Key Statistical Concepts from Beginner to Advanced

Hello everyone! ??

In last week’s newsletter, a Bhaavana Sreesailam asked a fantastic question:

Heyy Monisha, as newbie in this field I wanted to know which part of statistics is crucial for data analysis?

During my first semester in the Master’s program in Statistics at Loyola College , I had the same question. I remember feeling overwhelmed by the sheer volume of concepts and techniques. However, as I progressed through my studies and applied these concepts in real-world scenarios, I realized how each statistical tool plays a vital role in data analysis.

Based on my experience and also interviewing at FAANG companies and financial firms, here are the key statistical concepts and interview questions that can help you prepare for your next big opportunity.

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Beginner Level: Descriptive Statistics

1. Mean, Median, and Mode: Measures of central tendency that tell you about the average and most common values in your data.
2. Standard Deviation and Variance: These metrics help you understand the spread or dispersion of your data.
3. Histograms and Box Plots: Visual tools that provide a quick snapshot of your data distribution.
4. Sampling Methods: Techniques for selecting a representative subset of the population.
5. Probability Distributions: Functions that describe the likelihood of different outcomes.

Interview Questions:

1. When you’re explaining standard deviation to a non-technical team, how would you describe what it tells us about data?
2. How would you choose a sampling method for a survey?

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Intermediate Level: Inferential Statistics

1. Hypothesis Testing: Techniques like t-tests and chi-square tests help you determine if your results are statistically significant.
2. Confidence Intervals: These provide a range of values that likely contain the population parameter.
3. Regression Analysis: This helps you understand relationships between variables and make predictions.
4. ANOVA (Analysis of Variance): A method to compare means across multiple groups.
5. Correlation Coefficients: Measures the strength and direction of the relationship between two variables.

Interview Questions:

1. During an A/B test, how would you determine if the results are statistically significant?
2. How would you explain the concept of a confidence interval to a client?

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Advanced Level: Probability Theory and Multivariate Analysis

Concepts:

1. Bayesian Statistics: A powerful approach for updating probabilities based on new data.
2. Principal Component Analysis (PCA): A technique for reducing the dimensionality of your data while retaining most of the variance.
3. Machine Learning Algorithms: Many algorithms, like decision trees and neural networks, are grounded in statistical principles.
4. Time Series Analysis: Techniques for analyzing data points collected or recorded at specific time intervals.
5. Experimental Design: Methods for planning experiments to ensure that the data obtained can be analyzed to yield valid and objective conclusions.

Interview Questions:

1. How would you handle missing data in a time series dataset to maintain the integrity of your analysis?
2. How do you prevent overfitting in a machine learning model?

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I hope this guide helps you navigate the essential statistical concepts and prepares you for the future. It’s not just about knowing the formulas; it’s about understanding why you’re using certain methods and how they help make sense of the data.

Mastering statistics is a journey, and every step you take brings you closer to becoming proficient. ???        

Good luck! ??

Thanks for taking the time to read this newsletter. Your support means a lot! If you have any thoughts or stories to share, I’d love to hear them.

Until next time, take care and keep shining! ??