Top Resources For Statistics In Data Science 2022/data-science-insights/top-resources-for-statistics-in-data-science-2022

Top Resources For Statistics In Data Science 2022

Top Resources For Statistics In Data Science 2022

Every industry today is relying on an understanding of the data generated through processes, products, services, customers, and teams. To expand wide into the market space, businesses first need to work on existing products’ strengths and then penetrate the untapped market areas. The whole of industries come with a set of processes streamlined into the operational flow and other supporting departments. Working on all the data that is generated from everywhere has led to an increased demand for professionals. 

The experts need to be equipped to fulfill specific business needs. Data scientists are those professionals skilled in technical know-how. They have the aptitude for analyzing vast chunks of data that they can easily tap the problem areas and also wander into the untapped latent problem areas. The overall objective is to bring significant business results and more eminent profits in the domain. The rising demand of the industry has led to the growth of the profile of data scientists. According to a report, by 2026, there’s an intended 19% rise in the number of data scientist jobs, and about 540K new positions are about to be produced. Looking at such a promising industry, it becomes imperative for an aspirant to gauge their skillset and decide accordingly whether they’re a good fit for the industry or otherwise.

Just as any other stream of work, Data science as well requires a certain bare minimum qualification to a decree for this area of work. It’s not always necessary for professionals to have a data science background wheeling beforehand. Data scientist profiles vary with the level of expertise, educational qualification, and experience. A bachelor’s/master’s degree pursued in any of the STEM subjects proves beneficial as it lays a good foundation for the basic mathematical or statistical knowledge that’ll prove to be of utmost importance in the future.

In order to analyze the data, the imperative tool is Statistics. The concepts involved in statistics help provide insights into the data to perform quantitative analysis on it. To put it in simpler words, statistics is the basic use of mathematics in formulating a technical analysis of data. As we all know that data science is a concept to unify statistics, data analysis, and their related methods in order to understand the actual phenomena with data. Let’s delve deeper into the role Statistics for data science has to play:

    It clearly helps in predicting and classifying data whether it would be right for the clients viewing by their previous usage of data.

    Cross-validation and LOOCV techniques are inherently statistical tools that have been brought into machine learning and data analytics world for inference-based research, A/B & hypothesis testing.

    Statistics help in picking out the optimal data and weeding out the unnecessary dump of data for companies who like their work organized, alongside helping to spot out anomalies that further helps in processing the right data.

    Dashboards, charts, reports, and other data visualization types in the form of interactive and effective representations give much more powerful insights than plain data & make it more readable and interesting.

    It also allows segmenting the data according to different kinds of demographic or psychographic factors that affect its processing. It also optimizes data in accordance with minimizing risk and maximizing outputs.

Having talked about the critical role statistics plays in data science, it’s important to understand the areas of statistics that aspiring data scientists should focus on. According to Elite Data science, a data science educational platform, aspiring data scientists must understand the fundamental concepts of descriptive statistics and probability theory, which include the key concepts of probability distribution, statistical significance, hypothesis testing, and regression. Statisticians use logic and reasoning to identify the strengths and weaknesses of alternative solutions, conclusions, or approaches to problems. They use statistics, calculus, and linear algebra to develop their models and analyses.

The commendable role, possessing the knowledge of statistics plays in implementing basic data science models are reflective in the following ways:

  • It definitely helps the data scientists to design & implement experiments to inform product decisions

  • It helps in building models that predict signal, not noise

  • It turns big data into the big picture, understanding the reflexive actions of customers for your product or service

  • It gives a clear understanding of user engagement, retention, conversion & leads

  • It also enables your users & provide them with what they want

  • It also gives out intelligent predictions

  • Summarizes the data in the form of a story, making it easier to comprehend

A data science professional doesn’t simply summarize the numbers, they’re the storyteller of the company, communicating the meaning of the data & why it is important to the company. With statistics, data scientists derive insights to encourage decisions that improve products or businesses, distilling the data into actionable insights that promote the vision of the company.

Talking about online resources for statistics for data science professionals, nowadays there are more online learning options than ever, including courses that are absolutely free. Whether you want to prepare for your upcoming course or need to pick up some extra skills to help with your job, there’s an online course for everything. 

Although there are several certification programs and online courses that provide certifications in the market, there are some essential criteria that can help data scientists choose the right certification for themselves.

  • Firstly, the program curriculum - how effective, relevant, and latest is it to develop your skills

  • Secondly, the program delivery - whether it is self-paced or instructor-led. Now both are fine and it depends on your choice. If you choose instructor-led learning, ensure you are learning from prime instructors. For self-paced, ensure you get a practical-oriented approach. 

  • The course should focus more on programming languages like Python, R, etc.

  • Lastly, the evaluation systems should be credible.

There are many online certifications nowadays that an aspirant can go in for among the leading data science certifications from USDSI, MIT, Stanford, Harvard, etc. being a dynamic landscape, data science is continuously evolving and certifications are an excellent way to keep up with the competitive advantage.

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