Data science is a field based on computer science and programming, as well as mathematical theories and formulas. The data science industry draws on the field of statistics for concepts and theories used to create algorithms and models, and also encompasses data analytics and the development of visualizations. Beginner data scientists can benefit from broadening their knowledge of statistical concepts to better understand the data science industry and its many tools.
How Data Scientists Use Statistical Tools and Concepts
Data scientists use statistical tools and concepts in multiple ways, depending on the type of data science and the industry. Most data scientists use statistics to learn more about a dataset by extracting meaning from the data and understanding the relationship between the distinct variables within a dataset. For example, data scientists can use statistical theories, like logistic regression, to make predictions based on past data, as well as visualize those predictions through charts or graphs. Statistics are also used when examining data such as using descriptive statistics to learn more about a dataset.
Data scientists working with machine learning and artificial intelligence use statistical tools and concepts to develop advanced models and diagrams. Most machine learning models are based on statistical theories modeled through graphing the function or formula underlying a specific theorem. These models can then be used to train a machine to make decisions based on the findings from that dataset. So, understanding the theorems behind machine learning models is essential.
Statistical tools are even more important for data scientists who primarily utilize quantitative research and numerical data, particularly in business and finance, academic institutions and government, as well as economics. In these fields, statistical tools are essential to understanding how to trace the significance of past and present data. Interviewing for a data science job usually includes being tested on statistical concepts, because understanding these concepts, as well as statistical tools, is an important aspect of analyzing and visualizing a dataset, and is a requirement for many data science careers.
Statistical Concepts for Beginner Data Scientists to Know
Of the many statistical concepts that are used within the data science industry, the following terms and definitions are essential for beginner data scientists. These terms focus on the theories and models most widely used for automation and machine learning, as well as predictive analytics and algorithms.
Descriptive Statistics and Exploratory Data Analysis
Descriptive statistics are a summary of the basic features of a dataset. This includes information about the distribution, variability, and average values within the dataset such as the mean, average, and standard deviation. While this information can be plotted on a graph for a visual understanding of how points are distributed within a dataset, many data science tools include features that will return this information through coding or calculations.
So, whether you are working with spreadsheet software, data science libraries, or even programming languages, it is common to find analytic features, like functions and formulas, within the tool that make it easier to pull up this information. Descriptive statistics are often used to convey introductory information about the data that has been collected, making this information a primary example of exploratory data analysis. These descriptive statistics can then be used to determine the type of advanced analytics needed to make an argument or prediction about the dataset.
Logistic and Linear Regression
Beyond descriptive statistics, there are several statistical formulas that can be used to provide a more in-depth analysis of the data. Regressions are the most popular, as they can be used to make predictions, as well as to chart the relationship between multiple variables. With statistical regression, only one equation is estimated and all of the X predictor variables are added at the same time to predict the usefulness of each X predictor, while statistically controlling for any linear association of a specific X variable with all other predictor variables in the equation.
While there are many types of regression models and analyses, the two most commonly used are logistic regression and linear regression. Logistic Regression can be used to test the probability of an event in a case where there is a discrete either/or outcome. For example, logistic regression can be used when there are only two options such as pass/fail grades on a test or win/lose for the outcome of a game. In contrast, linear regression measures a linear relationship between an X predictor variable and a y outcome variable. Linear regression is generally plotted on a graph using the equation, “y = ax + b, ” and the probability of an outcome is calculated based on how well the line of best fit expresses the relationship between the data points.
Bayes Theorem, Probability, and P-values
When working with these more complex analytical theories and formulas, it is also important to understand how to interpret the information that is produced. One method of understanding your data output and its significance is through knowledge of the Bayes Theorem, probability, and the importance of p-values. Probability measures the likelihood of an event or outcome, and it is common for data scientists and researchers to use statistical theories to understand probability and make predictions.
Bayes Theorem is a formula used to find the probability of different scenarios under a certain set of conditions to make a prediction about the probability of a specific outcome. In addition to Bayes Theorem, p-values also act as a measure of probability or likelihood within the study of statistics and data science. In addition to determining the probability of a particular event, P-values between 0 and 1 are also used as a measure of statistical significance. Through probability and statistical significance, data scientists can determine whether or not a hypothesis is likely to occur or is verified by the data.
Sampling Distribution and Errors
When determining the significance of the data that has been collected it is also important to understand the different types of sampling. Sampling is an essential part of data collection and analysis because it is the process of selecting a representative sample of the dataset to analyze. When working with big data, in particular, sampling helps to break down the dataset so that it is easier to understand and manipulate. With that being said, there are multiple rules which correspond to appropriately selecting a sample from a dataset.
For example, a sample can be randomly selected from a dataset through a form of probability sampling or through an algorithm or computer model. In addition, a sample can be selected through non-probability sampling, in which you analyze the distribution of data to target a particular subset of the data. Generally, it is better to randomly gather a larger sample set to ensure that the data being collected can reach statistical significance and to catch sampling errors and mistakes. One of the most common sampling errors is selecting a subset of the population that is not representative of the whole, leading to miscalculations, bias, and assumptions about the larger dataset.
Need to Know More About Statistical Analysis and Modeling?
As a foundation of both data science and analytics, numerous statistical concepts and theories are relevant to beginners in the field. Anyone beginning a career as a Data Scientist or analyst should learn more about statistical analysis and modeling. Many of Noble Desktop’s Data Science classes include beginner-friendly certificate programs and bootcamps that cover statistical tools and algorithms. For example, the Data Analytics Certificate program offers instruction in statistical analysis, with a focus on predictive and prescriptive analytics. The Python for Data Science and Machine Learning Bootcamp focuses on turning knowledge of statistical analysis and modeling into a deeper understanding of algorithms and machine learning.