Understanding Statistics in Analytics Key to Progressing Forward in Data Science

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If you are pursuing a data science course, you will come across tons of resources on statistics in analytics. In the modern data science era, it is impossible to think of any project that doesn’t have a statistical analysis for analytics and intelligence.

Statistics has been a key subject in mathematics for many centuries now. In the last 100 years, a lot of marketing and business intelligence tool has taken up statistics as the mainstay in their operations. Even before that, various groups of professionals relied on statistics for understanding various relationships between constants, variables, and outliers. However, the real power of statistics could be fathomed only during the Internet Era when computer programmers began to utilize the various techniques in the subject for the development of advanced analytics, including the very famous Predictive Analytics tools.

 So, what really is the connection between Statistics and Analytics, and how is data science gets influenced by the adoption of statistics in analytics? We will try to answer this correlation and the future of analytics based on statistics in this article.

Learning basics statistics is very important

Ask any data scientist how they progressed ahead in the field – they are most likely to tell you their initial experience with statistics and basic algorithms that trained hard on this topic. It is absolutely non-negotiable for any trainer in data science to skip analytics based on statistical techniques.

What type of statistical technique should I start learning first?

We will try to be very focused on this part of the discussion hereon. Statistics is built upward from data mining and collection operations, that require multiple stakeholders and participants to collect, organize, analyze, experiment, and explore trends and patterns that could be relevant to either solving a particular problem at hand, or for deciding the futuristic trends that could give rise to new kinds of problems. Any analytical statistics explores the 5 Ws and 1 H clusters — Why, Who, What, When, Where, and How! Since businesses are most interested in the What and Why part of the cluster, we require highly trained data science platforms that can stay focused on the risky trends and anomalies associated with them.

If you are new to statistics and unable to link and establish a relationship with analytics, you can start with basics in stats.

The different statistical analysis techniques are:

Descriptive analysis: Descriptive analysis is a quantitative approach toward summarizing data types for describing a particular behavior, pattern, or event. It is the most basic type of data analysis that enables you to practically analyze any data in a quantitative setup. When you work with large volumes and varieties of data, you have a better success rate and convenience if you deploy statistical tools with the descriptive framework.

Familiar terms in this space include Mean, Mode, Median, Standard Deviation, and Quartiles / percentiles, and kurtosis, and skewness.

Advanced statistics in analytics classes would require a solid understanding of concepts related to Univariate analysis, bivariate and multivariate analysis.

Inferential analysis: This is the branch of statistics that would allow an analyst to derive conclusions or inferences from a stated group of data samples and population. Basically, you can extrapolate on any sample group and expand the results to find an answer to bigger problems. Population census, environmental crisis, and computer software designing effectiveness are all derived using inferential analysis. I am particularly fond of the inferential analysis as it allows us to come up with a reasonable outcome, even while accounting for sampling errors. This is explained very well during the analysis of Point Estimates, and Interval Estimates.

Prescriptive Analysis versus Predictive

The future of business analytics lies within the popular context of predictive versus prescriptive analysis. Efforts are being made using AI and machine learning techniques to differentiate “forecasting” outcomes (predictive) and associate the results with manageable outcomes that can be supervised by a manager (prescriptive). Google’s route mapping and Uber’s fare predictive algorithms are two wonderful examples of prescriptive and predictive models, respectively.

There are a lot of other analysis techniques influencing the role of statistics in analysis, however, nothing comes close to descriptive, inferential, and predictive / prescriptive analysis today.

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