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  • 1. Coding for data
    • 1.1 What is data science?
    • 1.2 Why data science?
    • 1.3 Tools and techniques
    • 1.3.1 Computational tools
    • 1.3.2 Statistical techniques
    • 1.4 Plotting the classics
    • 1.4.1 Literary characters
    • 1.4.2 Another kind of character
    • 1.5 Surviving the computer
    • 1.6 About the software
    • 1.7 Using the Jupyter notebook
    • 1.8 More on the Jupyter notebook
  • 2. Programming
    • 2.1 A sampling problem
    • 2.2 A simpler problem
    • 2.3 Introduction to variables
    • 2.4 Introduction to functions
    • 2.5 A first pass
    • 2.6 Expressions
    • 2.7 Call expressions
    • 2.8 Sub-expressions
    • 2.9 Names and variables
  • 3. Data types
    • 3.1 Numbers
    • 3.2 Strings
    • 3.2.1 String methods
    • 3.3 Comparison
    • 3.4 Lists
    • 3.5 Arrays
    • 3.6 Ranges
    • 3.7 Append
    • 3.8 Function arguments
    • 3.9 Leaping ahead
    • 3.10 Iteration with For loops
    • 3.11 Indentation, indentation
    • 3.12 Reply to the Supreme Court
    • 3.13 More on arrays
    • 3.14 Selecting in arrays
    • 3.15 Filling arrays
  • 4. Data frames
    • 4.1 Introduction to data frames
    • 4.2 Data frames, Series and arrays
  • 5. Permutations
    • 5.1 Population and permutation
    • 5.2 A permutation test
    • 5.3 The permutation idea
    • 5.4 Permutation and the t-test
    • 5.5 Testing validity of tests
  • 6. More building blocks
    • 6.1 Introduction to functions
    • 6.2 On None
    • 6.3 Functions in more detail
    • 6.4 Functions as values
    • 6.5 Conditional statements
    • 6.6 Indexing in Pandas
    • 6.7 Example: noble politics
    • 6.8 Safe Pandas
    • 6.9 Text encoding
    • 6.10 Numbers and strings
  • 7. The mean and straight line relationships
    • 7.1 The mean as a predictor
    • 7.2 Where and argmin
    • 7.3 Mean and slopes
    • 7.4 Optimization
    • 7.5 Finding lines
    • 7.6 Using minimize
    • 7.7 Believable slopes
    • 7.8 Combining Booleans
    • 7.9 Standard scores
    • 7.10 Correlation
  • 8. Classification
    • 8.2 Nearest neighbors
    • 8.3 Training and testing
    • 8.4 Rows of tables
    • 8.5 Implementing the classifier
    • 8.6 Accuracy of the classifier
  • 9. Confidence
    • 9.1 The education minister
    • 9.2 Random choice
    • 9.2 Reverse probability
    • 9.3 Bayes bars
    • 9.4 Confidence in bars
  • 10. The end of the beginning
  • Exercises
  • Extra pages
    • More on lists
    • Monty Hall with lists
    • Berkeley introduction to functions
    • Deviations around the mean
    • Squared deviations around the mean
    • Finding the best slope

  1. More building blocks

We have already covered some important parts of programming for data science, such as expressions, variables, data types, arrays and data frames.

In this section, you cover the last two building blocks you will need as a foundation for your future analyses.

These are:

  • Writing functions
  • Conditional statements
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