Your big horn-rimmed glasses and nerdy appearance can point you towards a career of immense importance – data science. With the way data is changing the world, you should start considering becoming a data scientist.
But first, you need to learn how to get the data science degree and programs that will prepare you for rewarding data scientist jobs.
From the start of the 21st century to the present day, Data Science sits comfortably as one of the hottest careers in the world. Data Scientists receive a tempting salary and job growth is good. The world generates more data every day that needs to be gathered and analyzed by scientists for several productive uses.
Therefore, in this article, we will show you how to become a data scientist. You’ll see the types of college programs and degrees you need, and how much it will cost to become a data scientist. Plus, we’ll show you the types of jobs you can get with your degree and answer other questions you might have on the subject.
What is Data Science?
Data science is the disciplinary field that creates data scientists. It is a multidisciplinary field that trains professionals in the ability to extract insights and knowledge from structured and unstructured data.
As a multidisciplinary field, data science derives its knowledge from mathematics , statistics, computer science and information science . Specifically, it combines statistics, data analysis, and machine learning with related methods for data understanding and analysis.
Due to its close relationship with these other disciplines, it has become difficult these days to distinguish data science from analytics, business intelligence, predictive modeling and statistics. People (professionals and novices) now use either of these terms interchangeably.
Also, the concept of data mining and big data is same as data mining. This is why some universities offer their data science programs under the name Big Data.
So you see that data science is concerned with the analysis of big data using computer programming and virtual mining methods.
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Who is a Data Scientist?
How does it sound? Not nice enough. So, let’s break this down in human terms.
A computer scientist is a person (the professional) who collects and analyzes data, with their multidisciplinary knowledge, to provide a solution. He must therefore have the statistical knowledge and computer skills necessary to solve complex problems.
In addition, the Data Scientist will use mathematical techniques and algorithms to solve some of the most analytically complex business problems. It is for this reason that the data scientist is a treasure trove for large corporations and companies looking to scale up their operations.
The primary function of a data scientist is to sift through the meanings of the structured and unstructured data their organization receives. Thus, you will find the data scientist either extracting data from a database, preparing the data for various analyses, creating and testing a statistical model, or creating reports that management can understand through using data visualizations.
What skills should a data scientist have?
It is not enough to have acquired the right education for data scientists (which we explained above). Without certain special skills, you may not be able to succeed in this profession.So what are these skills you need to have?
Business acumen or wisdom. Because you’ll find yourself working in a variety of business settings, you need to learn more than basic knowledge of the business industry. It will help you solve complex problems and create solutions that align with your business goals.
Communication skills. You may know how to communicate with data, but people will rely on you to communicate your findings and solutions with them in a language they understand. You must be able to translate your technical findings and analysis clearly into the non-technical departments of the organization where you work.
Advanced technical skills. Your skills in math, statistics, machine learning tools, data mining, data cleaning, data visualization, and unstructured data techniques should be out of this world.
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A word constantly pops up when we describe what data scientists do. This word is “analyze”. Data scientists collect and analyze data, it’s true. But data analysts also collect and analyze data. So, does this make a Data Analyst a Data Scientist?
This is the central question of the Data Science vs Data Analytics argument. How does the data scientist differ from the data analyst?
Data analysts examine large sets of data to pick out trends, develop charts, and create visual presentations that help businesses make better strategic decisions. Data scientists, on the other hand, design and build new data modeling and production processes using prototypes, algorithms, predictive models and custom analytics.
So you see, while the analyst uses already existing techniques to perform his tasks, the scientist develops new processes and techniques to facilitate data analysis.
Moreover, data analysts in their routine work are masters of SQL. On the other hand, the data scientist, in addition to having all the skills of analysts, has a solid foundation in modeling, analysis, mathematics, statistics and computer science. He uses them to better communicate his findings to relevant business stakeholders to influence how they approach a business challenge.
Also, while the data analyst will solve questions posed by the business, the information specialist will formulate questions whose solutions are likely to benefit the business.