```
title Data Science
term Spring 2020
credits 4
time Tues/Fri 1:30 - 2:50pm
level Intermediate
place Brown Science / Sci 217
faculty Jim Mahoney
repeat no, cannot be repeated for credit
prereq previous programming experience and some facility with math
```

**Data Science from Scratch: First Principles with Python 2nd Edition** by Joel Grus, ISBN 1492041130 | amazon

Data science combines data analysis, computing, and numerical methods to analyze and understand large collections of numbers from all sorts of sources. It's been gaining popularity lately as a paradigm for interpreting everything from movie recommendations to image recognition. Using the Python programming language, this course will explore the basics of data science through statistics, numerical visualization, and machine learning.

```
week 0 Jan 23 : chap 1 jupyter, terminal, getting started
week 1 Jan 28 : chap 2, 9 coding review & practice
week 2 Feb 4 : chap 3, 4 visualization ; matrices
week 3 11 : chap 5, 6 statistics & probability
week 4 18 : 6, 7 ...
week 5 25 : chap 10, 11 machine learning intro ; look at kaggle
week 6 Mar 3 : chap 12 * k neighbors
week 7 10 : chap 13 * naive bayes : spam filter
-- spring break --
week 8 31 : chap 14, start 8 * regression
week 9 Apr 7 : chap 15, finish 8 ...
week 10 14 : chap 18 * neural nets .
week 11 21 : projects 1
week 12 28 : projects 2
week 13 May 5 : presentations
```

textbook chapters - topics summary

```
summary of chapters
1 intro
2 python | coding background
3 visualize | math background
4 linear algebra |
5 statistics |
6 probability |
7 hypothesis tests |
8 gradient descent | math aside
9 data input | more coding background
10 data exploring | getting off the ground
11 machine learning | overview of methods
12 k-nearest neighbors | method 1
13 naive bayes | method 2
14 linear regression | method 3, part 1 (needs gradient descent)
15 multiple regression | method 3, part 2
16 logistic map | method 3, variation
17 decision trees | method 4
18 neural networks | method 5, part 1
19 deep learning | method 5, part 2
20 clustering | method 6
21 natural language | problem type 1
22 network analysis | problem type 2
23 recommender systems | problem type 3
24 databases and SQL | related topic 1
25 MapReduce | related topic 2
26 ethics | related topic 3
27 epilog
```

https://cs.marlboro.college /cours /spring2020 /data /syllabus

last modified Sun September 27 2020 3:44 am

last modified Sun September 27 2020 3:44 am