Most Essential Skills Of A Data Scientist

Most Essential Skills Of A Data Scientist

Data science is attracting a broad audience from a range of backgrounds because of its novelty, popularity, and all the perks involved. Unlike most fields, data science is not restricted to the holders of a particular degree. As long as one has the skills which the companies are looking for in a data scientist, one can make it big in the field. Here are the essential skills that can make you a desirable candidate for a data scientist job.

The Right Knowledge - An Absolute Must!

A data science degree is only one of the things that make you a Data Scientist. An aspiring professional needs a different set of learned skills to thrive in the field. These skills include coding, Mathematics, especially statistics, SQL, big data computation frameworks like Hadoop, and so on. Though these skills can be learned independently and separately, that does not imply that a data sciences degree is irrelevant or useless. People who have completed their higher education in data sciences or related fields such as mathematics have a critical advantage when it comes to the sector. Another vital element is your expertise over data structures as well as unstructured data, which are both significant elements in the work of a data scientist.

Big Data Computation Frameworks - Highly Necessary

These are frameworks that manage and analyse big data. Data scientists are required to know the ins and outs of big data frameworks, as it is a growing sector that offers employment to many each day. The most popular ones requested by companies are Hadoop and Apache Spark.
Hadoop is a popular computation platform that allows the user to handle large volumes of data, even beyond the capacity of the system being used. The platform is also used to convey this data to different points of the system.
Apache Spark performs functions similar to Hadoop but is faster. It uses the system memory cache to store computations, whereas Hadoop physically writes them on the hard drive. The platform is specially built for data science to enable the execution of complex algorithms faster and also to prevent loss of any data. You can use it on a cluster of machines. It also saves time by disseminating large data sets while working with them, making computation easier. It is also capable of handling unstructured data.

SQL - Basics Are Vital

Structured query language is used to handle data in a database. Even though it is used mostly in business applications, data scientists are also required to be able to execute complex codes in it. SQL is a tool that can make extracting and operating on data from databases easier; hence, it is indispensable. There are many resources available online that can teach you SQL, help you solve SQL problems and exercises to improve your proficiency level.

Mastery over Analytics tools

Unless the name did not make it clear enough, data science means the study of data. Data analytics play a significant role in this study, and therefore, all aspirants should have mastered the standard tools of data analytics. The most popular one is R, and a large portion of data scientists prefers it. However, R has a steep learning curve, which means the more progress you make, the harder it gets. It also means that it will be challenging to learn even if you have learned computer programming up to a certain level.

Coding in Python

Python is a computer language with a growing fan-base in all sectors, and data science is no different. It is easy to use, convenient, flexible and runs on all platforms. Python has many salient features that make it the go-to language for coding. In data science, the part of it which attract programmers is the presence of several libraries, which are pre-existing and free to use functions. Many commonly used tasks and roles are present as libraries, which makes it convenient for coders. Learning Python online, practising python exercises and python mini projects are the best way to improve your coding skills.

AI and ML - The Hotspots

As sectors disrupting everything around them, it is no surprise that AI and machine learning made this list. It might not be as crucial an addition to this list, but knowing it is guaranteed to make one stand out from the rest. AI is capable of data analytics better than humans, and most data scientists are not experts in the areas of machine learning, neural networks, and artificial intelligence techniques. Therefore, knowing this puts you in an advantageous position.

Data Visualisation Is Essential

In business applications, data visualisation is essential due to one primary reason: Not everyone can make sense out of numbers. charts, graphs and plots have been an unavoidable part of presentations since the beginning of businesses. To be able to use the information obtained, it is essential to visualise the data first. Therefore, data visualisation is a valuable skill in the arsenal of a data scientist. One must know how to use visualisation tools such as Matplotlib(Python Library), Tableau, among others.

Non Technical Skills - Cannot Be Ignored

Every job has its requirement of technical proficiency. However, every position in the world has a list of non-technical specifications that affect your value as an employable person. These factors include language and communication skills, business acumen, team spirit and a passion for the job. These factors determine your chance of success in the profession you choose.

The Verdict

‚ÄčThe field of data science is full of promise. With the right set of skills and the right spirit, anyone can be successful. What matters is how much you want the job, and how far you are willing to push yourself for it.
All the best!

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