7 Most Important Key Skills Data Scientists Should Have In Their Mind & CV

Nov 24, 2018 | 28503 Views

The hottest job of the century is all about acquiring and mastering right skills aligned to the job. Presenting them on your resume is quintessential if you want to land a data scientist job.

However, there are some key skills that go in a resume which make for a good and professional data scientist.

1. Statistics And Mathematics
Statistical skills are vital in tech companies is driven by data science. One should be aware of statistical terminology and concepts such as tests, distributions, likelihood estimators, interquartile range, etc, among others.

Consequently, mathematics also aids in improving the predictive presentation which creates a huge difference in the outcome. Performing these calculations would require basic knowledge of multivariable calculus or linear algebra. These would help to build our own implementations in-house.

2. Strong programming and computing dexterity
To pursue the dream to be a data scientist one should be exceptionally good at coding and have a computing mind that would write, analyze and understand a code at any given time. This comes through vigorous study and practice of various programming languages. Good knowledge of R is generally preferred for those who want to pursue this career, as it is particularly designed for data science. In fact, more than 40 percent of data scientists are using R to solve statistical problems. However, R has a sharp learning curve. Itâ??s difficult to learn if you are not comfortable with coding. Python, on the other hand, is the most common and preferred language that we usually see being used in data science roles, along with existing object-oriented languages. Because of its adaptability, we can use it for almost all the steps involved in data science process. Python can take various formats of data and we can easily import SQL tables into the working code. It also allows creating data sets. This is why around 51 percent of data scientists prefer Python over other development languages.

3. Passion To Partake
We discover ourselves when we come out of the box. A healthy competitive spirit is needed for a man to thrive. A number of competitions, hackathons, and seminars are being conducted frequently these days for people who are passionate about data science. Taking part in these activities will enhance knowledge, and we come to know contemporary trends in the field. To name a few, Machine hack, Hackerearth, and NMIMS are some of the platforms.

4. Projects do matter
Learning is just the beginning, we need to implement. The knowledge one possesses can be appreciated only when it is represented. Taking up live projects, understanding the architecture behind the screen would help a lot. Hands-on experience in the field of data science is very much needed at the moment, large firms look for people who have experience and an analytical mindset.

5. Handling Unstructured Data
Unstructured data are unspecified content that does not fit into database tables. Examples include videos, posts, blogs, customer reviews, and audios, among others. Categorizing these data types is very difficult because they are not well organized.

It is important that a data scientist is capable of working with unstructured data. Apache Hadoop, Microsoft HDI insight, NoSQL, Polybase, presto are some of the software we need to be aware of in order to handle unstructured data.

6. Storytelling With Data
Engaging business stakeholders in a technique that captures their attention both emotionally and logically is one of the requisite skills that a data scientist needs to have. A vast amount of data produced in a company needs to be expressed in a pattern that can be understood by the hoi polloi. People naturally understand pictorial representation in the form of graphs and charts rather than just plain numbers. As a data scientist, one should be able to visualize raw data with the help of data visualization tools like ggplot, d3.js, matplotlib, and Tableau. A lot of people find it difficult to understand the serial correlation and p-values. They understand these complex things when represented visually.

7. Elevated Academics
It takes a lot to become a data scientist. Most importantly, educational qualifications have a main role in being a data scientist. Companies prefer candidates with masters in fields of statistics, mathematics, computer science, and physical science. PhDs in fields like applied mathematics, statistics, computer science, and Astro science are mostly preferred for research-oriented companies and the ones who are trying to develop advanced models in data science.

Source: HOB