IST356: Programming Techniques for Data Analytics

IST356: Programming Techniques for Data Analytics#

  🗓️ Term: Fall 2026 

  🏛️ Room: Hinds Hall 111

  📋Sections:

  IST 356 M001 (15927):  Tu/Th  9:30a - 10:50a
  IST 356 M002 (15956):  Tu/Th  2:00p - 3:20p


  👨 Professor: Michael Fudge
  ✉️ Email: mafudge@syr.edu
  🕰️ Office Hours:  M/W 12:00pm - 1:30pm

Course Description#

Approaches for building pipelines in data analytics using the Python programming language; data cleaning, extraction, wrangling, API’s, web scraping. Building data products. Programming experience required.

Additional Course Description#

This course is a tour of programming techniques for building data pipelines for analytics. It will not just emphasize exploratory approaches, but also techniques to build extract transform load pipelines to run code in production. Throughout the course we will learn how to source data from a variety of sources (files, data streams, APIs, web scraping, etc.) and ultimately transform data as to prepare it for dashboards or machine learning. You will also learn some simple data visualization but that is not the primary emphasis of the course.

Prerequisites#

This courses uses the Python programming language. Proficiency n any programming language is the only pre-requisite. Students should have a clear understanding of these concepts:

  • Input, output, variables and data types

  • Control flow statements (if, for, while)

  • Functions (function definition, calling, parameters, return values)

  • Data structures (lists, dictionaries)

  • Using code in other libraries

Audience: IST256 or IST356?#

This course is intended as a follow up course to IST256. It is also appropriate for students with prior experience with programming who have an interest in data analytics.

  • IST256 is for students with little to no programming experience. The course content is 75% python fundamentals and 25% python for data analytics.

  • IST356 is for students with prior programming experience. The course content is 25% python fundamentals and 75% python for data analytics.

Are you ready for this course? 🧪 Take the diagnostic assessment.

Credits#

3 credits

Course Fees#

None

Learning Objectives#

Upon completion of this course, students will be able to:

  1. Explain techniques for sourcing or transforming data, and be able to justify the choice of technique

  2. Solve data-oriented problems using programming techniques

  3. Evaluate different code modules and application programming interfaces for suitability

  4. Apply data transformational programming techniques to build a larger data pipelines

  5. Create production quality data pipelines from exploratory code

Textbooks And Supplies#

📖 Textbooks#

There is no textbook you need to purchase for this course. All required readings are available freely online, and in many cases, the online sources are the most up-to-date references to the relevant course material. See reading list section for additional details.

Main Text#

Bring Your Own Device#

You are expected to bring your computer to each class session. This class is very hands-on, and you will be programming in class often using your own device.

Software to install and Github#

This course requires you to install software on your computer, or use a cloud computer from your Github account. Instructions are provided here:

🧑‍🏫 IST356 Course Setup

Please complete the course setup ASAP. The expectation is you will be ready to code on the first day of class.

Course Requirements and Expectations#

Attendance and Participation#

You are expected to attend and participate in every class session. If you must miss class, you are responsible for making up the work and catching up on what you missed. Do not expect a pre-recorded lecture or instructor support if you miss class. A suggestion is to partner with a classmate to share notes from class.

Assignments#

The assignments are programming / problem solving activities that you will complete outside of class. The assignment due dates are posted on the course schedule. Consider these assignments formative assessments - practice so you can get better at computational thinking, problem solving and writing code. It is important to reflect upon your work and take an honest assessment of your abilities as you complete each assignment.

Each Monday we will review the homework assignment. Students will be asked to contribute to the discussion.

Factors affecting your grade:

  • Is the code working and correct?

  • Were the instructions followed? (e.g. commit after each function is written)

  • Good reflection: specific, uses terminology, actionable

  • Handed in on time, so your prof only needs to review submissions 1 time.

Exams#

Exams are summative assessments. They are designed to test your knowledge of the material. The exams will be issued in class on the dates posted on the course schedule. Exams are closed-book but you may bring one 8.5x11 sheet of paper with your notes on it. Exams length is 60 minutes. They will be a mix of mix of multiple choice, short answer and code tracing and code writing.

Project#

The project is your opportunity to demonstrate what you have learned in an experiential fashion. In your final project you will be expected to create a data pipeline of your choosing.

  • The pipeline should incorporate techniques we learned in the course and the more techniques you incorporate correctly the higher your grade.

  • It is expected that you will be able to explain your choices, and they will be appropriate for the problem.

  • The pipeline should be first written exploratory, and then refactored into a production quality pipeline. Both pipelines should be submitted.

  • There should be a simple data visualization or dashboard from the pipeline output as to demonstrate its usefulness.

Grading#

Assessment

Type

Learning Outcomes

Quantity

Points Each

Points Total

Assignments

Formative

1, 2

8

10

80

Exams

Summative

3, 4

2

50

100

Project

Summative

5

1

20

20

Total

200

Grading Table#

The following grading scale translates your total points earned into a letter grade to be submitted to the University registrar.

Student Achievement

Total Points Earned

Registrar Grade

Grade Points

Mastery

190 - 200

A

4.000

180 - 189

A-

3.666

Satisfactory

170 - 179

B+

3.333

160 - 169

B

3.000

150 - 159

B-

2.666

Low Passing

140 - 149

C+

2.333

130 - 139

C

2.000

120 - 129

C-

1.666

Unsatisfactory

100 - 119

D

1.000

0 - 99

F

0.000

Other Policies#

Use of AI in this Course#

Based on the assignments in this course and our specified learning outcomes, the full use of artificial intelligence (AI) as a tool, with disclosure and citation, is permitted in this course. Students do not need to ask permission to use these tools before starting an assignment, but they must explicitly and fully indicate which tools were used and describe how they were used. Exams are issued in class with the understanding that AI will not be permitted.

Class Schedule#

Week

Tuesday Class

Thursday Class

Friday Assignment Due

1

8/25

8/27

8/28

Course Introduction

Python 1-1

2

9/1

9/3

9/4

Python 1-2

Python 1-3

Assignment 1 (A1) — Submission Practice

3

9/8

9/10

9/11

Python 1-4

UI 2-1

4

9/15

9/17

9/18

UI 2-2

Data Wrangling 3-1

Assignment 2 (A2) — Python

5

9/22

9/24

9/25

Data Wrangling 3-2

Data Wrangling 3-3

Assignment 3 (A3) — UI/Streamlit

6

9/29

10/1

10/2

Data Wrangling 3-4

Data Wrangling 3-5

7

10/6

10/8

10/9

Data Wrangling 3-6

Exam 1 Review

Assignment 4 (A4) — Data Wrangling 1

8

10/13

10/15

10/16

No Class: Fall Break

Exam 1 (E1) — 50 pts

Assignment 5 (A5) — Data Wrangling 2

9

10/20

10/22

10/23

Web APIs 4-1

Web APIs 4-2

10

10/27

10/29

10/30

Web APIs 4-3

Web APIs 4-4

11

11/3

11/5

11/6

Web Scraping 5-1

Web Scraping 5-2

Assignment 6 (A6) — Web APIs

12

11/10

11/12

11/13

Web Scraping 5-3

Web Scraping 5-4

13

11/17

11/19

11/20

Data Visualization 6-1

Data Visualization 6-2

Assignment 7 (A7) — Web Scraping

14

11/24

11/26

No Class: Thanksgiving

No Class: Thanksgiving

15

12/1

12/3

12/4

Data Visualization 6-3

Data Visualization 6-4

16

12/8

12/10

12/11

Exam 2 (E2) — 50 pts, Last Day of Classes

No Class: Final Exam Period

Assignment 8 (A8) — Data Visualization

Finals

12/10 - 12/15

Project Due (P) — see registrar final exam schedule

Reading List#

Unit 1. Python Essentials (Python)#

Reading List#

Reference Documenation#

Unit 2. User Interface (UI)#

Reading List#

Reference Documenation#

Unit 3. Data Wrangling#

Reading List#

Reference Documentation#

Unit 4. Web APIs#

Reading List#

Reference Documentation#

Unit 5. Web Scraping#

Reading List#

Reference Documentation#

Unit 6. Data Visualization#

Reading List#

Reference Documentation#