Description

DS 121 is the second in the three-course sequence (DS 120, 121, 122) that introduces students to theoretical foundations of Data Science. DS 121 covers an introduction to key concepts from Linear Algebra (vector space, independence, orthogonality and matrix factorizations). The DS theme running through the course is exploratory data analysis, enabling a better understanding of the data at hand. The course will link mathematical concepts with computational thinking, specifically through the use of problem sets that require students to answer mathematically-posed questions using computation.

This course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning I, Digital/Multimedia Expression, Critical Thinking.

DS 120 (or equivalent) is a prerequisite, and DS 110 (or equivalent) is a corequisite.

General Information

Course meetings
Lectures are held Tuesdays and Thursdays at 1:30-2:45pm in WED room 130. Discussion labs are held on Mondays at 9:05-9:55am (in CDS 164), 12:20-1:10pm (in BRB 121), 1:25-2:15pm (in CDS 264), and 2:30-3:20pm (in CGS 311). We actively encourage questions and interaction during all lectures and discussion labs.

Announcements

Course schedule
9/4/2023, 10:13:22 AM

This page contains the lesson plan for DS 121 lectures.

All lectures will be livestreamed on Zoom. The lecture notes and videos will be made available after each lecture. All lecture and discussion lab notes are available on Google drive.

WeekTopicReadingDue dates
1

Vectors

[Lecture 1 video]

[Lecture 2 video]

Boyd-Vandenberghe Chapter 1 and 2.1

3Blue1Brown video 1 and video 2
2

Vector algebra

[Lecture 3 video]

[Lecture 4 video]

Boyd-Vandenberghe Chapter 5.1-5.2

Deisenroth-Faisal-Ong Chapter 2.1-2.3


3Blue1Brown video 3 and video 4

HW1 due 9/15

3

Vector geometry & Clustering

[Lecture 5 video]

[Lecture 6 video]

Boyd-Vandenberghe Chapter 3

3Blue1Brown video 5 and video 6		HW2 due 9/22

4

Supervised learning

[Lecture 7 video]

[Lecture 8 video]

Boyd-Vandenberghe Chapter 4HW3 due 9/29

5

Matrix algebra

[Lecture 9 video]

No new reading, but recommend Aggarwal Section 1.1-1.2.1 as test reviewTEST1 on 10/5

6

Matrix geometry

[Lecture 10 video]

Boyd-Vandenberghe Chapter 6 and 7.1

(note: go to discussion labs, not lecture, on 10/10)

HW4 due 10/13

7

Matrix inverses & LU decomposition

[Lecture 11 video]

[Lecture 12 video]

Aggarwal Section 2.1 and 2.3

HW5 due 10/20

8

Vector subspaces

[Lecture 13 video]

[Lecture 14 video]

Aggarwal Section 2.4-2.5

3Blue1Brown video 7 and video 8		HW6 due 10/27

9

Orthogonality & QR decomposition

[Lecture 15 video]

[Lecture 16 video]

Aggarwal Sections 2.6-2.7.2

Boyd-Vandenberghe Chapter 10

3Blue1Brown video 9

HW7 due 11/3

10

Linear regression

[Lecture 17 partial video]

No new reading, but recommend Boyd-Vandenberghe Chapter 11 as test reviewTEST2 on 11/9

11

Markov chains & Eigenvalues

[Lecture 18 video]

[Lecture 19 video]

Boyd-Vandenberghe Chapter 12

Deisenroth-Faisal-Ong Section 4.2

3Blue1Brown video 13 and video 14

HW8 due 11/17

12

PageRank

[Lecture 20 video]

Aggarwal Sections 10.1-10.3 and 10.6

(note: no lecture on 11/23)

13

Diagonalization

[Lecture 21 video]

[Lecture 22 video]

Deisenroth-Faisal-Ong Section 4.4HW9 due 12/1

14

SVD & PCA

[Lecture 23 video]

[Lecture 24 video]

Aggarwal Sections 7.1-7.4HW10 due 12/8

15

Course recap

[Lecture 25 video]

Aggarwal Sections 8.1-8.2(note: no lecture on 12/14)
FINAL EXAM on 12/19

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