X # Basic Statistics Concepts Data Scientists Need to Know

Prospective data scientists need to know deep learning, business intelligence, and programming languages. They also need to understand basic statistical concepts. Professionals use these concepts daily for running statistical tests, balancing sample sizes, and defining the relationship between variables.

Learning basic statistics concepts allows you to work with independent, dependent, and random variables. Below are the essential statistics concepts in data science and the top resources to learn them. Take a look at how to learn statistics for data science.

## What Is Statistics?

Statistics describes the practice of gathering, analyzing, interpreting, and preparing large amounts of numerical data. There are two major types of statistics: descriptive and inferential. Descriptive statistics summarize the features of a dataset. Inferential statistics are used to make predictions. • Career Karma matches you with top tech bootcamps
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There are also estimation statistics that are used to analyze data, translate results, and interpret frequency statistics. Frequency statistics refers to the number of times a value occurs in a dataset.

### What’s the Role of Statistics in Data Science?

Statistics is a crucial component of data science because it helps scientists perform quantitative analysis and make accurate predictions. While working with a wide variety of data, there could be uncertainties within these datasets. Statistics help solve these uncertainties.

The role of statistics in data science reveals how data is distributed and the impact of independent and dependent variables. Data scientists who utilize statistics will have a clear understanding of the relationship between variables and the likely outcome of their analysis.

### Reasons to Master Statistics

You should master statistics if you want to become a data scientist. Statistics will help you make accurate predictions and determine the outcome of your experiments. Data scientists can also solve complex problems and find actionable insights through statistics.

Mastering statistics can help data scientists stand out among their competitors and help them progress in their careers. This is because statistics are an imperative part of data science. As well, according to PayScale, data scientists with statistics skills earn \$97,030 per year.

### Statistics and Machine Learning

Statistics and machine learning are closely related. For machine learning, statistical techniques help clean and prepare data for modeling. Experts conduct hypothesis tests and use estimation statistics to choose models and present their final results.

To structure predictive modeling and interpret the data, you can use data visualizations and exploratory analysis. Both concepts rely on statistics. Overall, machine learning cannot function without statistics.

## Top Essential Statistics Concepts in Data Science

To learn statistics, you need an understanding of the essential statistics concepts listed below. These concepts are vital for any data scientist. You can master these concepts through degree programs, certificate programs, or online short courses.

### Statistical Features

Statistical features are the very first concept that data scientists use for analyzing datasets. This concept describes percentiles, mean, median, and bias, among many other statistical functions. Every data scientist uses statistical features in their processes.

Statistical features are vital knowledge for data scientists as they cover the fundamentals of statistics in a basic plot box. Using this concept, data scientists can determine the value of their variables and whether data points are similar.

### Statistical Sampling

To understand statistical sampling, we need to know what population means in statistics. The term population refers to a group of elements that experts draw statistical samples from. For example, if you’re performing an analysis on 36-year-old men in California, the population would include all 36-year-old men in California.

A sample is a subset of a population and is used to represent the larger group. Data scientists can’t analyze all 36-year-old men in California, so they will analyze a portion. Instead of having to analyze the entire population, data scientists can use samples to draw conclusions. It’s best to use random samples to avoid biased results.

### Probability Distributions

A probability distribution describes all the potential outcomes of a random variable within a determined range. The maximum and minimum values limit this range. There are various probability distributions, such as normal or Gaussian, binomial, chi-square, and exponential distributions.

This knowledge is essential for data scientists to identify probable outcomes of an experiment and eliminate variables. Probability distributions also help characterize data and solve natural and social phenomena.

### Probability Theory

Probability theory refers to the likelihood of an event occurring. You might think that probability distributions and probability are the same, but this isn’t true. While the practical knowledge of both concepts might have similarities, there are key differences.

Probability distributions link each potential outcome of an experiment with the likeliness of it happening. Probability theory is the chance of an event occurring. Data scientists must understand probability to make predictions. This concept instills confidence in their predictions and gives them an indication of how their experiments may turn out.

### Bayesian Statistics

Bayesian statistics is a unique approach to applying probability to statistics. Data scientists use Bayesian statistics to modify their opinion on probability after discovering new information. Data scientists need to know Bayesian statistics because they are part of an environment that is constantly changing.

Sometimes, data scientists are confident about their analysis until more data comes to light. Here, they can use mathematical tools from Bayesian statistics to combine their prior beliefs with recent evidence. The result of this is called posterior beliefs.

### Over and Under Sampling

Over and under sampling are used to balance unequal data classes. For instance, if class one has 200 samples, but class two only has 50 samples, the classes are not balanced. These data classifications are too imbalanced and could cause issues with predictive modeling.

To balance both classes, you could use oversampling, which means duplicating the samples from class two until its size is equal to class one. Alternatively, you can utilize undersampling, which means you only use some samples from the majority class to balance with the minority. Balanced sample sizes are important to data science.

### Dimensionality Reduction

Dimensionality reduction is the practice of reducing the number of random variables in a dataset. The basis of dimensionality reduction relies on feature selection and feature extraction. Without dimensionality reduction, predictive modeling becomes a much more complicated task.

This concept is important for all data scientists to make the analysis process quicker and easier. Some machine learning algorithms don’t perform well with large dimensions, so dimensionality reduction resolves this.

### Central Tendency

Central tendency is a term that refers to a single value that describes the middle position in a dataset. You might know these popular central tendency terms: mode, median, and mean. These terms are valid measures of central tendency, but the term you use depends on the dataset.

Using this concept, data scientists can get an instant idea of how the dataset looks. Data scientists can quickly determine where a majority of data falls and how this impacts probability.

## How to Learn Statistics for Data Science

Gaining practical knowledge in statistics involves learning tons of complex functions and concepts. You can make the task easier by referring to the step-by-step guide below.

### Learn the Key Concepts for Statistics

You should become familiar with the concepts of statistics listed above. There are other key concepts for statistics that may be valuable to learn. The more fundamental concepts you understand, the faster you can master statistics.

Learn techniques like performing hypothesis tests and regression analysis basics such as linear regression and multi-linear regression. You can also learn SQL, sampling distributions, Excel, and similar tools.

### Learn Bayesian Thinking

Bayesian thinking is imperative for statistics in data science, and you need to understand all its ins and outs. It’s also a great idea to learn deep learning and how Bayesian thinking influences it. Doing this will give you a broader understanding of how Bayesian thinking works.

To practice Bayesian thinking, you can use tutorials, quizzes, and short online courses to train your knowledge. Your learning method depends on your preferences, but most students pursue a hands-on learning approach.

### Learn Markov’s Chain

A Markov chain is a model that describes a sequence of potential events where the probability of each event relies on the state of the previous event. Markov’s chains help data scientists determine highly probable results.

Learning Markov’s chains can be done by finding online tutorials, quizzes, and courses. Choosing a learning method that includes peer or instructor help for this concept can be beneficial.

### Learn Statistical Modeling and Fitting

Statistical modeling is done by applying statistical analysis to a dataset. Statistical fitting refers to the process of finding the best way to represent a data spread. Data scientists need these skills for predictive analysis and to find data trends.

It’s always best to practice these concepts rather than using theory on its own. You can choose a studying method that suits you and practice these concepts until you perfect them.

### Learn Machine Learning

Descriptive statistics and inferential statistics are critical in machine learning. Machine learning is very useful in both statistics and data science. It is a growing field and can help you get a data science job later in your journey.

Understanding the fundamentals of machine learning will grant you more comfort and confidence with statistics. You can also gain a better understanding of statistical processes and applications.

## Top Resources to Learn Statistics for Data Science

To learn statistics for data science, you can access countless resources like websites, books, courses, and tutorials. You can choose the resource that works best for you and your learning style. Below are some of the top resources to help get you started.

### All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)

This book focuses on different forms of statistics like applied statistics, frequency statistics, and estimation statistics to enhance your knowledge. Graduate and skilled undergraduate students who know calculus and linear algebra will benefit from this book.

### Listendata

You can find tutorials on statistics, data science, Excel, R programming, Python, and more concepts at Listendata. Over 40 statistics tutorials are teaching linear regression, bootstrapping, descriptive statistics, and relationships between variables. You will also find helpful infographics.

### Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (2nd Edition)

This book will teach you about exploratory data analysis and how to work with random samples, classification techniques, regression, and experimental design. You will learn Python and R programming fundamentals as well as how to use them for statistics. This book also outlines what is and isn’t important in statistics.

### Statistics.com

From this website, you can learn data science, statistics, analytics, and much more. You can find short courses, certificate programs, and degree programs for statistics. There are also helpful resources like blog articles, a glossary, and a knowledge base. This is the place to go if you want to learn statistics.

### Statistics at Square One

This website teaches you about Chi-square tests, populations, samples, central tendency, and the theory of probability. You can access resources like research papers, news, observations, and analysis. There are also job listings on this website.

## The Bottom Line

Understanding basic statistics concepts is imperative for prospective data scientists. Having practical knowledge of these concepts will help professionals become data science experts. According to the Bureau of Labor Statistics, statistics job opportunities will increase by 33 percent between 2019 and 2029. This means statistics is a growing topic.

If you want a great job in data science, you should learn statistics. You can follow our step-by-step guide and access other resources. Regardless of your level of statistical knowledge, you can learn statistics for data science.

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