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Learn Statistics for Data Science Quickly
Statistics transforms raw data into insights. Master these fundamentals to make data-driven decisions effectively.
Statistics is the heart and soul of data science. Tools in statistics will allow a data scientist to analyze, interpret, and make sense of data. Here’s an essential guide to statistical concepts in a nutshell:
1. Descriptive Statistics
Summarize data using the following:
Mean, Median, Mode: Central tendencies.
Standard Deviation, Variance: Data spread.
2. Basic Probability
Inference foundation
Distributions: Normal, Binomial, Poisson.
Conditional Probability: The likelihood of some event given another event.
3. Inferential Statistics
Drawing Conclusions From Samples
Hypothesis Testing: Null vs. alternative.
Confidence Intervals: Likely range of parameter.
4. Regression Analysis
Describing the relationships between models:
Linear Regression: Outcome is continuous in nature.
Logistic Regression: Outcome is in binary categories.
5. Bayesian Statistics
Update probability using Bayes’ theorem by taking new evidence.
6. Data Visualization
Communicate insights effectively:
Histograms, Box Plots, Scatter Plots: Summarize and visualize data.
7. Sampling and Resampling
Work with subsets:
Random Sampling: Selecting with equal chance.
Bootstrap: Resample to reduce bias.
Applications
Used in marketing (segmentation), healthcare (predictions), finance (risk), and tech (A/B testing).
