• Post Graduate Program in Analytics and Artificial Intelligence
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  • Data Science Prodegree
    Co-created with KPMG in India
    4.8 out of 5 by 6071 learners
    16 X industry demand

A career in data science is highly attractive owing to its payment structure, job opportunities, and future career prospects. There are any number of Data scientist courses that you can find and that makes you qualify for the job.
The major criteria for this career are a few skills that one can easily master through the right path. These skills could very well be different from any former experience in the career thus far. Developing these skills will help the recruiters to identify you as the best option for what they are looking for!

Programming language
A strong knowledge base of any major programming languages such as Python, R, or SQL is the foremost requirement to be an expert in data science. No matter what the company or the job profile is, this is one field of expertise that is non-negotiable.

Statistics
Statistics hold more value in data science since it helps to deal with the raw data of the companies. It helps with the evaluation, designing, and making decisions in the later stages.

Deep learning
This machine learning technology enables computers to work like the human brain. An enormous amount of data is managed through computing power to make it possible. A career in data science, especially that in the automobile and AI industry requires this particular skill.

Working with unstructured data
Data science is mainly about the gigantic amount of data from various sources. The vast majority of this data is in a raw and unstructured format. A skilled data analyst can easily go through them to find and identify what they are looking for to make it useful.

Appetite for problem-solving
Simply looking at the data is not what makes the analyst skillful. It also calls for the right appetite to identify the problems underneath and finding the ideal solution as well. For which the analyst needs to have the drive for problem-solving and look in the right areas.

Data visualization
This is the skill that enables a data scientist to identify and decode the raw data into an identifiable visual to use it to convey. This skill enables the analyst to see what the data is useful for with the help of the various data visualization tools.

Communication skill
It comes next to the visualization part. The visualized data needs to be explained in a simple and well-constructed plan to the stakeholders. AT this juncture, the analyst must have strong communication skills to convey the key points and make them believe in the same. Polishing communication skills would be an added advantage to improve career prospects.

Familiarity with data science tools
Data science involves various types of tools to help with data processing. An analyst must have a fairly good idea about the working of most of the tools. Since each type of data requires different tools, it is highly imperative to be on familiar terms with these tools. Most of them are pre-programmed, so you just need to know how to use them in the proper way.

Intuition
Last but not the least, having a strong intuition on what to look for, how to use it, and which tool needs when to get the best result out of the data analysis happens to be the strongest point of being a successful data scientist.

Conclusion
Most of these skills are covered in the data scientist course available from various sources online or otherwise. What needs more work would be on soft skills which also have an equally important role in a successful career. A career in data science does not have refined eligibility criteria, instead, it mainly depends on these acquired skills.

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About Imarticus
Imarticus Learning is India’s leading professional education institute that offers training in Financial Services, Data Analytics & Technology. We’ve successfully transformed careers of over 35,000+ individuals globally through our Certification, Prodegree, and Post Graduate programs offered in association with leading and renowned global organisations in the Financial Services, Data Analytics & Technology domain.
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