Top 10 technology skills a data scientist should know

The technique of extracting meaningful insights from the big data stored in the datasets of enterprises is known as data science. It has become the hottest technology trend for enterprises in the competition driven business world. Big organizations are continuously receiving huge amounts of data each day and yet they need some methods to manage and analyze this huge data to gain useful insights. Consequently, emerging technologies like Hadoop, Cassandra, and Apache Spark have changed the entire landscape of storing, analyzing and managing the big data. Simultaneously, businesses are looking for top data science talent they can put on their big data projects and that’s why being a data scientist is considered to be the hottest job of the 21st century.

With the growing competition, the task of finding a suitable data scientist has become quite tough. A data scientist helps businesses to design policies and define processes based on empirical evidence rather than fond hopes. In another words, a data scientist helps to create policies and processes based on facts found from stored data.

A data scientist requires a mixture of multidisciplinary skills that includes mathematics, statistics, computer science, and business communication. Following are a list of the top 10 skillsets a data scientist must have before you hire him on your enterprise data project.

1. Basic Tools: A data scientist is in great demand and he must know how to use the tools of his trade. This simply means that a data scientist should be well aware of the statistical programming languages like R or Python, and SQL. You need to confirm that your data science team includes such a skillful scientist for making your datasets meaningful yet productive.

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2. Statistics: As a data scientist, a basic understanding of statistics is vital. He should be well aware of statistical tests, distribution, and likelihood estimators, etc. Statistical skills are important at all company types especially when dealing with a data driven business.

3. Machine Learning: If you’re a big data driven company where you need to deal with terabytes or petabytes of data on a daily basis, in such instances you need to be familiar with machine learning methodologies and techniques. In such cases you need to know things like random forests, k-nearest neighbors, and ensemble methods. These data science techniques can be further carried out with the help of Python or R libraries. It is really important to understand how and when to use a particular technique.

4. Mathematics: To perfect your data science skills as a data scientist you should know some basic multivariable calculus or linear algebra. A data scientist needs to have a good knowledge of mathematical calculations for building your own implementations in house. This is important for those businesses where the product is defined by the data. The small improvements in predictive performance can create a huge difference when it matters the most. Companies can get an advantage over their market competition when such data oriented decisions need to be taken.

5. Data Mining: A data scientist deals with big data which can be messy as well. It is really important for a data scientist to deal with imperfections in data which include missing data, inconsistent string formatting, and date formatting. A skillful data scientist converts messy or imperfect data into a managed set of data to gain insights by applying different science or data techniques.

6. Data Visualization: Visualizing data in an attractive yet easy manner is a mixture of science and art. This is an essential skill where data-driven decisions impact the business the most. A data scientist should be familiar with visualization tools like ggplot and D3.js. He should know the principles behind visually encoding data to present for audience in an easy yet convincing style.

7. Software Engineering: It is recommended to hire a data scientist who is skillful with data engineering software. A data scientist will be responsible for the entire data logging that plays a vital role in creating a product or other data driven strategies for any business.

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8. Analytics: From data analysis to business analytics, a data scientist must be good at analytical skills. This includes mix modeling, attribution modeling, retail optimization, sales forecasts, user profiling and segmentation, customer churn, calculating long-time value, and many others. A data scientist should perform data analysis from a business perspective which includes finance, marketing, ROI, etc.

9. Business Acumen: A data scientist should possess solid industry knowledge. He should be familiar with those problems with which a business is dealing. This will make it easy for a data scientist to find a suitable solution for the business. He can also recommend a new way to deal with the existing problem with the help of data. One can only leverage data at its full potential only if a data scientist has good knowledge of the industry he is dealing with. By arming a business with quantified insights a data scientist can enable smart decision making for businesses.

10. Communication skills: Being able to communicate well is a must have skill for quantitative professionals. Enterprises are looking for someone who is well versed with the technical terminologies and can explain it to a non-technical person in an easy style. A data scientist should be capable of conveying technical things to the marketing or sales team.

If a data scientist has all these qualities you can hire him for your business data project, else you need to search for another one. A tech company dealing with data science projects is the right choice to hire resources. You can hire such companies on your data projects to gain business advantages and profit.