Statistics is a branch of mathematics that involves studying and manipulating data in order to represent specific characteristics. The data used can be quantitative or qualitative. Statistics provides users with a means for collecting, reviewing, and analyzing data, as well as a way to draw conclusions from this data and ultimately make better business decisions.
Not only is Statistics its own branch of mathematics, but it also plays an important role in the process of data analytics. Applying statistical methods to data analytics is a vital process, one that fuels new discoveries, informs the decision-making process, and helps predict what is to come.
This article will explore how Statistics has historically been used in data analytics, and the many benefits it provides those currently working with data.
A Brief History of How Statistics has been Used in Data Analytics
For the past three-and-a-half centuries, statistics has played an integral role in the field of data analytics. In 1663, the first statistical data analysis experiment was completed in London by John Graunt. Graunt kept a record of information pertaining to mortality and hypothesized that he could create a warning system for the early detection of the Plague. Over two hundred years later, in 1865, the phrase “business intelligence” was coined by Richard Millar Devens. Then, in 1880, the Hollerith Tabulating Machine was invented. It helped to streamline the US Census Bureau’s decade-long backlog of data that had to be processed. This machine used punch cards and reduced a decade of number crunching into just several months.
How is Statistics Currently Used in Data Analytics?
Statistics continue to play a fundamental role in data science and data analytics. Here are just a few of the many ways this branch of mathematics is helping Data Analysts work with big data:
- Testing hypotheses: A key aspect of the analytics process is testing hypotheses. A hypothesis test is designed to evaluate two mutually exclusive statements pertaining to a particular population. This test is a helpful tool for evaluating how statistically significant a finding is.
- Creating probability distributions & estimation: Applying statistical methods to data aids in creating probability distribution and estimation, which in turn can help create a better understanding of logistic regressions and machine learning.
- Informing business intelligence: Statistics is often used for various business processes to provide a level of confidence in results that can then be used for forecasts and predictions.
- Creating learning algorithms: Algorithms such as naive Bayes and logistic regression have evolved to meet the needs of data analysis.
- Aiding with prediction & classification: Statistics is a powerful tool that can be used for data prediction and classification.
- Incorporating descriptive statistics: The use of descriptive statistics offers descriptions and summaries of data, in addition to visualization options that allow the insights to be presented to a non-technical audience in an easy-to-understand manner.
- Determining probability: There are many uses for statistical formulas pertaining to probability. Some examples are political polling, clinical trials, actuarial charts, and even determining how likely a population is to encounter a disease.
Benefits of Using Statistics in Data Analytics
When working with data analytics or data science, statistics provide a foundation, offering basic concepts and building blocks that are necessary to understand before a Data Analyst can work with more advanced algorithms. In addition, visual representations of data are a helpful tool for spotting patterns and outliers, as well as metrics like median, mean, and variance.
There are many benefits to using statistics for data analysis, such as:
- It provides a way to classify and organize information. Classification allows users to categorize and organize data into accurate and observable analyses. This is an important first step for businesses that will apply these insights into their business plans.
- Statistics can find structure in data. When a company applies statistical analysis on a massive set of data, it can identify anomalies as well as trends. The company can then discard any irrelevant data early on rather than wasting resources and time down the road.
- It provides important insights into business operations.
- Statistics aids in data processing.
- It can be used to spot clusters in data, as well as other structures that depend on variables like time or space.
- Statistical methods allow users to calculate probability distribution.
- It enables statistical modeling using graphs and networks.
- Statistics plays an important role in data visualization. Visual representations of data in data analytics provide a way to visualize numbers so that trends and patterns in quantitative data can be spotted. These visualizations use the same display formats that statistics uses, such as histograms, pie charts, and graphs. These visualizations present data in a readable and interesting manner that makes it easier to notice flaws or trends.
Hands-On Data Analytics & Data Science Classes
Learning more about statistics is an important skill-set for all Data Analysts. data science classes provide an effective and engaging way to study how Statistics is used when working with big data. Courses are available in-person in New York City, as well as in the live online format in topics like Python and machine learning. Noble also has data analytics courses for those with no prior programming experience. These hands-on classes are taught by top Data Analysts and focus on topics like Excel, SQL, Python, and data analytics.
Those who are committed to learning in an intensive educational environment can enroll in a data science bootcamp. Industry experts teach these courses, which provide timely, small-class instruction. Over 40 bootcamp options are available for beginners, intermediate, and advanced students looking to learn more about data mining, data science, SQL, or FinTech. Noble Desktop’s Data Science Certificate spans 84 hours and provides learners with lessons in machine learning to apply statistical analysis and regressions to predictive models.
For those searching for a data science class nearby, Noble’s Data Science Classes Near Me tool makes it easy to locate and learn more about the nearly 100 courses currently offered in the in-person and live online formats. Class lengths vary from 18 hours to 72 weeks and cost $915-$27,500. This tool allows users to find and compare classes to select the one that’s the best fit for their learning needs.