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  • 1. Data science for everyone
    • 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
  • 2. Programming
    • 2.1 A sampling problem
    • 2.2 A simpler problem
    • 2.3 Expressions
    • 2.4 Variables
    • 2.5 Names
    • 2.6 Call expressions
  • 3. Data types
    • 3.1 Numbers
    • 3.2 Strings
    • 3.3 Comparison
    • 3.4 Arrays
    • 3.5 Ranges
    • 3.6 More on arrays
    • 3.7 Arrays and axes
    • 3.8 Reply to the Supreme Court
    • 3.9 Revision - three girls
    • 3.10 Selecting with arrays
  • 4. Data frames
    • 4.1 Introduction to data frames
  • 5. Permutations
    • 5.1 Population and permutation
    • 5.2 lists
    • 5.3 Iteration with For loops
    • 5.4 Indentation, indentation
    • 5.5 Ones and zeros
    • 5.6 A permutation test
  • 6. More on simulation
    • 6.1 Sorting arrays
    • 6.2 Monty hall problem
  • 7. More building blocks
    • 7.2 None
    • 7.1 Functions
    • 7.1 Functions as values
    • 7.2 Conditional statements
  • 8. The mean and straight line relationships
    • 8.1 The mean as a predictor
    • 8.2 Where and argmin
    • 8.3 Mean and slopes
    • 8.4 Optimization
    • 8.5 Finding lines
    • 8.6 Believable slopes
  • 9. Classification
    • 9.1 Standard scores
    • 9.2 Nearest neighbors
    • 9.3 Training and testing
    • 9.4 Rows of tables
    • 9.5 Implementing the classifier
    • 9.6 Accuracy of the classifier
  • 10. The end of the beginning
  • Exercises
    • Thinking about names
    • Three girl simulations
    • Array indexing
    • More simulations
    • Data frames
    • Brexit analysis
    • For loops
    • Money and death
    • Function exercises
    • Function as values exercises
    • Conditional statement exercises
  • Extra pages
    • More on lists
    • Monty Hall with lists
    • Berkeley introduction to functions
    • Deviations around the mean

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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