What is the Difference Between Data Science & Statistics?
While statisticians collect, analyze, and interpret quantitative and qualitative data, data scientists must be able to use multiple disciplines to think beyond simple exploratory analysis. In other words, statistics uses mathematics to create and provide an overview of data and is a fundamental tool of data scientists. Data science uses the scientific method to extract, evaluate, and visualize structured and unstructured data as well as report on their findings.
Skills for Statistics and Data Science
Mathematics and statistics are necessary to perform data science. Towards Data Science lists statistics and probability, multivariable calculus, and linear algebra and optimization methods as among the most essential skills for data science.
Statistical features, including the mean, mode, and bias, are frequently the first methods data scientist learn in order to explore data. Every data scientist should also know the statistics concepts of descriptive statistics (measures of frequency, measures of central tendency, measures of dispersion or variation, and measures of position) and probability theory (mathematical formulas used to measure the likelihood of a certain event occurring).
Other statistics commonly used in data science include probability distribution, dimensionality reduction, over- and under-sampling, and Bayesian analysis.
In addition, Data Science Central currently lists Python, R, SAS, and SQL as the most dominant programming languages for both statistics and data science.
Comparison Between Data Science and Statistics
Data science uses interdisciplinary scientific methods to interpret data and extract insight from data (structured or unstructured) to facilitate decision making. Statistics provide methods to collect data, design experiments, and perform mathematical analysis on a given set of data.
Data science uses a variety of tools, such as machine learning, advanced mathematics, and scientific methods to sift through and organize vast amounts of data into proper sets or models. Statistics apply a specific set of tools, such as variance analysis, mean, median, and frequency analysis, to measure an attribute or determine values for a particular question.
Both data science and statistics support decision making but data science uses scientific methods to discover and understand patterns, performance, and trends, while statistics focuses on mathematical formulas, models, and concepts to provide data analysis.
Raw Data vs Data Mining
Data is comprised of raw information and data scientists utilize mathematics, statistical formulas, and computer algorithms to mine the data and identify patterns or trends within a data set. Data scientists also apply their knowledge of social sciences and a specific industry (such as health or business) to interpret the meaning of the identified patterns to provide valuable insight for an organization into real-world situations.
Real-World Examples of Statistics in Data Science
Using statistics such as experimental design, hypothesis tests, and confidence intervals, a data scientist can design and interpret experiments to provide insights for product decisions. For example, a boutique hotel wants to test the effectiveness of a new marketing campaign. A data scientist determines a balance between experimental and control groups, sample sizes, and how to run an A/B study. A data scientist can also interpret the data to help the hotel management decide if the difference in the results is significant enough to require additional study or investment.
Regression, classification, time series analysis, and causal analysis statistics are used by data scientists to provide potential reasons to a business owner why sales have increased or decreased in a certain month. This knowledge will help a business owner understand what is driving sales, forecast future sales, and predict potential future trends.
To turn big data into the big picture, data scientists use clustering, Dimensionality reduction, and latent variable analysis statistics to help grocery stores label and group their customers to help determine and understand customer buying habits. By creating groups like senior citizens, family-focused, and single millennials, grocery stores’ management can create more targeted marketing campaigns.
In order to understand user engagement, conversion, retention, and leads, data scientists may use regression, causal effects analysis, latent variable analysis, and survey design. Predictive modeling, latent variable analysis, dimensionality reduction, collaborative filtering, and clustering statistics are applied to suggest what online users want next based upon their digital interactions (purchases, reviews, clicks) with a specific website.
And using Bayesian data analysis, data scientists can intelligently estimate outcomes for web data, such as click-through rate. By incorporating data, global data, and prior knowledge, data scientists will be able to determine a desirable estimate, communicate the properties of that estimate, and provide a summary of what the estimate means.
Interestingly, one of the largest software companies in the world, Oracle, still uses a 2001 definition by Gartner as a go-to explanation of big data. Gartner defined big data as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
In other words, big data is comprised of complex data sets that are so voluminous traditional data processing software is unable to manage them. This is where a skilled data scientist comes into play, applying multiple disciplines and scientific methods to massive volumes of data to address questions and problems that no one was previously able to tackle.
Big Data into Big Picture as a Key Distinctive
The ability to turn big data into the big picture is a key factor in distinguishing a data scientist from a statistician. While there is no doubt data science and statistics are closely linked, statistics is only one of many components and methods of data science. Both disciplines will continue to exist within the job market for the foreseeable future and will likely continue to have a big overlap in skills.
Data scientists use statistics, among other disciplines, to tell a story with the data, explaining their insights in a way that is easily understood without sacrificing the integrity of the data. Strong communication skills are vital because the role of a data scientist is to be a translator or ambassador between the data and a company or client, conveying the meaning of the data and what actionable insights make the data important to the company.
If you have an interest in learning how to mine large sets of data for useful information, as well as an ability to use statistics, computer programming, and information technology, data science might be the right career path for you. In an ever-evolving field, data scientists are in demand across multiple industries, including health care, science, business, and finance.