Review the article below and describe the area of statistics that best serve your career or goal, Why? MBA

Learning statistics is essential for pursuing a career in data science or analytics. Data scientists and analysts use statistics to uncover the meaning behind data. A spreadsheet with millions of customer characteristics is just a bunch of numbers and can be overwhelming – but when you translate the data into key findings, the information can unveil trends and inform decisions.

“Statistics is the art and science of learning with data,” says Michael Posner, associate professor of statistics and director of the Center for Statistics Education at Villanova University. “It is about using data to inform decision-making or to gain knowledge.”

The good news is that you don’t need to enroll in a university to learn basic statistics. Many free online tools teach statistics concepts so you can prepare for a career in data science or analytics. This guide will help you get started.

Why Learn Statistics? Statistics is essential in data science and analytics professions. “Someone without strong statistical thinking skills will conduct analyses without full consideration of what is most appropriate in a given situation, often getting the right answer to the wrong question,” Posner says.

It helps data scientists and analysts tell the story behind the data. “Statistics can take the collected, cleaned, sorted and summarized data that analytics gives us and help us push it a bit further,” says Phong Le, associate professor of mathematics at Goucher College in Maryland who teaches classes in Goucher’s integrative data analytics major.

In her role as a data scientist at the research firm Valkyrie in Austin, Texas, Keatra Nesbitt relies on statistics to help clients understand data so they can make important business decisions.

“Because of statistics, I’ve been able to analyze financial data at a university, improve a high school’s state-mandated math test scores from a 54% pass rate to over 90%, rebuke a company’s misconceptions about its employees and identify a successful brand strategy for a large corporation to outperform other brands,” she says. “No matter the type of problem you are presented with, being a statistician gives you the critical thinking skills necessary to approach the issue.”

Statistics and Data Science

“Data science is the combination of statistics and computer science,” Nesbitt says, adding that statistics is a core component to pursuing a career in data science.

By using statistics, data scientists can gather raw data and make conclusions about what those numbers mean. Statistics also helps them weed out data, separating meaningful information from superfluous data.

“When analyzing features in the dataset, I can test if the sample differences are statistically significant,” Nesbitt says. “This may change the design or type of input features used in the model.”

What’s the difference between statistics and data science? Phong says that in practice, data science is “the gas pedal, finding patterns and creating dramatic summaries and visualizations,” while statistics is the brake pedal, “reminding us that not everything data-driven is generalizable and what worked before may not work in the future.”

Statistics and Machine Learning

“The field of machine learning has borrowed several concepts from statistics and built new algorithms and tools on top of them while also incorporating theory from other mathematical fields, such as linear algebra, calculus and discrete mathematics,” says Vangelis Metsis, assistant professor in Texas State University’s computer science department.

While statistics is the process of understanding relationships between dependent and independent variables, Metsis says machine learning is about applying the data to make accurate predictions, even if that relationship is not fully understood.

Statistics helps experts understand why machine learning models behave the way they do, Metsis adds. It allows users to interpret the increasingly complex models used in machine learning.

Statistics and Its Use with Data and Analytics

Statistics is widely used in business. Business analysts use statistics to analyze data so managers can make decisions. For example, analysts might study data related to business performance and use it to predict possible outcomes, allowing a company to plan for the future.

Business analysts aren’t the only ones who should understand data. Even if you are not responsible for overseeing spreadsheets, coding or collecting data, “you need to know precisely how good data can enhance your decision-making and build your perspective,” Le says.

Descriptive statistics helps you analyze and present data in a way that can be easily interpreted. It describes the characteristics of a given dataset using the core concepts outlined above.

“Descriptive statistics reveal a lot about the data, but are simple to calculate and don’t require much skill or computing power,” Posner says.

Instead of presenting a long list of numbers, descriptive statistics allows analysts to determine the mean, median and standard deviation, so they can better understand how data is distributed. Because of this, descriptive statistics allows data scientists and other analysts to better interpret the numbers.

Descriptive statistics also helps with data visualization. “Not only do we calculate summary measures ... but we look at graphical displays that give you the entire distribution of data,” Posner says. “This not only shows you the shape and location of the data, but also whether there are outliers that are different from the rest of the data or other interesting characteristics of the data.”

Descriptive statistics uses measures of central tendency, such as mean and median, to describe the center of the dataset and measures of variability, such as standard deviation, minimum and maximum. Measures of variability are used to describe the spread of the data.

What descriptive statistics does not do is allow you to generalize where the data sample came from, Metsis says. “For example, a basketball team may want to use descriptive statistics to understand the performance of their players and make improvements to their training practices but (does not) attempt to extrapolate those findings to the whole league.”

Since machine learning uses data to make predictions rather than to understand a given dataset, this and similar fields like data science are more closely related to inferential statistics, Metsis says.

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