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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

1.3 Tools and techniques

As you remember:

Data science is an approach to data analysis with a foundation in code and algorithms.

The data scientist uses computational tools in order to apply statistical techniques.

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