Data Science
Extracting insight from data using statistics, code, and domain knowledge.
CurrentintermediateGuide only -- no course yet
Overview
Data science combines statistics, programming, and domain expertise to extract insight and build predictive models from data. In practice it's an iterative loop: gather and clean data, explore it, model or summarize it, then communicate findings -- most of the actual time going to the first two steps.
- What it is
- The practice of extracting insight and building models from data, combining statistics and programming.
- Why it's used
- Organizations increasingly make decisions from data rather than intuition alone -- data science is the skill set that turns raw data into that evidence.
- Where it fits
- Builds on Python and basic statistics; NumPy/Pandas/SciPy are the core tools used day to day.
Core concepts
- Data cleaning
- Exploratory data analysis
- Descriptive statistics
- Visualization
- Communicating findings
Example
Most real data science work looks like this: load data, group and aggregate it, and look at the result -- the sophisticated modeling steps most people picture are a smaller fraction of the actual job.
import pandas as pd
df = pd.read_csv("sales.csv")
print(df.groupby("region")["revenue"].sum())Common use cases
- Business intelligence and reporting
- A/B test analysis
- Building predictive models
Project ideas
- Analyze a public dataset (e.g. a CSV of sales or weather data) and summarize three findings