7 Basic Concepts Of Statistics For Data Science Beginners

Descriptive statistics summarize data by finding the average (mean), middle value (median), and most common value (mode), while also measuring how spread out the data is using standard deviation. Probability helps predict the likelihood of events, aiding in decision-making when outcomes are uncertain. Inferential statistics allows conclusions about a larger group based on a smaller sample. Distributions show how data is spread, with the normal distribution being a common example. Correlation shows relationships between variables, while causation indicates one directly influences another. Sampling involves selecting a small group from a population to make broader conclusions. Regression analysis is used to predict outcomes and understand relationships between variables, helping build predictive models in data science.

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Transcript

1. 7 Basic Concepts of Statistics for Data Science Beginners www.ashokveda.com
2. Introduction www.ashokveda.com Data science relies on statistics to turn data into insights. Let’s learn the 7 basic concepts every beginner should understand.
3. Descriptive Statistics www.ashokveda.com This summarizes data using measures like Mean: Average value Median: Middle value Standard Deviation: Data spread
4. Probability www.ashokveda.com Probability calculates how likely an event is to happen. Used for predictions and AI models. Example: What’s the chance a customer buys a product?
5. Inferential Statistics www.ashokveda.com Inferential statistics help predict or generalize about a population using a small sample. Example: Predict customer satisfaction based on a survey.
6. Distributions www.ashokveda.com Distributions show how data is spread. Normal Distribution: Bell-shaped curve Poisson Distribution: Counts events, like website visits
7. Correlation: Shows a relationship between two variables (e.g., sales and ads). Causation: One variable directly affects another. Correlation and Causation www.ashokveda.com
8. Sampling means studying a part of the data to represent the whole. Sampling www.ashokveda.com Random Sampling: Ensures fairness. Used to save time and effort in analysis.
9. Regression predicts outcomes and shows relationships. Regression Analysis www.ashokveda.com Linear Regression: Predicts continuous data like sales. Logistic Regression: For yes/no outcomes, like purchases.
10. Importance in Data Science www.ashokveda.com Analyze trends Predict outcomes Build AI models Make better decisions
11. Python Tools for Statistics www.ashokveda.com Use Python libraries to apply these concepts NumPy for math pandas for data handling scikit-learn for models
12. THANK YOU www.ashokveda.com Mastering these 7 concepts builds your foundation in data science, enabling accurate predictions, insights, and smarter decisions.