What are the most important statistics topics to prepare for in an interview?

Mastery of Descriptive Statistics is important for most Data Science - related jobs, but is especially crucial to dominate in the job of Data Analyst. Admittedly, the essential topics to know are when to use different measures of center (median, mode, and different types of mean) and dispersion (range, different types of quantiles, standard deviation). An often overlooked skill is not only knowing different types of visualisations, but also when to best apply them. For instance, histogram is best applied for showing distribution of a single categorised variable; line plot is best used to show how a numerical (often continuous) variable changes with the increase of a categorical variable.

Embarking on the statistical journey, you first master Descriptive Statistics, delving into averages, spreads, and quartiles—a storyteller for your data. Probability introduces chance, Bayes' theorem, and distribution buddies. Inferential Statistics, the guessing games, teaches hypothesis testing and the art of confident guesses. Regression Analysis equips you for predicting the future, while various statistical tests—T-tests, ANOVA, and Chi-squared—become tools for group comparisons. Experimental Design guides wise experiments, and Time Series Analysis turns you into a time-traveling pattern detective.

Opinions might vary here, but if represented well, descriptive statistics can do wonders. Get your in-depth knowledge correct, and with the proper visualisation tools, preferably PowerBI or Python can gain you points. Other areas of statistics depend on what are you looking for - an academic would be more interested in inferential statistics as a whole, while a corporate position might be looking for regression topics. And irrespective of how detailed your knowledge is, it is incomplete without programming knowledge.

Todo depende de las necesidades requeridas y del sector de la empresa en donde se vaya a trabajar. Hay empresas que por su dinámica solo basta que el profesional tenga habilidades de estadística descriptiva y eso es suficiente para los objetivos del cargo. De todas formas siempre es adecuado que todo profesional de la estadística independiente del alcance del análisis, es recomendable que inicie su aventura con un buen análisis exploratorio de los datos. Eso le permitirá conocer la estructura de los mismos y encaminar el análisis según la naturaleza de los datos.

Visualization Data Analyst requires this skill. In particular, one should be able to select the most direct illustrative data representations to pass a clear message. For this purpose, histograms and charts are extremely useful.

Descriptive statistics are essential in data science for several reasons. They involve summarizing, organizing, and presenting data in a meaningful and concise manner, which helps in understanding the main features and characteristics of a dataset, identifying patterns and trends, and gaining insights from the data . Descriptive statistics provide a foundation for further analysis, decision-making, and communication of findings . They are used to summarize complex quantitative data through tabulated and graphical descriptions, making it easier to interpret the data . In data science, descriptive statistics are crucial for gaining initial insights into the data before proceeding to more advanced analysis techniques

To prepare for an interview about statistics, knowledge of descriptive statistics is essential, such as: measures of central tendency and measures of dispersion. For data science, the first step is exploratory data analysis, in addition to identifying and treating outliers. Basic concepts of probability are also fundamental, it is necessary to have knowledge of probability distribution (normal, etc.), statistical inference (hypothesis testing, confidence interval) and going a little deeper, statistical modeling with knowledge of linear, multiple regression , analysis of variance, dimensionality reduction (PCA). In general, these concepts are very important to prepare for an interview that will cover statistics.

Descriptive statistics involve summarizing and presenting key features of a dataset. Measures like the mean (average), median (middle value), and mode (most frequent value) offer insights into the dataset's central tendency. Measures of variability, such as range, variance, and standard deviation, provide information about data dispersion. Additional descriptors like quartiles and interquartile range help understand the spread. Descriptive statistics serve as a foundation for data exploration, facilitating a clear understanding of the dataset's distribution and characteristics. They play a crucial role in summarizing large amounts of data, making it manageable and interpretable for analysis and decision-making processes.

Basic descriptive statistics, normal probability curve and inferential statistics related to hypothesis testing, parametric and non parametric tests, type-I and type -II errors

Descriptive Statistics: Measures of central tendency: Mean, median, mode, and their interpretations in different contexts. Measures of variability: Range, variance, standard deviation, and their applications. Data visualization: Histograms, box plots, scatter plots, and their use in understanding data distributions and relationships.

Important Concepts: Random sampling: Ensures your sample is representative of the population and avoids bias. Hypothesis testing: We propose a hypothesis (e.g., there is no difference between two groups) and use the data to test if it can be rejected. P-value: The probability of observing the data we did, assuming the null hypothesis is true. Lower p-values provide stronger evidence against the null hypothesis. Statistical power: The chance of correctly rejecting a false null hypothesis (avoiding Type II error).

Inferential statistics involves concluding a population based on a sample of data. It includes hypothesis testing, where statistical tests assess whether observed differences are significant or due to chance. Confidence intervals provide a range of plausible values for population parameters. p-values indicate the probability of obtaining observed results by random chance. Understanding and controlling for Type I and Type II errors is crucial. Inferential statistics bridges the gap between sample data and broader population insights, aiding decision-making and generalization of findings beyond the specific sample. It plays a pivotal role in scientific research, business analytics, and various fields where generalizing from data is essential.

Understanding of P value in regression is very important. Other tests like t tests , Anova are often asked in interviews. Having a hold over central limit theoram and normal distribution are also essential.

Inferential statistics is an important aspect of data science as it involves making inferences and predictions about a larger population based on a sample of data . It helps in drawing conclusions from the sample and generalizing it to the population, comparing two models to find which one is more statistically significant, and feature selection . Inferential statistics involves probability, distributions, and hypothesis testing, which are crucial for analyzing data effectively . It is used to determine if a sample selected is statistically significant to the whole population or not

Inferential Statistics: Hypothesis testing: Null and alternative hypotheses, p-values, statistical significance, and interpreting results. Confidence intervals: Estimating population parameters with a margin of error. Regression analysis: Linear regression, understanding coefficients, R-squared, and its limitations.

No strategy survives the first encounter with the enemy. The variety of hypotheses and tests in inferential statistics is wide and confusing; and there is no single test oder parameter that suits of models. Thus, 1. make sure you understand your data and where the data come from, 2. understand what information you want to win by applying statistics, 3. make sure that you understand the method you are going to apply on your data, 4. make sure that the method suits your data and your analytics goal, 5. find a parameter that quantifies the quality of fitting the model to your data depending on your goal.

Probability and distributions are fundamental in statistics. Probability assesses the likelihood of events occurring, ranging from 0 (impossible) to 1 (certain). Probability distributions describe how probabilities are distributed across possible outcomes. Common distributions include the uniform, normal (bell-shaped), binomial (two outcomes), and Poisson (rare events) distributions. These models characterize random variables, enabling the quantification of uncertainty and prediction of outcomes. Probability and distributions underpin statistical inference, hypothesis testing, and various analytical methods, serving as foundational concepts in data analysis and decision-making.

Probability and distributions are fundamental in data science for several reasons. They provide the basis for understanding uncertainty and randomness in data, which is essential for making informed decisions and predictions . Understanding probability allows data scientists to assess the likelihood of events and outcomes, which is crucial for various statistical and machine learning techniques . Moreover, knowledge of different probability distributions, such as normal, uniform, and binomial distributions, is essential for modeling and analyzing different types of data . In data science, probability and distributions play a key role in tasks such as data analysis, feature selection, and algorithm training

Probability and Distributions: Basic probability concepts: Events, probabilities, conditional probability, Bayes' theorem. Common probability distributions: Normal, binomial, Poisson, and their applications to real-world scenarios. Central Limit Theorem: Understanding how sample means approach the population mean with increasing sample size.

Sampling involves selecting a subset of data from a larger population for analysis. Proper sampling techniques, like simple random or stratified sampling, ensure representative samples. Estimation uses sample data to make inferences about population parameters. Point estimation provides a single-value guess, while interval estimation provides a range of likely values. Confidence intervals, derived from sample statistics, quantify the precision of parameter estimates. Sampling and estimation are crucial in generalizing findings from limited samples to broader populations, enhancing the efficiency and reliability of statistical analyses.

Sampling and estimation are crucial in data science as they help in making inferences about a population based on a sample of data . Sampling techniques are used to select a representative sample from a population, which is essential for ensuring the validity and reliability of the results . Estimation involves using the sample data to estimate the parameters of the population, such as mean and variance, which is important for making predictions and drawing conclusions about the population . In data science, sampling and estimation are used in various tasks such as hypothesis testing, model selection, and algorithm training

As dataset sizes becoming increasingly huger, tests and verifications on smaller, humanly-handleable subsets become more important. For this, it's essential to extract significant representatives of the data. Taking only data around the average will not change statistics measures, but your understanding of what is behind the data as you might ignore important side-distributions. But also taking the same amount of data points from different populations may change the results if the sizes of the distributions are not the same. It's not only the numbers that count, but also relations.

Other important statistics topics to prepare for in an interview could possibly be non-parametric statistics, time series analysis, machine learning for statistics.

Experimental design encompasses planning and structuring studies to investigate hypotheses. It involves factors, levels, and randomization to control for confounding variables. Randomized Controlled Trials (RCTs) are common experimental designs. Analysis of experiments uses statistical methods, like Analysis of Variance (ANOVA) for comparing means, to assess the impact of independent variables on the dependent variable. Well-designed experiments and robust analyses enhance the validity and reliability of scientific findings, supporting evidence-based decision-making in various fields from medicine to social sciences.

There should always be two tests: the first one testing the maths, and the one testing the real life behind the data. When you have your model of the data, make sure the model is sophisticated, reproducible, and aligns with new data, too. Then test it with respect to the origin of the data. E.g., by when analysing data from a machine, pick / simulated data from a point in time when the machine was malfunctioning and see how the model reacts to it. Or with data from another season / year of the business. At the end of the data, your model doesn't exists only to please itself, but to be used for forecasting or decision-making. Thus, make sure the model is ready for it.

you need to know your stuff when it comes to the nitty gritty of implementing ideas into code. check tht you are aware of the needs of your interviewer and can respond to them in terms of your skillset in statistical coding in whatever program they work in.

This is a technical and very important aspect. You will definitely be asked to write a code snippet in R (dplyr, tidyverse, ggplot2) or Python (Pandas, NumPy, SciPy, Matplotlib) to perform EDA. Also, be prepared to create SQL queries to demonstrate not only the knowledge of formulating a basic query but also joins, summary stats, and window functions. The goal is to demonstrate a complete understanding of the schema of SQL.

There must also be detailed understanding of linear and logistic regression models, interpreting coefficients, and assessing model fit.

I would recommend revising the following tools: visualisations, data processing and statistics in in Python, specifically using Pandas and SciPy, or in R; knowledge of basic queries and how to join tables in SQL; depending on the role, knowledge of noSQL databases could be beneficial, such as MongoDB and ArangoDB. I would also advise to not be dogmatic in your thinking. For instance, different tools can be applied for creating the best type for each type of visualisation: R and Python can create more publication-ready and statistically-valid plots; Google sheets can quickly create pretty plots; Excel's plots are highly customisable; Canva, Figma, or Adobe Illustrator can create beautiful custom visualisation, such as bubble plots.

Statistical Software and Tools: Familiarity with popular statistical software: R, Python (pandas, statsmodels), Excel (basic statistical functions). Understanding the strengths and limitations of different tools: Choosing the right tool for the specific task and data type.

Use technical solutions whenever possible to save time and decrease individual derivations due to individualised definitions. Yet, keep in mind that not everybody is able to code. No matter how important python will become even more, most accountants will still write no single line of code. Sit down with your audience, understand what they need, and how you can provide them the fitting mixture of ready-to-use data and flexibility for their Self-Service. You don't need python, Java, or C++ to derive descriptive statistics.

Biostatistics: Biostatistics encompasses vital statistical methods applied in health-related research, such as evaluating the efficacy of biomarkers in diagnosing and prognosing diseases, along with conducting epidemiological studies

A strong ability to communicate, partner, think strategically and explain statistics clearly in a non-technical way is the only way you can be sure you are adding value as a statistician. Being technically strong is not enough any more yet these skills are often overlooked or not valued enough by young statisticians.

attribution: a judgement call that can never be taken over by algorithms or ai is the question: does this particular (research) question warrant a certain design and to what extent does the design answer the question(s) i pose? societies or even people do not live in the experimental setting

Data Cleaning and Manipulation: Identifying and handling missing values: Imputation techniques, outlier detection, and their impact on analysis. Data transformation: Logarithmic, scaling, and other techniques to improve normality or linearity for analysis. Basic data manipulation skills: Sorting, filtering, aggregating data using tools like Excel or Python libraries.