064 Statistics for data science

Technological advancements have helped Data Science grow from the cleaning of datasets and applying statistical approaches to areas that include data processing, predictive analytics, data mining, business intelligence, artificial intelligence, deep learning, and so much more. The horizon is expanding. Before starting a career in Data Science, many have confusion, whether Statistics is essential in a career in Data Science? The answer is an astounding yes. One thing you need to know is Statistics before you kickstart your Data Science journey. You are responsible for managing your organization’s raw data when you land in your first Data Science job, cleaning it and finding insights from them. You will be mistaken if you think it is going to be the same as any other programming job or assignment. One of the careers that have begun to emerge in recent years is Data Science. In reality, the most essential and fundamental knowledge required to assume the role of a data scientist is Statistics.

The art of unravelling secret insights from data sets is through Statistics. It is a broad field with applications in various areas. Statistics include gathering, evaluating, interpreting and, eventually, presenting and arranging information. Probability distributions, hypothesis testing, statistical significance, and regression are some of the statistical subjects for a Data Science career. Furthermore, understanding of Bayesian Reasoning is also crucial. One needs to have a strong sense of core statistical principles such as descriptive statistics, distributions, hypothesis testing, and regression as an aspiring data scientist including Bayesian Reasoning, which involves conditional probability, priors, posteriors, and maximum probability. You can either use the intuitive method of making a choice based on your gut feeling or choose the other quantitative methodology based on heaps of “data or information.” Of course, when it comes to making a crucial business decision, the former approach also works well. Still, business owners prefer the rational and scientific way of evaluating the data and finding solutions. By this time, you would probably have begun to think as to how Data Science and Statistics are related? A data scientist is the one who, using a mathematical approach, juggles the raw data and uses his/her programming skills to work on the latest tools and techniques to derive foresight and solutions that can help business owners make critical decisions smartly. For these reasons, we may claim that Statistics plays a significant role in the role of data scientists (1) To frame more substantial questions that allow you to manage data resources efficiently (2) To improve prediction and estimation methods as well as calculate the degree of certainty of events (3) To focus on results from various data tools that can be replicated (4) To accumulate comprehension using mathematical techniques (5) To identify measures that can cause improvements in outcomes. If you want to shine in your Data Science career, it is very, very important to have sound statistical knowledge. And Statistics is a subject that you can learn even if you have never had any formal training for it.

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