What Statistics to Learn for Data Science?

Data science is like uncovering hidden treasures in data to help us make smart choices. To do well in this world, it’s important to learn about statistics. Statistics is like the secret language of data science. In this blog post, we’ll explore the most important stats concepts and tricks you need to know in simple terms.

Check-> 10 Best Statistics Courses

Why Statistics Matters

Before we dive into the nitty-gritty of statistics for data science, let’s understand why it’s so important.

Statistics helps us:

• Explore Data: It helps us see what’s going on in our data.
• Prepare Data: It gets our data ready for analysis.
• Make Predictions: It helps us guess what might happen based on our data.
• Build Cool Stuff: We use it to create smart computer programs and models.
• Test Ideas: We can check if changes we make are really working.

Now, let’s get into the key stats concepts you should know for data science.

Describing Data

Describing data is like telling a story about it. These stats tools help us do that.

1. Averages

• Mean: It’s just the average number.
• Median: The middle number when you line up all your data.
• Mode: The number that shows up the most.

2. Spread of Data

• Range: The biggest number minus the smallest number.
• Variance: It’s like a measure of how much our data is spread out.
• Standard Deviation: This one helps us see if the numbers are close together or far apart.

3. Data Patterns

• Normal Distribution: Imagine a hill-shaped curve; that’s normal.
• Skewness: It’s like saying if our data is leaning to one side.
• Kurtosis: This one tells us if our data has extreme values.

Making Predictions

Making predictions is like guessing the future, and these stats tools help us do it.

4. Testing Ideas

• Hypothesis Testing: It’s like a detective game to check if our ideas are true or not.
• p-value: A small p-value means we have strong evidence for our ideas.

5. Confidence Intervals

• Confidence Intervals: A range that probably has the right answer.
• Margin of Error: This tells us how sure we are about our guess.

6. Guessing Relationships

• Regression Analysis: It helps us understand how things are connected.
• Simple Linear Regression: This is when we look at the relationship between two things.
• Multiple Linear Regression: Here, we study the connections between more than two things.

The Magic of Probabilities

Probability is like predicting with a bit of uncertainty. Let’s explore it.

7. Probability Basics

• Probability: It’s just a fancy word for the chance of something happening.
• Probability Distribution: It shows the chances of different things happening.
• Conditional Probability: The chance of something happening based on something else happening first.

8. Bayesian Probability

• Bayes’ Theorem: A special way to figure out chances when you know some things already.
• Bayesian Inference: Making guesses with the help of Bayes’ Theorem and what we already know.

Check-> 9 Free Online Courses for Statistics for Data Science

Sampling and Collecting Data

Collecting data is like gathering clues, and these are the tools to do it right.

9. Picking Samples

• Simple Random Sampling: Everyone gets an equal chance to be picked.
• Stratified Sampling: Dividing our data into groups and picking from each group.
• Cluster Sampling: We pick whole groups randomly.

10. Smart Experiments

• Control Groups: Some groups don’t get the experiment, so we can compare and see if it worked.
• Randomization: Mixing things up randomly to avoid unfairness.

Machine Learning Metrics

Machine learning is like teaching computers to think. These tools help us see how well they’re doing.

11. Accuracy and Errors

• Accuracy: How often our computer is right.
• Precision and Recall: How good our computer is at finding the right stuff.
• Mean Absolute Error (MAE) and Mean Squared Error (MSE): How wrong our computer is when making guesses.

12. Checking Models

• Cross-Validation: Making sure our computer isn’t just memorizing things.
• Overfitting and Underfitting: Making sure our computer learns well and doesn’t guess too much.

Time Series Magic

Time series is like seeing patterns in time. These tools help us with that.

13. Time Series Parts

• Trend: The long-term pattern in data.
• Seasonality: Repeating patterns that happen at certain times.
• Noise: Random ups and downs that hide the real patterns.

14. Predicting Time

• Exponential Smoothing: Guessing what’s coming soon using what’s happened recently.
• ARIMA Models: Fancy tools to make time-based predictions.

Some Best Statistics Resources-

• Intro to Statistics- FREE Course
• Intro to Inferential Statistics– FREE Course
• Intro to Descriptive Statistics– FREE Course
• Basic Statistics (Online Course)
• Statistics and probability (Khan Academy)
• Practical Statistics for Data Scientists (TextBook)
• Data Science: Statistics and Machine Learning Specialization(Online Course)
• Statistics for Data Science (YouTube Video)

Conclusion

Statistics might sound tricky, but it’s like learning the secret language of data science. Armed with these simple statistics concepts, you’ll be ready to explore data, make smart predictions, and build awesome stuff in the exciting world of data science. So, let’s dive in and unlock the power of statistics!