Weekly Due Date Info:
1.1 Basic input and output
1.2 Errors
1.3 Why whitespace matters
1.4 Salary calculation
1.5 Output art
1.6 zyLab training: Basics
1.7 zyLab training: Interleaved input / output
1.8 LAB: Formatted output: Hello World!
1.9 LAB: Input: Welcome message
1.10 LAB: Input: Mad Lib
2.1 Variables and assignments
2.2 Identifiers
2.3 Objects
2.4 Numeric types: Floating-point
2.5 Arithmetic expressions
2.6 Python expressions
2.7 Division and modulo
2.8 Module basics
2.9 Math module
2.10 Representing text
2.11 Number games
2.12 LAB: Divide by x
2.13 LAB: Driving costs
2.14 LAB: Expression for calories burned
2.15 LAB: Using math functions
3.1 Intro to plotting and visualizing data
3.2 Styling plots
3.3 Text and annotations
4.1 What is data?
4.2 What is data visualization?
4.3 Python for data visualization
4.4 Data frames
4.5 Bar charts
4.6 Pie charts
4.7 Scatter plots
4.8 Line charts
5.1 String basics
5.2 List basics
5.3 Set basics
5.4 Dictionary basics
5.5 Common data types summary
5.6 Additional practice: Grade calculation
5.7 Type conversions
5.8 String formatting
5.9 Additional practice: Health data
5.10 LAB: Caffeine levels
5.11 LAB: House real estate summary
5.12 LAB: Simple statistics
6.1 If-else branches (general)
6.2 If-else statement
6.3 Equality and relational operators
6.4 Boolean operators and expressions
6.5 Membership and identity operators
6.6 Order of evaluation
6.7 Code blocks and indentation
6.8 Additional practice: Tweet decoder
Exam 1 (Chap 1-5)
7.1 Survey sampling
7.2 Measures of center
7.3 Measures of variability
No class - power outage
7.4 Box plots
7.5 Histograms
7.6 Violin plots
8.1 Loops
8.2 While loops
8.3 For loops
8.4 Counting with range()
8.5 Getting index and value enumerate()
8.6 Dice statistics
8.7 LAB: Count input length
8.8 LAB: Output range with increment
8.9 LAB: Smallest and largest numbers
8.10 LAB: Output values below an amount
8.11 LAB: Adjust values by normalizing
9.1 Introduction to probability
9.2 Addition rule and complements
9.3 Multiplication rule and independence
9.4 Conditional probability and Bayes' Theorem
9.5 Combinations and permutations
11.1 Introduction to random variables
11.2 Properties of discrete probability distributions
11.3 Properties of continuous probability distributions
11.4 The normal distribution
11.5 The Student's t-Distribution
12.1 Confidence intervals
12.2 Confidence intervals for population means
12.4 Hypothesis tests
13.1 Introduction to simple linear regression (SLR)
13.2 What is a time series?
13.3 Time series patterns and stationarity
14.1 What is data mining?
14.2 Data preparation
14.3 Analyzing results
14.4 Supervised learning
14.5 Unsupervised learning
15.1 Misleading statistics
15.3 Data privacy
15.4 Ethical guidelines