Can anyone learn data science?
Data science experts come from diverse backgrounds which include chemical engineering, economics, physics, statistics, mathematics, computer science, operations research, etc. You will find several data scientists with a bachelor’s degree in statistics and machine learning but this is not a requirement to learn data science. However, being familiar with the basic concepts of Math and Statistics like Linear Algebra, Calculus, Probability, etc. is important to learn data science.
Data scientists are highly educated. 88% have at least a Master’s degree and 46% have PhDs. A very strong educational background is usually required to develop the depth of knowledge required to be a data scientist.
What degree do you need in order to be a data scientist?
In order to be a data scientist, you need to have a bachelor’s degree in one of the following domain:
- Computer science
- Social science
- Applied math
At the end of pursuing one or more than one of these degrees, you are expected to have a wide range of skills which are applicable to data science. These skills include experimentation, quantitative problem solving, coding, handling large sets of data, and others.
The ability to understand people, marketing and businesses are also regarded as a powerful tool in a data science career. The skills are often seen to be highlighted in business, political science, psychology, and various liberal arts degrees. These are often considered as a great minor, which complements a data science degree or a technical degree.
Once you are done with the bachelor ‘s degree, you can apply for master’s in data or other relevant fields if you aim for a higher level position in this domain. Also, relevant experience in the field you wish to work in is equally important to be a data scientist.
Do you need a master’s degree or a Ph.D. degree to be a data scientist?
No, a Ph.D. is not necessary to become a data scientist but can be helpful if your Ph.D. was in some sort of quantitative field. This being said, some companies prefer hiring data scientists with PhDs and will not hire data scientists with only bachelor’s degrees (unless they have experience).
Real world data science experience always outweighs the time spent in pursuing a Master’s degree or a Ph.D. because getting these degrees can prove to be an extremely long grind. You have to work hard for a long period of time to acquire these degrees but eventually, you will have no real world experience in this domain.
According to our experts, a master’s in data science or a Ph.D. can be a good way to go, in developing a technical data science skill set for potential employers but it is not a requirement to start with a career in the field of data science.
Lack of a quantitative degree does not stop one from studying data science. It is possible to learn data science even without having a Master’s degree. Ph.D.s will matter only if you apply for a higher level in the domain of data science. When you begin to learn data science, Ph.D. or a Master’s Degree is not a necessity. If your goal is to opt for an advanced leadership position, you may have to earn either a master’s degree or doctorate.
What skills are needed to be a data scientist?
There are certain schools which offer specialized data science programs, which are specific to the educational requirements to pursue a career in data science. Students who do not wish to opt for this extensive approach can pursue other options in this domain.
This includes directed Massive Open Online Courses (MOOCs) and boot camps. Some of the data science programs that are worth exploring are Simplilearn’s Big Data & Analytics certification courses. These programs can help deepen your understanding of the core subjects which support the need to be a data scientist, along with providing a practical learning approach that you will not find in any textbook.
Important technical skills that are required to become a data scientist include:
Programming: You need to have in-depth knowledge of programming languages like Python, C/C++, Perl, SQL and Java. Python is regarded as the most common coding language required in data science roles. Programming languages help one clean and organize an unstructured set of data.
In-depth knowledge of SAS and other important analytical tools: The knowledge of analytical tools will help you extract valuable insights out of the cleaned and organized data set. Some of the most popular tools that data scientists commonly use include SAS, Hadoop, Spark, Hive, Pig, and R. Certifications in this domain will further help you to establish your expertise in the use of these analytical tools.
Must be skilled at working with unstructured data: This specifically emphasizes on the ability of a data scientist to understand and manage data which is coming unstructured from varied channels. If a data scientist works on a marketing project to help the marketing team offer insightful research, the professional should be proficient in handling social media as well.
Must possess a strong business acumen: The technical skills cannot be utilized in a productive way if a data scientist does not have proper business acumen and sound know-how of the elements that develop a successful business model. You won’t be able to recognize the problems and potential challenges that need solving for the business to sustain and develop. Without business acumen, you won’t really be able to help your organization explore new business opportunities.
Need to possess strong communication skills: If you are a data scientist, you should be able to understand data better than anyone. However, to be successful in your role, and for your organization to benefit from your services, you should be able to strongly communicate your level of understanding with someone who is a non-technical user of large volumes of data. You need to possess strong communication skills in order to be a data scientist.
Must have great data intuition: This is one of the most important skills that a data scientist requires. Great data intuition means observing patterns where none are noticeable on the surface and understanding the presence of where the value rests in the unexplored pile of data samples. This makes a data scientist more efficient in their work. This is an important skill which comes with experience and boot camps are an ideal way of polishing it
Why is python used for data science?
When it comes to data science domain, Python is considered to be a very powerful tool. Python is open sourced and flexible, which adds more to its popularity. It is known to have massive libraries for data manipulation and is extremely easy to learn and use for all data analysts.
People who are familiar with programming languages such as, Java, C++ or C, and Visual Basic, will find this tool to be very accessible and easy to work with. Apart from remaining an independent platform, this tool has the ability to efficiently integrate with the existing Infrastructure system and can also solve the most difficult of problems in a simplified way.
It is said, that this tool is powerful, friendly, easy and plays well with others, apart from running everywhere.
What are the other languages used?
Apart from Python, other languages used are-