube||DataCamp (Optional) (Chapter 1)||Jae, Naman, Kanu, Kate, Avik, Elton, Varun, Gayatri|
||Monday, 4/20/20||Exploring Seaborn In-Depth||Notebook, YouTube||DataCamp (Optional) (Chapter 1)||Kanu|
||Monday, 4/27/20||Special Topic Guest Leture||YouTube||Dhruv|