The demand for Data Science professionals is now at an all-time high. There are companies in virtually every industry looking to extract the most value from the heaps of information generated on a daily basis.
With the trend for Data Science catching up like never before, organizations are making complete use of their internal data assets to further examine the integration of hundreds of third-party data sources. What is crucial here is the role of the data scientists.
Not very long back, the teams playing the key role of working on the data always found their places in the back rooms of multifold IT organizations. The teams though sitting on the backseat would help in steering the various corporate systems with the required data that acted as the fuel to keep the activities running. The critical database tasks performed by the teams responsible allowed corporate executives to report on operations activities and deliver financial results.
When you take up a career in Data Science, your previous experience or skills do not matter. As a matter of fact, you would need a whole new range of skills to pursue a career in Data Science. Below are the skills required to become a top dog in Data Science.
Data scientists are expected to have knowledge and expertise in the following domains:
The areas arch over dozens of languages, frameworks, and technologies that data scientists need to learn. Data scientists should always have the curiosity to amass more knowledge in their domain so that they stay relevant in this dynamic field.
The world of Data Science demands certain important attributes and skills, according to IT leaders, industry analysts, data scientists, and others.
A majority of Data scientists already have a Master’s degree. If Master’s degree does not quench their thirst for more degrees, some even go on to acquire PhD degrees. Mind you, there are exceptions too. It isn’t mandatory that you should be an expert in a particular subject to become a Data Scientist. You could become one even with a qualification in Computer Science, Physical Sciences, Natural Sciences, Statistics or even Social Sciences. However, a degree in Mathematics and Statistics is always an added benefit for enhanced understanding of the concepts.
Qualifying with a degree is not the end of the requirements. Brush up your skills by taking online lessons in a special skill set of your choice — get certified on how to use Hadoop, Big Data or R. You can also choose to enroll yourself for a Postgraduate degree in the field of Data Science, Mathematics or any other related field.
Remember, learning does not end with earning a degree or certification. You need to practice what you learned — blog and share your knowledge, build an app and explore other avenues and applications of data.
The Data Scientists of the modern world have a major role to play in businesses across the globe. They have the ability to extract useful insights from vast amounts of raw data using sophisticated techniques. The business acumen of the Data Scientists help a big deal in predicting what lies ahead for enterprises. The models that the Data Scientists create also bring out measures to mitigate potential threats if any.
As a Data Scientist, you may have to face challenges while working on projects and finding solutions to problems.
If you are a Data Scientist, you are expected not just to study the data and identify the right tools and techniques; you need to have your answers ready to all the questions that come across while you are strategizing on working on a solution with or without a business model.
Organizations vouch for candidates with strong business acumen. As a Data Scientist, you are expected to showcase your skills in a way that will make the organization stand one step ahead of the competition. Undertaking a project and working on it is not the end of the path scaled by you. You need to understand and be able to make others understand how your business models influence business outcomes and how the outcomes will prove beneficial to the organization.
And a Data Scientist is expected to be adept at coding too. You may encounter technical issues where you need to sit and work on codes. If you know how to code, it will make you further versatile in confidently assisting your team.
The world does not expect Data Scientists to be perfect with knowledge of all domains. However, it is always assumed that a Data Scientist has know-how of various industrial operations. Reading helps as a plus point. You can gain knowledge in various domains by reading the resources online.
To be a successful Data Scientist, you should be able to explain the problem you are faced with to figure out a solution to the problem and share it with the relevant stakeholders. You need to create a difference in the way you explain without leaving any communication gaps.
Let us now understand the important skills to become an expert Data Scientist – all the skills that go in, to become one. The skills are as follows:
Data scientists need to keep their brains racing with critical thinking. They should be able to apply the objective analysis of facts when faced with a complex problem. Upon reaching a logical analysis, a data scientist should formulate opinions or render judgments.
Data scientists are counted upon for their understanding of complex business problems and the risks involved with decision-making. Before they plunge into the process of analysis and decision-making, data scientists are required to come up with a 'model' or 'abstract' on what is critical to coming up with the solution to a problem. Data scientists should be able to determine the factors that are extraneous and can be ignored while churning out a solution to a complex business problem.
According to Jeffry Nimeroff, CIO at Zeta Global, which provides a cloud-based marketing platform – A data scientist needs to have experience but also have the ability to suspend belief...
Before arriving at a solution, it is very important for a Data Scientist to be very clear on what is being expected and if the expected solution can be arrived at. It is only with experience that your intuition works stronger. Experience brings in benefits.
If you are a novice and a problem is posed in front of you; all that the one who put the problem in front of you would get is a wide-eyed expression, perhaps. Instead, if you have hands-on experience of working with complex problems no matter what, you will step back, look behind at your experience, draw some inference from multiple points of view and try assessing the problem that is put forth.
In simple steps, critical thinking involves the following steps:
a. Describe the problem posed in front of you.
b. Analyse the arguments involved – The IFs and BUTs.
c. Evaluate the significance of the decisions being made and the successes or failures thereafter.
Handling a complex task might at times call for the execution of a chain of programming tasks. So, if you are a data scientist, you should know how to go about writing code. It does not stop at just writing the code; the code should be executable and should be crucial in helping you find a solution to a complex business problem.
In the present scenario, Data Scientists are more inclined towards learning and becoming an expert with Python as the language of choice. There is a substantial crowd following R as well. Scala, Clojure, Java and Octave are a few other languages that find prominence too.
Consider the following aspects to be a successful Data Scientist that can dab with programming skills –
a) You need to deal with humongous volumes of data.
b) Working with real-time data should be like a cakewalk for you.
c) You need to hop around cloud computing and work your way with statistical models like the ones shown below:
Different Statistical Models
Data scientists are expected to understand and have the ability to code in a bundle of languages – Python, C++ or Java.
Gaining the knack to code helps Data Scientists; however, this is not the end requirement. A Data Scientist can always be surrounded by people who code.
If you have never liked Mathematics as a subject or are not proficient in Mathematics, Data Science is probably not the right career choice for you.
You might own an organization or you might even be representing it; the fact is while you engage with your clients, you might have to look into many disparate issues. To deal with the issues that lay in front of you, you will be required to develop complex financial or operational models. To finally be able to build a worthy model, you will end up pulling chunks from large volumes of data. This is where Mathematics helps you.
If you have the expertise in Mathematics, building statistical models is easier. Statistical models further help in developing or switching over to key business strategies. With skills in both Mathematics and Statistics, you can get moving in the world of Data Science. Spell the mantra of Mathematics and Statistics onto your lamp of Data Science, lo and behold you can be the genie giving way to the best solutions to the most complex problems.
Data Science overlaps with the fields of Machine Learning, Deep Learning and AI.
There is an increase in the way we work with computers, we now have enhanced connectivity; a large amount of data is being collected and industries make use of this data and are moving extremely fast.
AI and deep learning may not show up in the requirements of job postings; yet, if you have AI and deep learning skills, you end up eating the big pie.
A data scientist needs to be hawk-eyed and alert to the changes in the curve while research is in progress to come up with the best methodology to a problem. Coming up with a model might not be the end. A Data Scientist must be clear as to when to apply which practice to solve a problem without making it more complex.
Data scientists need to understand the depth of problems before finding solutions. A data scientist need not go elsewhere to study the problems; all that is there in the data fetched is what is needed to bring out the best solution.
A data scientist should be aware of the computational costs involved in building an environment and the following system boundary conditions:
|Studying a customer can act as a major plus point for both a data scientist and an organization… This helps in understanding what technology to apply.|
No matter how generations advance with the use of automated tools and open source is readily available, statistical skills are considered the much-needed add-ons for a data scientist.
Understanding statistics is not an easy job; a data scientist needs to be competent to comprehend the assumptions made by the various tools and software.
Experts have put forth a few important requisites for data scientists to make the best use of their models:
Data scientists need to be handy with proper data interpretation techniques and ought to understand –
a. the various functional interfaces to the machine learning algorithms
b. the statistics within the methods
If you are a data scientist, try dabbing your profile with colours of computer science skills. You must be proficient in working with the keyboard and have a sound knowledge of fundamentals in software engineering.
Communication and technology show a cycle of operations wherein, there is an integration between people, applications, systems, and data. Data science does not stand separate in this. Working with Data Science is no different. As a Data Scientist, you should be able to communicate with various stakeholders. Data plays a key attribute in the wheel of communication.
Communication in Data Science ropes in the ‘storytelling’ ability. This helps you translate a solution you have arrived at into action or intervention that you have put in the pipeline. As a Data Scientist, you should be adept at knitting with the data you have extracted and communicated it clearly to your stakeholders.
The benefits of data
The technology and the computational costs involved in the process of extracting and making use of the data
The challenges posed in the form of data quality, privacy, and confidentiality
A Data Scientist also needs to keep an eye on the wide horizons for better prospects. The organization can be shown a map highlighting other areas of interest that can prove beneficial.
If you are a Data Scientist with different feathers in your cap, one being that of a good communicator, you should be able to change a complex form of technical information to a simple and compact form before you present it to the various stakeholders. The information should highlight the challenges, the details of the data, the criteria for success and the anticipated results.
If you want to excel in the field of Data Science, you must have an inquisitive bent of mind. The more you ask questions, the more information you gather, the easier it is to come up with paramount business models.
Let us draw some inference from the construction of a building and the role of an architect. Architects have the most knowledge of how the different blocks of buildings can go together and how the different pillars for a block make a strong support system. Like how architects manage and coordinate the entire construction process, so do the Data Scientists while building business models.
A Data Scientist needs to understand all that happens to the data from the inception level to when it becomes a model and further until a decision is made based on the model.
Not understanding the data architecture can have a tremendous impact on the assumptions made in the process and the decisions arrived at. If a Data Scientist is not familiar with the data architecture, it may lead to the organization taking wrong decisions leading to unexpected and unfavourable results.
A slight change within the architecture might lead to situations getting worse for all the involved stakeholders.
A Data Scientist with sharp business acumen should have the ability to analyse business risks, suggest improvements if any and facilitate further changes in various business processes. As a Data Scientist, you should understand how systems engineering works.
If you want to be a Data Scientist and have sharp risk analysis, process improvement and systems engineering skills, you can set yourself for a smooth sail in this vast sea of Data Science.
You will no more be a Data Scientist if you stop following scientific theories… After all, Data Science in itself is a major breakthrough in the field of Science.
It is always recommended to analyse all the risks that may confront a business before embarking on a journey of model development. This helps in mitigating risks that an organization may have to encounter later. For a smooth business flow, a Data Scientist should also have the nature to probe into the strategies of the various stakeholders and the problems encountered by customers.
A Data Scientist should be able to get the picture of the prevailing risks or the various systems that can have a whopping impact on the data or if a model can lead to positive fruition in the form of customer satisfaction.
Data scientists are not very different when compared to the commoners. We can say this on the lines of problem-solving. The problem solving traits are inherent in every human being. What makes a data scientist stand apart is very good problem-solving skills. We come across complex problems even in everyday situations. How we differ in solving problems is in the perspectives that we apply. Understanding and analyzing before moving on to actually solving the problems by pulling out all the tools in practice is what Data Scientists are good at.
The approach that a Data Scientist takes to solve a problem reaps more success than failure. With their approach, they bring critical thinking to the forefront.
Finding a Data Scientist with skill sets at variance is a problem faced by most of the employers.
When the employers are on a hunt to trap the best, they look out for specialization in languages, libraries, and expertise in tech tools. If a candidate comes in with experience, it helps in boosting the profile.
Let us see some very important technical skills:
Let us briefly understand how these languages are in demand.
Python is one of the most in-demand languages. This has gained immense popularity as an open-source language. It is widely used both by beginners and experts. Data Scientists need to have Python as one of the primary languages in their kit.
R is altogether a new programming language for statisticians. Anyone with a mathematical bent of mind can learn it. Nevertheless, if you do not appreciate the nuances of Mathematics then it’s difficult to understand R. This never means that you cannot learn it, but without having that mathematical creativity, you cannot harness the power of it.
Structured Query Language or SQL is also highly in demand. The language helps in interacting with relational databases. Though it is not of much prominence yet, with a know-how in SQL you can gain a stand in the job market.
Both Hadoop and Spark are open source tools from Apache for big data.
Apache Hadoop is an open source software platform. Apache Hadoop helps when you have large data sets on computer clusters built from commodity hardware and you find it difficult to store and process the data sets.
Apache Spark is a lightning-fast cluster computing and data processing engine designed for fast computation. It comes with a bunch of development APIs. It supports data workers with efficient execution of streaming, machine learning or SQL workloads.
We also have Java and SAS joining the league of languages. These are in-demand languages by large players. Employers offer whopping packages to candidates with expertise in Java and SAS.
Tableau joins the list as an analytics platform and visualization tool. The tool is powerful and user-friendly. The public version of the tool is available for free. If you wish to keep your data private, you have to consider the costs involved too.
Let us see the in-demand skill set for a Data Scientist in brief.
a. A Data Scientist should have the acumen to handle data processing and go about setting models that will help various business processes.
b. A Data Scientist should understand the depth of a business problem and the structure of the data that will be used in the process of solving it.
c. A Data Scientist should always be ready with an explanation on how the created business models work; even the minute details count.
A majority of the crowd out there is good at Maths, Statistics, Engineering or other related subjects. However, when interviewed, they may not show the required traits and when recruited may fail to shine in their performance levels. Sometimes the recruitment process to hire a Data Scientist gets so tedious that employers end up searching with lanterns even in broad daylight. Further, the graphical representation below shows some smart tips for smart Data Scientists.
Let us now throw some light into what employers seek the most from Data Scientists:
a. A strong sense of analysis
b. Machine learning is at the core of what is sought from Data Scientists.
c. A Data Scientist should infer and refer to data that has been in practice and will be in practice.
d. Data Scientists are expected to be adept at Machine Learning and create models predicting the performance on the basis of demand.
e. And, a big NOD to a combo skill set of statistics, Computer Science and Mathematics.
Following screenshot shows the requirements of a topnotch employer from a Data Scientist. The requirements were posted on a jobs’ listing website.
Let us do a sneak peek into the same job-listing website and see the skills in demand for a Data Scientist.
What are some general recommendations for Data Scientists in the present scenario? Let us walk you through a few.
If you are wearing the hat of a Python expert, you must also have the know-how of common python data science libraries – numpy, pandas, matplotlib, and scikit-learn.
Data Science is all about contributing more data to the technologically advanced world. Make your online presence a worthy one; learn while you earn.
Start by browsing through online portals. If you are a professional, make your mark on LinkedIn. Securing a job through LinkedIn is now easier than scouring through job sites.
Demonstrate all the skills that you are good at on the social portals you are associated with. Suppose you write an article on LinkedIn, do not refrain from sharing the link to the article on your Facebook account.
Most important of all – when faced with a complex situation, understand why and what led to the problem. A deeper understanding of a problem will help you come up with the best model. The more you empathize with a situation, the more will be your success count. And in no time, you can become that extraordinary whiz in Data Science.
Wishing you immense success if you happen to choose or have already chosen Data Science as the path for your career.
All the best for your career endeavour!