The world is flourishing and developing at a faster rate than ever before. According to an Economic Times article on 10th of May, 2019, India’s data composition is presumed to grow at a CAPR of about 72.6 percent to 10,96,58,793 million MB by 2022 claimed by a study. This increasing growth of data consumption leads to vast amounts of data generation. These vast bulks of data sets require extensive work to leverage the industries with the rapidly growing data production. Be it online platforms that saw an increase in internet users from 302.6 million in 2015 to 696.77 million in 2020, or the other platforms that require data retrieval and safeguard for better future projections, everywhere that you look, you would find a collection of data.

To keep up with this escalating need to manage data, data scientists’ demand has emerged out even more vital than ever. As specified by another article by Times of India in January 2020, __India is likely to witness a 62 per cent rise in data science job openings in 2020__. To address these needs, initiatives must be taken to educate the interested candidates with comprehensive data analytics courses. After we have understood the skills required of a data scientist, finding data analytics courses in Pune would be more comfortable. **Comprehensive data analytics courses** that cover the critical aspects of a data scientist’s skills are as crucial as the process of learning. The courses you choose should help you prepare yourself for a data analyst and scientist’s role.

Not only do the data analytics courses matter, but choosing the right data analytics courses holds significant importance too. Before selecting the best course that aligns according to your needs and requirements, you need to understand the critical skills required to become a data analytics scientist in the first place.

So what are the essential skills that are required of a data scientist?

Table of Contents

**Statistics**

The educational background of data scientists needs to be healthy and robust. The most important aspects of a data scientist are mathematical and statistical skills, followed by software engineering training. A data scientist’s role is to assist the stakeholders in drawing a future projection based upon the statistical findings from the data sets.

Thus, as a result, a data scientist should understand, study, and be familiar with statistical knowledge. Statistical knowledge enables the data scientist to predict the future probabilities of the data received and under consideration.

**Programming**

Another vital skill that a data scientist ought to possess is programming skills. Data scientists are expected to evaluate and understand the data sets and address the problem statements that emerge. To handle the problem statement, a data scientist should be well equipped with programming skills. Having knowledge of computer programming languages like Python and R and the ability to work with SQL are pivotal and play a significant role in data sciences. The R programming language is a specially developed programming language to deal with data sciences. If you are intending or aiming to become a data scientist, you should learn how to program.

**Algebra and a bit of calculus?**

For companies that are built above the foundation of data sets, it is imperative that a data scientist be well versed in multivariable calculus along with linear algebra. Since data scientists have to deal with machine learning, they should learn more about the techniques and be aware of where to implement them. Companies like Uber, Google Maps, etc., require extensive knowledge and data handling. Hence it is strongly recommended that data scientists be well aware of the mathematical skills that come with the Step functions, plotting of operations, and many more.

**Software engineering**

Since most of the role of a data scientist demands having knowledge of software and programming, being a software engineer has the edge over any other engineering field providing a better understanding of the software. Languages and software platforms like Python, SQL Database enable the data scientist to work and communicate with the data.

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**Machine Learning and cloud computing**

Machine learning is a skill that holds notable significance in a data scientist’s role, mainly if he/she works in a data-driven company. Machine learning is considered to be the subset of the domain of data sciences. With the help of knowledge in machine learning and cloud computing, a data scientist can coordinate with the company stakeholders to make their data-centric decisions.

**Data Wrangling**

There are times when the presented data sets have a missing value or data point that can potentially disrupt the entire data chain. The consequences of these results hold a crucial significance in data-driven companies. Thus, a data scientist should be able to work with such imperfect data banks and be able to communicate and work on the data despite the presence of a missing link, if possible. Since a data scientist’s role is to understand, read, and interpret the data, they are not supposed to bridge the gaps between them. They are supposed to truncate the processing and response time and time spent to collect and organize the disorganized data before its utilization.

**Data visualization and its conveyance**

Last but not least, the most significant skill that a data scientist needs to possess is understanding the data, working with data, finding the problem statement, and implementing the data visualization. Data visualization is done to map future projections and trends, further helping the stakeholders decide the company’s profitability. A data scientist should be well acquainted with data visualization skills and communicate and translate this information further.

**Key Takeaways:**

**Key Takeaways:**

- Working with a vast amount of data requires the data scientist to be well versed in software and programming skills.
- Mathematical skills like an understanding of multivariable calculus and linear algebra and various other things that revolve around solving data-centric problem statements are essential.
- Being educated with R-programing and SQL plays a crucial role.

After analyzing and understanding the data, a data scientist is required to visualize and interpret it further.