DATA88C.1 - Introduction to Python

About this course (Part 1 of 3): C88C: Computational Structures in Data Science introduces computational thinking and programming concepts specifically tailored for data science applications. The course covers fundamental programming constructs, data structures, algorithms, and computational problem-solving techniques. Students learn Python programming with a focus on data manipulation, analysis, and visualization. Part 1 builds the foundation in variables, control structures, functions, and core data types. This part provides the computational base for the rest of the course and serves as a bridge between general programming and data-specific applications.

Topics¶

Part 1 (sometimes listed as 88.1X on edX) includes 7 modules:

1. Module 1: Introduction - Computer Science vs. Data Science, the data science process, and the role of algorithms.

2. Module 2: Abstractions and Functions - Function definitions, environment diagrams, doctests, and optional arguments.

3. Module 3: Iteration - for and while loops, control flow.

4. Module 4: Sequences and Containers - Sequences, tuples, and lists.

5. Module 5: Higher Order Functions - Functions as values, map, filter, reduce, and functions that return functions.

6. Module 6: Sequences and HOFs - Lambdas and list comprehensions.

7. Module 7: Mutable Data - Mutability, identity, dictionaries, and mutation with function arguments.

The part ends with Review and Wrap-up for 88.1X.

Structure and assignments¶

• Modules: 7 content modules (plus “Succeeding in this Course” and Review and Wrap-up).

• Labs: 4 Jupyter lab assignments (Modules 1, 2, 3, and 6).

• Quizzes: 7 quizzes (one per module).