Here are the Most In-Demand Skills Required to Take up a Data Scientist Job in 2019

A data scientist is the one who takes the structured and unstructured data and use various skills like programming, statistics, and mathematics to make it useful. The analytic power of a data scientist is needed to use this data for various types of business challenges.

Data science is being used by almost all the organizations as the decisions based on the data help the organization to grow. The organizations find this technique as the most efficient to deal with all types of data.

In the current scenario, data scientists are in demand as the technology is being applied in almost every organization. Data scientists in India face difficulty in identifying top MNCs for their career but the scope of the job is good as every organization needs a data scientist.

In order to make a career as a data scientist, a candidate can take up a Data Science Certification Training course in which he will learn various aspects of data science enhancing his skills and abilities. He will also know the procedure of handling various types of data and how it is further processed.

If a person is good in mathematics and statistics, then it will be a plus point as he will be able to analyze data and will learn data analysis through various tools as well.

#Duties of a Data Scientist

Data scientist has to take various responsibilities as he has to use his traits and analytical skills to convert the raw data into useful information. He also has to collect data from various sources and then do the conversion. A talented data scientist is difficult to find and that is the reason that qualified data scientists are in high demand.

A data scientist is responsible for gathering and analyzing data and use various types of analytical tools to relate different datasets. The result of this analysis is to determine customer behavior and also take a look at opportunities and risk related to a business.

Data scientists develop statistical learning models, which are used to analyze data. So they must have experience in using the tools that help them in creating and assessing complex predictive models.

#Primary Skills Required for a Data Scientist Job

A candidate should possess the following skills for becoming a data scientist.

Education:

The candidate should be highly educated in order to become a data scientist. It has been estimated that 88% of the candidates have done master degrees and 46% have done Ph.D. In order to become a data scientist, a strong educational background is necessary. The candidate should possess a bachelor degree in computer science, physical science, social science, and statistics.

If the candidate also has mathematical knowledge, it will be a plus point for him. After possessing a bachelor degree, the candidate should also do a masters degree or Ph.D. Along with it, online training of Hadoop or Big Data would be an add on. Other things that a candidate should do is to start a blog, develop an app or learn more things to make a career in this line.

Programming:

The candidate should have the knowledge of analytical tools through programming and R language is the most preferred as it has been designed for the same purpose that is data science.

R programming is used to resolve many problems related to data science.  R programming is a good language for statistical data. There are many resources available on the internet from where people can learn R programming.

Python coding:

Python is a language, which is easy to learn, and it can be used for all the steps that are used in data science. SQL tables can be easily implemented through this language and datasets are created which can be of any type.

Hadoop platform:

Hadoop is a platform that is used in many cases regarding data science. The candidate should also be familiar with cloud tools like Amazon 3, CloudSim, etc. Though Hadoop is not always required, still it is an essential element, which helps the data scientist to resolve many types of problems.

Hadoop is used when there is low memory and data has to be transferred to other servers. Other places where Hadoop can be used is data exploration, data summarization, data filtration, etc.

Database:

Hadoop and NoSQL are one of the good options as they have become a component of data science. But there are some complex queries, which are to be executed through SQL.  Operations like addition, deletion, and extraction of data from a database are done by SQL. Other functions that can be performed by SQL is the transformation of database structures and analytical functions.

A data scientist must be familiar and expert in using SQL as this language will help him to work on data easily. The commands used through the SQL save time in programming through other languages.

Apache Spark:

Apache Spark is a big data framework, which works faster in comparison to Hadoop. The reason behind this is that Hadoop writes and accesses data through disk while Apache Spark does this through the memory.

Data science algorithms can be run through the Apache Spark as it takes less time. It also saves time in disseminating a large set of data and helps the data scientist in data processing. This platform also prevents data loss due to its speed of data processing. This makes data analysis easy through this platform.

Machine Learning and AI

Machine learning and AI include various things like neural networks, adversarial learning, reinforcement learning, and many other things. Data scientists have knowledge about them but are not much proficient in using them.

A candidate who knows machine learning will have more job options in comparison to other data scientists. Machine learning helps a data scientist to make predictions related to major organizational outcomes.

Data Visualization

The flowing of information in a business is done frequently so the data has to be made into a format that can be easily comprehended. Charts and graphs can explain the data easily and people like to see them for various purposes.

A data scientist should have the capability of making charts through data visualization tools, which help to turn complex data in an easy comprehensible format. The organization can also work easily with data, which is in the form of chart and graph.

Unstructured data

Unstructured data cannot be stored in a database table. Such data includes videos, blog posts, audios, etc. Another problem with such data is that it is not streamlined so sorting is difficult. The data scientist should have the capability to work on such data and manipulate it.

#Final Verdict

The job of a Data scientist is a tedious job as there are many things to learn. He also has to analyze data for the purpose of predictions related to the organization. The data professional/ expert must be familiar with programming, machine learning, Hadoop, etc. Before doing these, he should also have a good educational background, which will help him to determine the data.

The candidate who wants to become a data scientist must have the capability of analyzing the data from various sources and arrange them in such a way that it could be understood. Some unstructured data like audio, video, blog post, social media posts, etc. are difficult to analyze and the data scientist should have the capability of doing data analysis through these.

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