CSC 380 : Intro to Data Science  

Week 1:  Course Introduction and setup.
 

• Revision of basics needed for the course.

• Welcome & Introduction

• Introduction to Data Science

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Week 2:  Applied Probability and Statistics (1⁄2)
 

• Random Events and Probability

• Moments and Independence

• Statistics & Bayesian Probability

• Binomial Probability

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Week 3: Data Collection and Data Processing, Exploratory Analysis

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• Introduction to Pandas

• Data Collection (Part 1 of 2)

• Data Collection (Part 2 of 2) & Data Processing (Part 1)

• New topics added in this iteration include data scraping using the requests and BeautifulSoup libraries, querying APIs, and a brief introduction to SQL databases with a focus on joins.

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Week 4: Data Visualization, Introduction to ML

 

• Pandas cont…

• Data Preprocessing - Part 1 |

• New topics added in this iteration include processing Tabular Data, Audio and Image data

Week 5:Supervised Learning , Model Assessment

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• Data Preprocessing - Part 2 & Data Visualization

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Week 6:Unsupervised Learning

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• Data Visualization and Introduction to Machine Learning

• Machine Learning - Key Concepts & Supervised ML : Linear Regression

• Hands on Demo

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Week 7: ML continued

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• Data Preprocessing and ML 

• Evaluation and cont Machine Learning.

• Algorithms discussed include Linear Regression, Naive Bayes, Kmeans, Decision Tree, Logistic Regression and Knn.

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Week 8 : Applied Probability and Statistics (2/2)

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• Useful Discrete Distributions

• Useful Continuous Distributions and MLE

• Wrapping up Statistics and Probability

Week 9 :  Deep Learning
 

• Neural Networks

* Less focus on Lectures in the last week,  the focus was on one-on-one attention for the student's final project*