Data Analytics Programming for Analytics with Python Syllabus

Programming for Analytics with Python syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Programming for Analytics with Python in Data Analytics, not a summary of it.

1. 1

   Python Fundamentals

   4 topics

   • Variables and Data Types
   • Control Flow
   • Functions
   • Data Structures
2. 2

   NumPy for Numerical Computing

   4 topics

   • Arrays and ndarray
   • Vectorized Operations
   • Broadcasting
   • Array Indexing and Slicing
3. 3

   Data Manipulation with Pandas

   6 topics

   • Series and DataFrames
   • Reading and Writing Data
   • Indexing and Selection
   • Filtering and Sorting
   • GroupBy and Aggregation
   • Merging and Joining DataFrames
4. 4

   Cleaning Data in Python

   4 topics

   • Handling Missing Data
   • String and Date Manipulation
   • Reshaping Data
   • Applying Functions and Mapping
5. 5

   Visualization Libraries

   4 topics

   • Matplotlib Basics
   • Seaborn for Statistical Plots
   • Plotly for Interactive Charts
   • Customizing Visuals

Programming for Analytics with Python flashcards for Data Analytics

23 of 50 cards from the Programming for Analytics with Python deck — real questions with worked answers.

1. In Python, what is the difference between a mutable and an immutable data type, and give one example of each?

   A mutable object can be changed in place after creation; an immutable object cannot. Example mutable: list, dict, set. Example immutable: int, float, str, tuple.

2. What are Python's four built-in numeric/text scalar types used for variables, and how do you check a variable's type at runtime?

   Common scalar types are int, float, bool, and str (complex also exists). You check a variable's type with the built-in function type(x), or test membership with isinstance(x, int).

3. What is dynamic typing in Python, and what does it imply about variable declarations?

   Dynamic typing means a variable's type is determined at runtime by the value assigned, not declared in advance. The same name can be rebound to values of different types, and no explicit type declaration is required.

4. In Python control flow, what keyword combines an else with a nested if to create a multi-way branch, and what is its syntax?

   The elif keyword. Syntax: if condition1: ... elif condition2: ... else: ... Each branch's block is indented; only the first true condition's block executes.

5. What is the difference between a for loop and a while loop in Python?

   A for loop iterates over the items of an iterable (list, range, string, etc.) a definite number of times; a while loop repeats as long as a boolean condition remains True, used when the number of iterations is not known in advance.

6. What do the break and continue statements do inside a Python loop?

   break immediately terminates the entire enclosing loop. continue skips the rest of the current iteration and jumps to the next iteration of the loop.

7. How do you define a function in Python, and what statement returns a value from it?

   Use the def keyword: def name(parameters): followed by an indented body. The return statement sends a value back to the caller; without it (or a bare return), the function returns None.

8. What is the difference between a positional argument, a keyword argument, and a default parameter value in a Python function?

   A positional argument is matched by order. A keyword argument is passed as name=value. A default parameter value (def f(x=10)) is used when the caller omits that argument.

9. What do *args and **kwargs mean in a Python function signature?

   *args collects extra positional arguments into a tuple; **kwargs collects extra keyword arguments into a dict. They let a function accept a variable number of arguments.

10. What is a lambda function in Python and when is it typically used?

    A lambda is an anonymous, single-expression function written as lambda args: expression. It is typically used inline as a short throwaway function, e.g., as the key in sorted() or the argument to map, filter, or pandas apply.

11. Compare Python's list, tuple, set, and dict by mutability and ordering.

    list: ordered, mutable, allows duplicates. tuple: ordered, immutable, allows duplicates. set: unordered, mutable, no duplicates. dict: ordered (by insertion, 3.7+), mutable, unique keys mapping to values.

12. What is a Python list comprehension and give its general syntax?

    A concise way to build a list from an iterable. General syntax: [expression for item in iterable if condition], e.g., [x**2 for x in range(5) if x % 2 == 0].

13. How do you access a value in a Python dictionary, and how does d[key] differ from d.get(key)?

    Both retrieve the value for a key. d[key] raises a KeyError if the key is missing, while d.get(key) returns None (or a supplied default) instead of raising an error.

14. What distinguishes a NumPy ndarray from a Python list?

    An ndarray is a fixed-size, homogeneous (single dtype) N-dimensional array stored in contiguous memory, enabling fast vectorized operations. A Python list is a heterogeneous, dynamically sized container of object references and is slower for numerical work.

15. What do the ndarray attributes .shape, .ndim, .size, and .dtype each report?

    .shape is a tuple of dimension lengths; .ndim is the number of dimensions (axes); .size is the total number of elements; .dtype is the data type of the elements.

16. Which NumPy functions create an array of all zeros, all ones, and an evenly spaced range, respectively?

    np.zeros(shape) creates all zeros, np.ones(shape) creates all ones, and np.arange(start, stop, step) (or np.linspace(start, stop, num)) creates an evenly spaced range.

17. What is a vectorized operation in NumPy, and why is it faster than a Python loop?

    A vectorized operation applies an element-wise operation to whole arrays at once (e.g., a + b) without an explicit Python loop. It is faster because the looping runs in optimized, compiled C code over contiguous memory rather than the Python interpreter.

18. If a and b are NumPy arrays of the same shape, what does a * b compute?

    It computes the element-wise product: each element $c_i = a_i \times b_i$. It is NOT matrix multiplication (matrix product uses a @ b or np.dot).

19. What is broadcasting in NumPy?

    Broadcasting is the set of rules by which NumPy performs element-wise operations on arrays of different shapes by virtually stretching the smaller array across the larger one, without copying data, so their shapes become compatible.

20. State the NumPy broadcasting rule for comparing two array shapes dimension by dimension.

    Comparing shapes from the trailing (rightmost) dimensions, two dimensions are compatible when they are equal or one of them is 1. A dimension of size 1 is stretched to match the other; if neither condition holds, broadcasting fails with a ValueError.

21. Given a of shape $(3,1)$ and b of shape $(1,4)$, what is the shape of a + b under broadcasting?

    The result has shape $(3,4)$: the size-1 dimensions are each stretched to match the other array.

22. How does NumPy zero-based indexing let you access the element in row 2, column 3 of a 2D array a?

    Use a[2, 3] (rows and columns are counted from 0). The comma separates the axis indices; a[2, 3] is the element in the third row and fourth column.

23. What does the slice a[1:5:2] return for a 1D NumPy array, and what do the three numbers mean?

    It returns elements at indices 1 and 3 (start=1, stop=5 exclusive, step=2). The general form is start:stop:step, with stop excluded.

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