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Python Foundations through Data Science

Never written code before? Start here. You will learn the absolute basics of Python, output, variables, types, decisions, loops, and functions, using datasets, samples, and averages as your playground. By the end you are ready for Project 1.

First words

  1. Your first program: announce a dataset
  2. Print numbers: row counts
  3. Comments: annotating your analysis
  4. Strings: column labels
  5. Arithmetic: computing a mean

Remembering things

  1. Make a variable: store a sample value
  2. Reassign a variable: updating the row count
  3. Three basic types: int, float, str
  4. int() and float(): converting a reading
  5. Good names: data science style

Making choices

  1. Boolean comparison: is this value above average?
  2. if: flag an outlier
  3. if/else: include or skip a sample
  4. elif: data quality bands
  5. and / or / not: combined data filters

Doing it again

  1. while: countdown to data collection
  2. for + range: scan dataset rows
  3. Accumulation: summing observations
  4. break: stop when data goes missing
  5. Nested loops: a dataset grid

Many values and reuse

  1. Lists: a column of observations
  2. Loop a list: find the maximum value
  3. Your first function: describe a dataset
  4. Parameters and return: a mean function
  5. Capstone: a dataset report

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