Who is a Data Scientist

Who is a Data Scientist?

In the field of IT, a data scientist is a professional who is responsible for the collection, examining, and interpreting a huge number of the collected data. The main role of the data scientist involved in an offshoot of various old-fashioned technical roles that involves other professionals like scientists, mathematicians, computer professionals, and statisticians. This crux of the job of a data scientist requires the use of advanced analytics technologies, like predictive modeling and machine learning.

History of Data Science

It is equally important to know about the history of data science to understand about data science. Data science is a branch of computer science, and the term was first coined by the pioneer in computer science Peter Naur in the year 1960. In his book, the Concise Survey of Computer Methods, Peter Naur describes the foundational aspects of the approaches and techniques of data science.

What Does a Data Scientist Do?

The complexity of a data scientist involves taking up various major roles throughout their career, such as a data analyst, software engineer, trouble-shooter, business communicator, data miner, manager, and of course, a key stakeholder to help in decision-making at higher levels. The other roles that a data scientist have to play is that of a computer scientist, an analyst, a mathematician, and a trend-spotter. It is also important to note that this profession should work extensively in the field of Big Data, as they have to get some valuable business insights from it.

Qualifications and Required Skills

The education, training, and certifications of a data scientist include: 

• Degree: An advanced degree in data science, statistics, computer science, and mathematics.
• Certifications: Certifications courses are also available for this field that includes various options in SQL/Data Engineering, Certified Analytics Professional, and Microsoft MCSE Data Management and Analytics.
• Minimum Qualification: Since data scientists generally require an ample amount of educational or experiential background to finish a range of enormously complex planning and analytical tasks in real-time, even a bare minimum of a bachelor's degree in a technical field will do well.
• Tools: It is interesting to note that data science involves having knowledge with a huge number of big data platforms and tools, and these include Pig, Hadoop, Spark, Hive, and MapReduce. Other programming languages that are necessary here are Python, SQL, Scala, and Perl, and statistical computing languages, such as R, are important.
• Skills: Data science also involves hard skills that include machine learning, data mining, deep learning, and the skill to incorporate structured and unstructured data.
• Research: A small experience with statistical research techniques, like clustering, modeling, segmentation, data visualization, and predictive analysis, plays a major role in this field.

Characteristics

We present to you some of the characteristics that are required for a data scientist. Since data scientists require much technical knowledge, it is also important for them to have some soft skills that are combined with intellectual curiosity, skepticism and intuition and nevertheless creativity. Since the role involves direct interaction with many teams and even people across the globe, having some interpersonal skills is also important. The entire field of data science is straightforward and complex, and this calls out for the data scientists to be powerful storytellers to present the data insights to the teams of the entire organization. Having some amount of leadership skills plays a major role in getting involved in every aspect of the project, and this involves decision-making processes, risk management, risk predictions, business tactics, and main importantly, handling a huge number of data that is needed for predictive analytics.

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What are the Fundamentals to Become a Data Scientist?

As discussed above, having an educational qualification in various subjects like Information Technology, Computer Science, Statistics, and Mathematics will be apt for one to become a data scientist.

Other skills that are required to become a data scientist are:

Having a good knowledge of a creative mind, mathematical computation, analysis, and a fair amount of curiosity helps in a great way to become a skilled data scientist . Thinking like an entrepreneur to develop the business and handle the crises at the right time, makes a person an effective data scientist. A good amount of knowledge on computer algorithms, including two important programming languages such as the R and Python, is most important. The job of a data scientist involves working with an interdisciplinary team like data engineers, business strategists, analysts, data specialists, and other professionals. Most of these other disciplinary teams work as a supporting panel to the Data Scientist. However, even though the data scientists have enormous support and help from the other teams, they should devise their methodologies to work and analyze the data involved in the business. They should have the insight to visualize the data through the various data visualization tools.

When it comes to all the economic sectors and industries, data scientists play a major role in analyzing, handling, and creating methodologies to improve the business. Here, we present you with some of the major sectors where the role of a data scientist is major.

Find out the six major areas of Data Science: 

Usually, data science has a few major areas that play a very important role in the collection, analyzing and interpreting the data collected, find out the major areas that data science is involved in:

1. Pedagogy

Pedagogy is a teaching method that is used by the data scientists to work with organizations to analyze the best principles and ideologies to apply when they collect and analyze the information of the consumers and products.

2. Formatting of Data

Data science is a very complex field, and data scientists require the right kind of software and tools to analyze the required statistics and algorithms, as they deal with a high amount of information.

3. Multidisciplinary Investigations

As stated above, the complex nature of the work that the data scientists deal with requires them to use varying methods to collect a huge number of data. Hence, using the right kind of methodology to resolve the complex systems with interconnected pieces helps more largely.

4. Theory

With thousands of applications that are being introduced in the field of IT every day and among the data science is considered to be the sophisticated and evolving professional arena.

5. Tool Evaluation

Data scientists use quite a several tools to control and study a large number of data. Hence the data scientists must use the new tools as they are released in the market to understand their effectiveness and even to keep experimenting with the new ones.

6. Methods and Models for Data

The complexity of the nature of their work makes the data scientists depend largely on their intuition and experience to understand and analyze the kind of methods that are needed for modeling of their data; also, they require to regulate the methods now and then to hone in on the perceptions that they strive.

Various Tools that Data Scientists Use: 

There is a huge set of tools that a Data Scientist uses every day. These tools come under many categories like programming, statistical programming tools, scripting, and programming tools and tools for data analysis, among a whole host of other tools.

1. R Programming

An R Programming tool provides a complete analysis of the data for the Data Analysts to get a valuable reference from the data, this programming tool is one of the important statistical computing tools.

2. SQL

SQL mainly helps the Data Scientist in understanding and analyzing the structured data and work on a relational Database Management System. The full form of SQL is Structured Query Language.

3. Hadoop

Hadoop helps the Data Scientists to work with Big Data, as it makes the work easy and understandable. It is considered to be one of the important tools in the field of technology.

4. Python

A versatile tool widely used by data scientists, Python is an object-oriented programming language that has a wide variety of libraries that makes it the best tool to be used for almost all the tasks.

5. Tableau

The reason why Data Analysts use Tableau is because of its excellent reporting capacity. Also, the results of their analyses are procured in a manner that enables everyone to understand the data.

6. SAS

SAS is an advanced analytics tool that is widely used by Data Analysts. The features of the SAS includes analyzing and reporting any amount of data available. SAS is equipped with numerous analytics tools, including statistical functions with excellent Graphic User Interface. This helps the Data Scientists to transform their data into valuable business insights.

Conclusion

IT is a vast field and the world, and as we all know the world has become a global village due to the advent of technology and thanks to the people who opt to choose their profession as a Data Scientist, as it is due to their effort in collection, examining and interpreting a huge number of the collected data that makes it easier for the rest of them to do their job at ease. It is imperative to note that the versatility of the work of data scientists is unmatchable and is truly commendable. Interestingly, the demand for the role of a data scientist is more than what it used to be a decade back. With many companies deciding to hire a huge number of Data Scientists, there are many training centers and certification courses that are available for the budding data scientists to get their future set with the right kind of knowledge and training and usage of technology.