Top 7 Must-Have Data Science Skills: Data Scientist is the hottest job of the 21st century. Companies have started to realize the importance of data-driven strategies and are looking for individuals who can offer an insight into the constant stream of information.

According to research, about 70% of the US executives said that by 2021 they want candidates with data science skills. And, as the companies continue to digitize, the demand for Data Science professionals is only going to grow. 

If you want to make the transition to a Data Science career, this article is for you. A Data Scientist is not just someone sitting behind a computer and coding all day. It is a highly-critical profession that requires facilitating the decision-making process. Broadly speaking, all the essential skills needed for becoming a Data Scientist can be divided into two categories:

1. Hard skills
2. Soft skills

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In this article, we will discuss the top 7 must-have Data Science skills.

• Hard Skills

The crux of Data Science is extracting useful insights from raw data. This involves the process of extracting, exploring, and visualizing data using different tools and technologies. Some of the hard skills that a Data Scientist needs:

• Python & R programming

Some of the core jobs of a Data Scientist involves application development, data management, testing, etc. What you need is a high level of proficiency in programming languages. Python and R are among the most popular programming languages in Data Analytics. 

Both are statistical programming languages that are used across all industries. R has extensive APIs and rich libraries that make the process of writing codes easier. It allows Data Scientists to perform operations faster than Excel. However, even though R is a well-known language, it is being outperformed by Python. 

• Data Visualization

When data is extracted from databases, it is still in raw form. This data must be modeled and curated for representing it in a particular fashion. The core job of a Data Scientist is analyzing complex datasets. However, getting hands-on experience in Data Visualization is another important skill. It involves taking large and complex datasets and representing them in a pictorial manner so that the information is interpreted easily. 

Data Visualization tools such as Tableau, Qlikview, D3.js, PowerBI, etc. can be used for accumulating complex datasets and compiling them to be represented in interactive charts, graphs, and trends. Tableau and Microsoft are industry leaders. Other software like TIBCO and MicroStrategy are giving these technologies a tough competition.

• Statistics

Behind all the tools are the concepts and logics used to perform operations and a deep understanding of statistical methods to help Data Scientists use the information in a better way. Methods like Mean, Median, Mode, Kurtosis, Variance, Regression, Correlation, Quartile, ANOVA, Logistic Regression, Linear Discriminant Analysis, and K-nearest neighbors are commonly used statistical concepts in the field of Data Science.

• Mathematics

In order to master statistics, you need to be good with numbers. There are several areas in the field of Data Science where Mathematics and Statistics are used simultaneously. However, there are certain fields that are exclusive to Mathematics. 

Analytics includes performing mathematical operations. Mathematics is an important part of Data Science as without applying mathematical operations and calculations, there will be no inferences. Tools like Calculus, Linear Algebra, Discrete Mathematics, and Probability are important Data Science skills you must have.

• Database

All the raw data, Data Scientists use are stored in databases. For accessing the data, they need different codes in the form of queries. Also, it is not always a single database. Data Scientists work with multiple, relational databases that store petabytes of data. That is why you need the knowledge of query languages and databases for extracting the right data. Only then will you be able to create statistical models and perform different operations. 

There are several relational database technologies like MySQL, BigQuery, Amazon Redshift, PostgreSQL, etc. In the case of non-relational data, you can use MongoDB, Apache HBase, Apache Cassandra, etc. SQL is the most commonly used database query language.

2. Soft Skills

According to a study by Deloitte, around 2/3 of all job roles will require soft skill-intensive employees by 2030. Apart from the hard skills, a Data Scientist also needs to be a critical thinker and must be good at multitasking. Here are the soft skills you need to become a Data Scientist:

• Business Acumen

As a Data Scientist, you are responsible for solving complex queries. To do this, you must have an understanding of the real-life issues behind it. Some unspoken expectations from Data Scientists are knowledge of business, ability to perform a root-cause analysis, ability to infer conclusions, and ability to brainstorm from the present cases.

It is a common notion that a Data Scientist needs to be proficient in technology. However, now that the corporate dynamics are changing, strong business acumen is a must-have skill for a Data Scientist. Once you are familiar with the company and the industry you are working in, you can look for opportunities that provide better results. You don’t have to limit yourself to the KPIs. You can now fill the existing gaps and look beyond the normal.

• Communication skills

Having knowledge won’t be enough for you unless you know how you can convey it. Companies today are multicultural platforms with talents from different geographical regions. One of the toughest challenges they are facing is communication.

Data Scientists are responsible for coordinating with different teams and imparting the knowledge they should implement. Therefore, when you have outstanding communication skills, you will be able to do justice to all the roles and responsibilities. And now that many companies focus on larger geography, data scientists are able to communicate in multiple languages. Understanding the customers, listening to their issues, and communicating with them will help in creating a rapport and enhancing communication.

Data Scientist is an important business decision-maker and a technically-sound profession in terms of database, programming, mathematics, statistics, and data visualization. According to a 2017 report by McKinsey, Automation will bring major shifts in the next 15 years. Even though Robotics or AI will replace or change some jobs, it will create others. Millions of people will have to upgrade their skills and switch their occupations. If you are a beginner and want to make it big in the technology world, you have to upskill. Advanced education methods can definitely get you there in no time. Data Science is a desirable profession. Getting a Data Science certification and hands-on experience will surely help you land a high-paying job.