With the power of data unleashed and seen, it seems each industry has a gaping hole in their organizations to be filled by data scientists. Students and professionals alike are responding to this demand by taking data science courses and specializing in particular skills.
What is the whole package, though, that makes up the data scientist? How is one to become a data scientist that companies would scramble to pay for?
Below, we outline how.
These can be learned through degrees, courses, specializations, and certifications. The crucial thing is to follow the learning with the practice.
Statistical analysis helps to generate statistics from stored data. The results can then be analyzed to infer insights and conclude meaning about the entity being analyzed.
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You’ll be using programming languages to organize, clean, and make sense of data. Hence, you need to be fluent in its use so as to perform these basic tasks efficiently. Work and develop mastery in languages like R, Python, Perl, Java, C/ C++, and SQL. Python and R are the most important programming languages for coding in data science.
Data mining and processing
It is the process of deriving insights and information from huge databases. Using certain methods, knowledge is discovered that is then leveraged for making business decisions.
Excellent at working with unstructured data
Unstructured data is not organized in a pre-defined manner. A data scientist needs to realize that most of the data s/he works with would be unstructured and so, develop the skill to manage and glean valuable insights from it as well.
Knowledge of SAS and other analytical tools
Analytical tools will help you generate insights and information that would help the company. Some of the most popular analytical tools include R, Pig, Hive, Hadoop, and Spark. Certifications in these tools can further help your career.
No matter the technical expertise of a data scientist, he/ she needs to develop non-technical aspects as well for other career aspects. These include:
An understanding of business
Ultimately, a data scientist working with a corporation is working to generate growth and profit for the business. Armed with a strong understanding of how businesses work, what is important to them, etc. will help the data scientist to know what data to mine and how.
Since each company is generating huge amounts of data every day, the data scientist’s job becomes a tremendous one. Knowing how the company operates, how businesses succeed, will help him to channel his technical skills productively, in a direction where companies can experience actual growth.
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Data intuition is developed as a result of working extensively with data. It is the ability to sense patterns and possibilities in the data when logically and on the surface, none seem to be present. It is also knowing where the truly valuable bits of data lie. As would be evident, this closeness to and intuition about data doesn’t come easy. It comes with experience and exposing oneself to varied types and amounts of data in different contexts.
Developing this unquantifiable intuition could make one an indispensable data scientist.
While one does not need to be a wizard in English or any other language, one should know how to communicate ideas to another person effectively.
Data science is a complex and specialized field that most people do not know the nuts and bolts of. Using jargon would be of no use. What a data scientist needs to do is distill the highly complex concepts into layman terms and understandable examples. This skill would be useful in persuasion and negotiation when one would attend a meeting. If a non-technical user of data is able to understand the crux of what you are explaining, then you have communicated well.
The above is a comprehensive list of all the skills needed to gain mastery in the complex yet fascinating world of data science. Remember to remember that mastery of these skills comes with time. Till then, continue to gain work experience and work on your own projects. They are all making you more skilled.
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How is unstructured data different from structured data?
Structured data is simple to find and analyze, but unstructured data takes longer to process and comprehend. Structured data is made up of numbers and values, whereas unstructured data is made up of sensors, text files, audio and video files, and so on. Unstructured data is often stored in its natural format, but structured data is clearly defined and searchable. Unstructured data comes in a variety of formats, whereas structured data comes in predetermined formats.
What function does statistics play in data analysis?
To put it another way, statistics is the fundamental application of mathematics to the formulation of a technical analysis of data. It's utilized to solve real-world problems so that data scientists and analysts may seek out significant trends and changes in data. Because statistics focuses on evaluating, collecting, and interpreting data in a logical and typically numerical manner, it's only natural that the approaches established in statistics may be used for Data Analytics. You can use analytics to help you create hypotheses, and statistics to test them.
Is there a drawback to using data intuition?
Data is a type of knowledge that is both objective and unbiased. Since Data intuition is subjective, business choices should be founded on data that is objective. When you don't have enough facts or time to analyze logically before making a decision, intuition comes in handy. Even while intuition may be developed based on knowledge and experience (a form of data), using it in commercial decision-making can still be hazardous.