DS 200: Introduction to Data Sciences

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DS 200: Introduction to Data Sciences

Description

DS 200: Introduction to Data Sciences (4 credits) - The course introduces students to data sciences, an emerging discipline focused on the knowledge and skills needed to harness the power of data to advance science and engineering, address complex national and global challenges, inform public policy, and improve human lives. It demonstrateshow the discipline of data science integrates knowledge and skills in computer sciences, statistics, and informatics (with exposure to application domains such as life science, health science, cyber security, astronomy, etc). Through a combination of lectures, hands-on labs, and case studies, students   are introduced to the "big picture" of data sciences including elements of understanding data through exploratory data analysis, testing hypotheses against data, building predictive models, all using real-world examples. The course also introduces students to opportunities to specialize in Applied Data Sciences (with an emphasis on data sciences applications in the real world), Computational Data Sciences (with an emphasis on well-engineered data analytics systems), and Statistical Data Sciences (with an emphasis on advanced statistical theory and methods).

Materials

I will be using various resources for the course. The following are the three main resources:

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning by Chris Albon

Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller (Author), Sarah Guido

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

Useful Links:

https://colab.research.google.com/

www.hackerrank.com

https://chrisalbon.com/

https://scikit-learn.org/

https://pandas.pydata.org/docs/ https://matplotlib.org/

https://numpy.org/

Course Format:

This is a web class so we will follow the web or remote asynchronous format for this class. Please visit the remote learning help page at keeplearning.psu.edufor Frequently Asked Questions and helpful links for remote learning assistance.

I use a partially flipped classroom model for my classes. For online classes we will follow the following format: All the modules for the week will be posted in the beginning of the week. Each module section will consist of videos and a worksheet. There will be a discussion section for each week and you have to post questions related to the worksheets in the discussion section. You have to submit all the completed worksheets by the end of every week to claim the 10% class participation points.

Reading:  It is expected that you will read the relevant slides/book section before each lecture.

Homework: Homework will be assigned on datacamp. The links will be posted on Canvas.

Grading of the Course:

Course Grading Scale

The following are minimum cutoffs for each grade:

•     93.00% = A

•     90.00% = A-

•     87.00% = B+

•     83.00% = B

•     80.00% = B-

•     77.00% = C+

•     70.00% = C

•     60.00% = D

•     less than 60.00% = F