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Summary - Statistics

In this group you learned mean, median, standard deviation, and correlation.

Summary: statistics

In this group you learned to summarise data with numbers:

  • Centre: mean (sensitive to outliers), median (robust), mode (categorical).
  • Spread: variance and standard deviation; the ddof difference.
  • Relationship: correlation; linear only, not causation.
  • Per-feature computation with axis=0.
print(X.mean(axis=0), X.std(axis=0))
print(np.corrcoef(X, rowvar=False))   # correlation matrix between columns

These measures form the basis of scaling and feature selection in the next groups.

Practice Quick summary

Print the mean, median and standard deviation of x in a single print, separated by spaces.

Starter code

model.py
import numpy as np

x = np.array([3, 5, 7, 9, 11])

# buraya yaz

Sample solution

model.py
import numpy as np

x = np.array([3, 5, 7, 9, 11])

print(x.mean(), np.median(x), x.std())

Expected output

7.0 7.0 2.8284271247461903

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 Which best describes the typical salary in a skewed salary distribution?

  • The median Correct answer
  • The mean
  • The variance
  • The maximum

Explanation: A few very high salaries pull the mean up; the median isn't affected.

2 Compute the standard deviation of each column.

Question 
s = X.std(____)
Answer 
s = X.std(axis=0)

Explanation: axis=0 gives one value per feature.

3 Match the concepts.

Term Meaning
center Mean, median
spread Variance, std
relationship Correlation
robust Median

All lessons in Beginner

1 What is ML?

  1. What is machine learning?
  2. Supervised and unsupervised
  3. Data and model
  4. Training and testing
  5. Feature and label notation
  6. Summary - ML fundamentals

2 NumPy basics

  1. What is NumPy?
  2. Creating arrays
  3. Array properties
  4. Array indexing
  5. Reshaping arrays
  6. Summary - NumPy basics

3 Pandas basics

  1. What is Pandas?
  2. Creating a DataFrame
  3. Column operations
  4. Selecting and filtering data
  5. Viewing first rows
  6. Summary - Pandas basics

4 Data preprocessing

  1. Data cleaning
  2. Missing data
  3. Data types
  4. Label encoding
  5. Data splitting
  6. Summary - Data preprocessing

5 Statistics basics

  1. Mean
  2. Median
  3. Standard deviation
  4. Variance
  5. Correlation
  6. Summary - Statistics

6 Linear algebra

  1. Vectors
  2. Matrices
  3. Matrix multiplication
  4. Transpose
  5. Identity matrix
  6. Summary - Linear algebra

7 Project: Data analysis

  1. Loading data
  2. Cleaning data
  3. Computing statistics
  4. Printing results

8 Regression

  1. What is regression?
  2. Linear regression
  3. Model training
  4. Prediction
  5. Error measurement
  6. Summary - Regression

9 KNN

  1. What is KNN?
  2. The K parameter
  3. Distance measurement
  4. Classification
  5. Model training
  6. Summary - KNN

10 Decision trees

  1. What is a decision tree?
  2. Splitting criterion
  3. Depth
  4. Classification
  5. Overfitting
  6. Summary - Decision trees

11 Classification

  1. What is classification?
  2. Binary classification
  3. Multi-class
  4. Accuracy
  5. Confusion matrix
  6. Summary - Classification

12 Model evaluation

  1. Train-test split
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Model evaluation

13 Project: Classification

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

14 Scaling

  1. What is scaling?
  2. StandardScaler
  3. MinMaxScaler
  4. When to use?
  5. Pipeline
  6. Summary - Scaling

15 Feature engineering

  1. What is a feature?
  2. Feature selection
  3. Feature extraction
  4. Categorical features
  5. Numerical features
  6. Summary - Feature engineering

16 Model selection

  1. What is model selection?
  2. Cross-validation
  3. Grid search
  4. Score comparison
  5. Hyperparameter
  6. Summary - Model selection

17 Project: Regression

  1. Preparing data
  2. Training model
  3. Prediction
  4. Error measurement

18 Metrics

  1. What is a metric?
  2. Accuracy
  3. Precision
  4. Recall
  5. F1 score
  6. Summary - Metrics

19 Data visualization

  1. What is visualization?
  2. Scatter plot
  3. Histogram
  4. Line plot
  5. Box plot
  6. Summary - Visualization

20 Beginner final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4