Data Science (The MIT Press Essential Knowledge) PDF 2023.

 Data Science (The MIT Press Essential Knowledge) PDF 2023.

What is a Data Scientist?

The data scientist profession requires a range of technical and domain-related skills to manage and analyze data to solve business problems.

 The data scientist is partly a mathematician, partly a business analyst and partly a computer scientist. A good data scientist is able to detect trends and patterns in the data and knows how to use them for useful and actionable results. 

Data scientists are at the forefront of modern business, transforming the way we work. Data Scientist Skills Diagram: History of Data Scientists: In 2001, a computer scientist, William S. Cleveland, wrote an article entitled “Data Science: An Action Plan for Expanding the Technical Area of Statistics”. 

This article presented Data Science as a discipline of applied statisticians. That was only 20 years ago, and the world of technology and business has evolved rapidly since then. As this is a relatively new career path, current data scientists come from a wide variety of backgrounds and backgrounds. Many start their careers as statisticians, mathematicians or data scientists. 

But as access to computers, artificial intelligence (AI) and data learning tools has become commonplace, the role has evolved. 

A Data Scientist is no longer confined to the IT department; it is now an integral part of the entire company. Due to its expansion and crucial influence on the company, the role of the Data Scientist requires a logical and innovative person, able to translate data information into business strategy.

 What is qualifications to become in Data Scientist? In the last ten years, higher education institutions have developed specific courses for data scientists. Those interested in working in this field can obtain a bachelor’s or master’s degree in data science from a large number of universities. 

 The courses taken by Data Scientists generally cover statistical modeling, data management, data visualization, machine learning, software engineering, data ethics, research design and user experience. They can learn SQL, Python, Perl and a series of other programming languages such as R. 

They will get familiar with Hadoop, Pig, Spark, Hive and MapReduce. However, with the availability of more open-source software and commercialized data science tools, what people learn today could soon become obsolete. Therefore, data scientists need to be agile and continue to develop new skills and techniques within the sector.

Data scientists need more than just a diploma:

An excellent data scientist must be curious, always looking for new information and thinking about new ways to confront the challenges of the company. Being very intuitive and accustomed to demanding evidence are also excellent qualities for a data scientist. It must be creative enough to find answers where there are none, by continually searching for ideas and results. Data scientists must also have a thorough knowledge of the field of activity. Knowing the data and the programming is one thing, having the insight to create a business strategy from that information is another. They must be able to identify the risks and opportunities of the company and use this data to propose growth strategies. It’s one thing to know that people buy more when it’s a while, but how can a company take advantage of this type of information? The role of the data scientist is to understand and answer these kinds of questions, and this continually pushes the company to new heights. An excellent data scientist must also have excellent communication skills: to be able to report to stakeholders and managers and clearly explain the results of analyses; to be able to explain where incomplete data are located, and what needs to be done to solve them; to convince and persuade of the best course of action according to these results. New programs and techniques will evolve, but the ability to think critically and have good quantitative and domain-specific skills will always be in demand. What does a data scientist do? A data scientist takes data, develops hypotheses and inferences, and then uses machine learning to detect patterns, relationships and trends in that data.

Each day it can:

Analyze data sets. Clean up data. Creating dashboards and reports View data. Make statistical inferences. Develop statistical learning models. Create complex predictive models. Use statistical tools. in order to communicate to the interests concerned and accompany them the results of the analysis of the data. Convince decision makers. Large retail companies can produce up to 40 petabytes of data every day. Their data scientists use this data to predict a range of outcomes, including when and where people buy certain articles. This allows them to plan events and sales to maximize sales, setting prices so they can make maximum profit, but also to sell off the largest amount of inventory. Data scientists usually work as a team to exploit big data in search of relevant information. They can also advise management on the type of data to be collected, how it should be analysed and the results of this interpretation. A 2017 study showed that 80% of a data scientist’s time is spent on data management: finding data, cleaning it up and organizing it. So they only have 20% of their time left to actually do the analysis. However, even that is changing: with the advent of machine learning and deep learning, data scientists are finding that they have more time for analysis, as these tools have become more automated and have supported much of the data clean-up and organization, giving data scientists more time to analyze. Why do data scientists have an important role to play? For a company, a data scientist is invaluable. It takes millions or even billions of data points and turns them into crucial information to make predictions about an organization that could either save or grow a business.

Here are some examples of the role of data scientists by sector of activity:

Data scientists are a crucial part of marketing. For example, a data scientist can produce a set of triggers that alert the company that its customers pose a high risk of unsubscribing. In marketing, it is well known that the cost of finding a new customer far exceeds the cost of retaining an existing customer. The triggers set up by the data scientist allow the company to intervene and make changes or talk to the customer to build loyalty. Health: This is a huge field offering massive opportunities for data scientists. Whether it’s managing lists and staffing at optimal levels or identifying patients who are at high risk of not following their doctor’s orders, a data scientist can find many opportunities, in order to improve the behaviors practised on trade especially health outcomes. . Detection of fraud: The insurance and banking sectors save billions of dollars every year by using data scientists to identify fraud risks. For example, when a client applies for a loan, a number of data points are collected about them. This information is processed and compared with known information on previous fraud cases. Almost immediately, the system can indicate if that person is a risk. How to become a data scientist: If you have a logical brain, you know how to handle numbers, you like working with computers and you have a good understanding of the business world, the data scientist position can be the job of your dreams. The first step is to obtain a bachelor’s degree in computer science, statistics or a related field. This degree will allow you to acquire skills in the following areas: Mathematics, especially statistics The coding Databases, data lakes and distributed storage Data cleaning techniques Data visualization and reporting skills A bachelor’s degree gives you some basic knowledge, but as the field develops, other qualifications or specializations will be required. Consider a Master’s degree in Data Science or a related field, and start exploring a specific business area that interests you. Once the qualifications have been obtained, the next step is to gain experience in the field you are interested in. Healthcare, marketing, government or business all offer excellent opportunities for specialization. While the skills of data scientists can be taught, understanding the relationships between data and real-life implications requires experience and time spent in the company.

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