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 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!
Job hunting can be a challenging task for many people, yet we all need to go through that process in order to build a career. A large proportion of the most desirable jobs on the job market right now are jobs related to analytics, like data scientists, data engineers, or even a data analyst.
As these jobs like being a data scientist become more and more desirable, they can become more and more competitive. Competitive job markets mean that the most skilled people are often the most employable. Employers are looking for data scientists that can tackle any problems thrown at them. So how does one actually get a job as a data scientist?
When it comes to getting a job as a data scientist, many people do not know where to start. The path to building a great career as a data scientist does not need to be complicated. Here are 7 actionable tips on how to get a job as a data scientist.
1. Know The Most Important Skills
Data scientists are a blend of a programmer, statistician, software engineer, and many more rolled into one. A data scientist needs to be able to run a project from start to finish. As such, a person who wants to get a job as a data scientist needs to have a versatile skill set in order to do the job competently.
Having a strong skill set is something that employers can put to good use. Knowing the most important skills within data science and analytics is the first thing any prospective data scientist should have down. Some of the most important skills for becoming a data scientist are:
Knowing these skills and being able to use them effectively are core components of getting a job as a data scientist. If you do not feel competent quite yet in your ability to competently use any of the above skills, then try focusing on upskilling, which is also our next tip.
2. Keep Learning
In data science, you have to stay on top of skills development in order to stay ahead in your field. The field of data science and analytics is always adapting and the problems change each time. As a result, upskilling and honing your skillset is essential to building a career as a data scientist.
Building real industry knowledge through practice in educational resources can do a long way. Having a strong technical foundation in analytics is something that can be built in the comfort of your own home.
Make Use Of Educational Resources
Knowing the basics is not going to cut it if you want to get a job as a data scientist. In-depth knowledge and problem-solving skills are needed to succeed in the analytics field. Making use of educational resources like online exercises, boot camps, and modules can go a long way in mastering analytics skills.
Trying out exercises, reading case studies, and doing tutorials like those from Strata Scratch can go a long way in keeping you on top of your game. Continuous learning is necessary to stay abreast of the analytics field, so take time to keep learning if you want to get a job as a data scientist.
3. Build Up Your Communication Skills
Getting a job as a data scientist is not only about having a strong analytical toolset, soft skills like communication are crucial too. Be able to describe how you would solve a problem and why you chose that route to a solution is a critical part of being a data scientist.
Data scientists need to be able to communicate each step of a project and the reasoning behind it. Try making notes of your thoughts as you solve a problem or tackle a project so that you can learn to explain each step to others in the future.
4. Practice Makes Perfect
Landing the perfect job when building a career as a data scientist takes time. Behind every successful data scientists is a large number of job applications and several interviews. The fact of the matter is that getting a job as a data scientist takes time and effort.
Stay positive, learn from the positions you did not land, and learn from the interviews that did not go as smoothly as you had hoped. Eventually, practise will make perfect and you will land a job as a data scientist.
Getting a job as a data scientist is not only about having the strongest skill set, it is also about meeting people within the industry who may help guide you to a great job. Making use of social networking sites like LinkedIn and attending industry meetups can go a long way in landing you your dream position as a data scientist.
6. Build A Portfolio
Employers come across hundreds or even thousands of CVs claiming that that candidate is capable of doing what they need, but not every job candidate has much to show for it. It is well and good to say that you are able to do something, but showing that you can do it and more will go a lot further.
Building a portfolio of past work and project shows an employer that you are competent and capable and have something to show for it. Standing out with a diverse and interesting portfolio can be the defining part of your application that will get you a job as a data scientist.
Bonus: all of the learning you’ve done on the side can go a long way in building up a portfolio. Save your best solutions to problems and exercises done on Strata Scratch to beef up your portfolio.
7. Find A Mentor
On top of regular networking, one of the best tips you can follow for getting a job as a data scientist is to find a mentor. A mentor can guide you through projects and educational resources and can even help you figure out exactly what employers are looking for in a data scientist candidate.