Best 20 Data Scientist Skills That You Need To Get Data Science Jobs
Since data took over the corporate world, data scientists are always on demand. And what makes this job much more lucrative is the shortage of highly skilled data scientists. Companies are willing to part with a lot of their revenue behind the right data scientist. However, to qualify for a position in Data Scientist at renowned organizations, you need to show them why you’re the best fit for their business. No wonder this requires supreme creativity and ton loads of the right data scientist skills.
We’ll get more specific. Why do companies prefer resumes with exceptional data science skills? After all, all they care about is revenue. The thing is data scientists are the reason global industries are reaching far more audiences than they did earlier.
It’s the job of these professionals to make meaning of random data and give them a business outlook. They can make or break a business’s global reach. So, leading corporations like the Fortune 1000 companies are always looking for people with highly professional data scientist skills.
Best Data Science Skills
Data science jobs require a diverse set of skills and mastery over critical domains like mathematics, analytics, problem-solving, and such. There’s no guaranteed set of select data scientist skills that are enough for every position. Each job requires different criteria while maintaining some simple fundamentals. Below, we’re presenting you the 20 data science skills that can help you fit in for almost any position.
Education is one of the primary factors based on which corporations screen their data scientist applicants. As much as we like to talk about how non-grads like Mark Zuckerberg or Steve Jobs have shaped the present world, we’ll all emphasize on education while asking for data scientist qualifications. You can, however, get respected positions even without a college degree given you’ve acquired exceptional data scientist skills on your own.
1. Advanced Degree
Apart from a few exceptions, most data scientists are equipped with advanced higher education degrees. According to reliable sources, about 88% of data scientists have at least a Master’s degree while 46% of them carry PhDs. It shouldn’t come as a surprise to you seeing such advanced degrees among regular data science skills.
Data science jobs are one of those few positions where advanced degrees are almost always mandatory. If you want to solidify your data scientist qualifications, we recommend you take minimum a Bachelor’s in fields such as Computer Science, Mathematics, Physical Sciences, or Statistics.
A degree in any of these disciplines will show your employers that you have a fundamental understanding of basic data scientist skills like Big Data, Statistics, Modelling, and such. If you already hold one of these degrees, we strongly suggest you consider further higher education to boost your chance into getting a top-notch Data Scientist position.
Most renowned organizations evaluate certifications pretty highly when checking the data scientist qualifications of their potential employees. Certifications come into play where Advanced degrees stop. Since there are likely to be a significant number of candidates who have at least a major in one of the disciplines mentioned above, it is only through certifications interviewers often weigh their would-be data scientists.
You can find a pretty massive list of certifications here – both online and physical, that can aid to your data science skills much more effectively. We encourage you to take on those certification exams that interests you and to learn something out of them.
Perhaps, you could use the knowledge you gained from a particular certification exam and build something productive. This way you’ll not only have to worry about getting prestigious data science jobs but also will have a competitive advantage over your fellow peers.
Critical thinking is the ability to analyze obvious facts and infer valuable information from them. This is one of the essential skills for data scientists because as a Data Scientist you’ll often work with tons of data and will be needed to model them into profitable business ideas. We often see organizations hiring people with seemingly shallow data scientist skills but possessing exceptional critical thinking abilities.
3. Problem Solving & Risk Analysis
Data scientists need to maintain great problem-solving skills as otherwise, they’re of little value to corporations. This is one of those data scientist skills that you’ll not likely be able to teach yourself. Instead, it needs to be developed from an early age and is often shaped during college. As a Data Scientist, you’re very likely to face newer problems every day.
To cope with such situations, an appetite for solving real-world problems is a must. Risk analysis is a complex topic, that, contrary to problem-solving, can be learned given you dedicate enough time to it. This is the art of calculating the risks associated with specific business models.
Since you’ll often be responsible for designing and implementing the business models for your company, the responsibility of assessing their risk factors also falls on your shoulder. Without proper risk analysis abilities, chances are you’ll screw up now or then as a Data Scientist which can easily result in getting you sacked.
4. Process Improvement
Most of the data science jobs we see nowadays requires their employees to improve legacy business processes as part of their job. It’s your role as a Data Scientist to devote yourself to finding the best possible solution to business problems and optimize them as much as you can.
Without proper critical thinking abilities and professional data scientist skills, this can become a pretty daunting task very fast. We suggest you spend a large portion of your time learning how Data Science professionals tackle this task and create your personalized approaches to process improvement.
If you can show your potential employer the ability to enhance their current business models and strategies, chances are you’ll be getting the job pretty quickly. However, if you can’t even improve on existing solutions, companies are not very likely to be convinced that you can curate future business processes.
5. Business Acumen
A solid understanding of the industry you’ll be working, and the respective business opportunities it offers is among very important skills for data scientists. Without understanding the business possibilities, it’s almost impossible to design successful business solutions.
Every organization you’ll apply for will evaluate great business ideas very positively. We often see people investing most of their time learning tools and algorithms, but very few tend to develop business ideas of their own. This is one of the primary difference between an average Data Scientist and a professional one.
Developing a high-level of business acumen aid not only your data science skills but also pose future entrepreneurial opportunities. If you can discern potential high-value business ideas and can develop working solutions, you will be able to create your personal Data Science firm quite easily. On the plus side, most data science jobs look for people like these who can get their current business growth to the next level.
Coding is the ability to make machines understand what you’re trying to achieve through it. It’s one of the must-have data science skills for any competitive data scientists. If you want to improve your data scientist skills for top-notch positions, learning the ability to program efficient solutions is a must. Below, we’ll outline the must-have programming skills you’ll require to grab top-paying data science jobs.
6. Python Programming
If you look carefully, you’ll find Python as one of the essential skills for data scientists. Python is a considerably high-level programming language that has been gaining immense popularity thanks to its empowering qualities. Python lets data scientists curate efficient and productive solutions to their everyday data science problems quite quickly.
One of the most sought-after data science skills, it’s highly unlikely for this innovative programming language to lose its charm soon. On the plus side, learning Python is one of the easiest jobs if you’ve got any earlier programming experience. Contrary to old-school programming languages such as C and Java, Python offers an easy to adopt programming scheme while making sure the learning curve is not very steep at the same time.
7. R Programming
Like Python, R is among another de-facto data scientist skills companies tend to look for in their potential employees. In-depth knowledge and mastery over this powerful programming language are preferred for most top-paying data science jobs. So, we highly recommend you learn this awe-inspiring programming language to boost your chance of getting those respected data science jobs.
Since, analyzing extensive datasets to find out potential business insights will be one of your primary tasks as a data scientist, mastery over this powerful statistical programming language is considered fundamental skills for data scientists like you. R lets you analyze business data effectively and infer solutions which have a high-level impact on business. So, it’s mandatory you boost your R programming skills today.
8. SQL Programming
For most data science jobs, having the ability to program using the SQL querying language is considered among essential data scientist skills. SQL is generally used to write scripts that carry out operations like adding, deleting and extracting data from databases. It’s one of the most critical skills for data scientists for analyzing and transforming database schemas.
If you’re already proficient in SQL from your academic studies, we suggest you build helpful tools using this. Such utilities will act as an effective portfolio for data scientist qualifications when sitting for data science jobs interview. For every data scientist, the ability to employ SQL will be counted among fundamental data science skills, as it lets them better understand relational databases and will increase their chance of getting hired.
Mastery over industry-standard analytical tools is one of the most critical data science skills needed to get those high paying data science jobs. These tools let a data scientist analyze the enormous array of daily business data and curate efficient data models to improve on present business solutions. Although a vast number of such tools are available, we’ll be touching on only the most basic ones today.
Apache Hadoop is a collection of data analytic tools that help data scientists solve problems utilizing huge datasets over network connections. This software stack provides an easy to use distributed storage framework and facilitates the processing of big data with tools such as MapReduce, SAMOA, and Cassandra. It’s essential you learn Hadoop effectively as it’s one of the most critical skills for data scientists.
Among the extensive collection of open-source data processing utilities Hadoop provides, some are much important over others. For example, Hive and Pig are two heavily used software in the industry. So, a fluent command over this software stack will be a high selling point for you in most data science jobs interview. Our experts highly recommend you boost your Hadoop knowledge as much as possible to improve your present data science skills to the highest level.
10. Apache Spark
One of the most trending big data software and tools currently, Apache Spark provides a handy cluster computing framework to boost your data scientist skills. The powerful in-memory data processing engine of the Apache Spark provides support for ETL, analytics, machine learning and graph processing for even the most extensive of business datasets. You can do both batch processing and stream processing with this powerful software.
The high-performing yet concise API support for a diverse set of open source programming languages including Scala, Python, Java, R, and SQL makes Apache Spark suitable to use in a large number of projects. If you not only want to boost your current data scientist skills but also want to add more data scientist qualifications, we strongly advise you to start learning Apache Spark from today.
11. Apache Kafka
Apache Kafka is a high-performing stream-processing software platform that lets data scientists analyze and handle business data in real-time. Learning this tool can prove to be a precious resource for your career and will increase your data scientist qualifications to the next level.
Even the mention of Kafka on your resume will serve as a strong selling point for you in most top-notch data science jobs that deal with real-time data. Since most top-notch businesses today rely on real-time data one way or the other, Kafka will come in handy in many situations.
This Apache software lets you subscribe to data streams effectively and store them in a fault-tolerant way for processing. You can create some practical projects with Kafka that building real-time data streaming pipelines or applications. This will increase both your data science skills and the chance of getting hired exponentially.
Unlike many top-paying CS jobs, most data science jobs require both a practical and theoretical knowledge of certain branches of Mathematics. It’s one of the essential data science skills you need to have for getting a respected position in top organizations. Although we won’t go into the debacle of what math skills are mandatory and whatnot, we’ll outline simple to follow guide to help you curate your math skills for everyday data scientist qualifications.
No wonder Statistics is one of the essential data scientists skills for most data science jobs. It is the branch of mathematics that deals with the collection, organization, analysis, and interpretation of data. A solid grasp over this field is mandatory to boost your chance of getting hired at a top data science company.
Amongst the diverse range of topics Statistics deals with, you’ll need to have a solid understanding of some key topics including Statistical Features, Probability Distributions, Dimensionality Reduction, Over and Under Sampling alongside Bayesian Statistics. Mastery over this area of mathematics, in general, will increase your data scientist qualifications considerably and will lead to high-paying jobs
13. Multivariable Calculus & Linear Algebra
Multivariable Calculus & Linear Algebra falls among those data science skills without whom you won’t be really able to curate modern-day business solutions. In short, Linear Algebra is the language of computer algorithms while Multivariable Calculus being the same for optimization problems.
Since, as a data scientist, your primary tasks will be to optimize large scale business data and define solutions for them in terms of programming languages, learning these branches of mathematics is mandatory.
On a side note, when you’re using Statistics or Machine Learning, what you’re merely doing is leveraging these areas of mathematics. So, we strongly urge you to focus on these mathematical fundamentals when wielding your data scientist skills for netting data science positions.
14. Machine Learning, Deep Learning, and AI
It’s not a surprise any modern-day business require their data scientists to be expert in different areas of Artificial Intelligence like Machine Learning and Deep Learning. In summary, Artificial Intelligence defines the simulation of ‘intelligent’ behavior in computers, while Machine Learning and Deep Learning refer to subfields inside AI that tries to achieve more specific behaviors by utilizing more complex methods.
If you’re surprised seeing such topics in the Mathematics section, don’t be. Given, you’ve had at least some kind of previous exposure to these innovative ideas; you should know they’re, in essence, pure mathematics. Learning the ins’ and outs’ of these advanced concepts will not only increase your data scientist skills but also help you stand out from your competitors in most data science jobs.
Although not a subfield of mathematics itself, Tensorflow is described in this section due to its relationship with advanced Machine Learning data science skills. Tensorflow is an opensource library that lets data scientists manage their dataflow and programs across a wide array of tasks. It can be thought of as a symbolic math library.
From data analysis to data validation, Tensorflow is employed for a diverse set of tasks by professional data scientists. If you want to outshine your fellow peers when it comes to rocking high-paying data science jobs, we suggest you enhance your Tensorflow skills alongside your mathematical abilities.
When looking for potential data scientists, companies often value communication skills above many technical data science skills. It’s because without fluent communication, employees are usually unable to keep up with the increasing demand organizations need to deal with. If you can show interviewers that you’ve got excellent communication skills, they might prefer you over another candidate having higher technical skills.
As a data scientist, it’s highly unlikely that you’ll be working alone. In most companies, there’ll be small to medium-sized teams that deal with a specific class of problems. Teamwork is the collaboration of multiple data scientists to take care of the business needs of your company. It’s among those essential data scientist skills without which you’ll likely to fail to make a long-lasting impression and may even lose your job.
So, when learning all those essential skills for data scientists, you should put extra emphasis on effective teamwork. Define the right ways of addressing problems to your co-workers. Teach yourself how to ask specific questions and provide feedback to increase your communication skills for data science jobs.
Documentation is the process of documenting your work in a way so that other data scientists can understand your approach to a particular problem more easily and quickly. It’s one of the most critical data science skills that will help your fellow peers to appreciate your contribution to projects.
There’s no defined way on how should you document your data science jobs. But you can learn from what others do and curate your own style. Proper Documentation will not only help others understand your solutions but also will aid you when you come back to an earlier problem after some time.
We suggest you start with simple approaches and just mark the procedures you’ve followed to obtain a solution at first. Later down the line, you may begin adding more information like why you chose a specific method, how to modify or replace it and such.
You can think of Data architecture as models or standards which governs the way you collect, store, arrange, or integrate business data. It’s one of the crucial data scientist skills for netting data science jobs with excellent salaries. If you don’t have an academic degree on either CS, Mathematics, or Statistics; you’ll need to spend considerable time learning Data architecture.
18. Data Wrangling
Data wrangling refers to the process of transforming data from one format to another. This is generally used for obtaining useful data from extensive lists of unordered, inconsistent, or messy data. Since unattainable data is of little value to organizations, it’s the task of data scientists to format them as required by the problem.
Since the amounts of data and methods for obtaining them are continually increasing, to keep up with it, you need to have a solid command over different Data wrangling techniques. Data wrangling is a must for helping you understand your data in a better way and letting your employers benefit from them. To increase your data scientist qualifications, we encourage you to start learning various Data wrangling methods right from today.
19. Data Modeling
Data modeling describes the steps in data analysis where data scientists map their data objects with others and define logical relationships between them. When working with massive unstructured datasets, often your first and foremost objective will be to build a useful conceptual data model. The various data science skills that fall under the domain of data modeling includes entity types, attributes, relationships, integrity rules, their definition among others.
This sub-field of Data architecture facilitates the interaction between designers, developers, and the administrative people of a data science company. We suggest you can build basic yet insightful Data models to showcase your data scientist skills to employers during future data science jobs interviews.
20. Data Mining
Data mining refers to methods that deal with discovering patterns in big datasets. It’s one of the most critical skills for data scientists as without proper data patterns; you won’t be able to curate appropriate business solutions with data. As data mining requires quite an intensive number of techniques including but not limited to machine learning, statistics, and database systems, we recommend readers to put great emphasis on this area for boosting their data scientist qualifications.
Although it seems to be daunting at first, once you get the hang of it, data mining can be pretty fun. To be an expert data miner, you need to master topics like clustering, regression, association rules, sequential patterns, outer detection among others. Our experts consider data mining to be one of those data scientist skills that can make or break your data science jobs interview.
As data science is a constantly evolving field with lots of improvisation and optimization done each day; it’s hard to predict what data scientist skills are enough for getting any data science jobs. However, it’s more than possible to outline some data science skills that are more than enough for even the most demanded positions.