Data Science

With the fast development of modern technologies, data science is currently in extremely high demand, and this is only going to grow. For verification, just type “data science jobs” into Google or search for them in any job hunting website such as LinkedIn, Glassdoor, or Indeed. You will be overwhelmed by the number of job opportunities in this sphere.

There are many explanations for such a popularity. The amount of data produced around the world is rapidly accumulating every day, and every business requires data analyzing and predictive modeling to remain alive and successful in today’s highly competitive market. Scientific research in any field can be conducted only if enough historical data is collected. In other words, the more data an organization or science has gathered, the more reliable data-derived forecasts it can make.

That said, as with any other sphere, there were (and are) various “fashionable” trends in data science in different periods of its existence: machine learning, deep learning, data engineering, big data, and even Covid-19 data science.

Broadly speaking, data scientists gather and investigate the data relevant to a certain business or scientific task, and extract from it meaningful insights and hidden trends. Using machine learning and deep learning algorithms to build predictive models, they then create reports of their findings and communicate their results to non-technical shareholders. In turn, shareholders can then make strategic, data-driven decisions to improve the business. 

All these steps require data scientists to be multi-skilled professionals. In particular, they should have sufficient knowledge of coding tools, be familiar with the mathematical principles behind various machine learning algorithms, understand the nuances of the business domain of a particular sphere of interest, follow data ethics, and have excellent communication skills so as to clearly explain complex ideas to a non-technical audience.

The above is an integral description of a classical data scientist role. Since this profession is relatively new however, different companies can have their own understanding of what the role of a data scientist should include. For example, in some cases, data scientists are rather data analysts focused on the investigation of the historical and current data without predicting future scenarios. In other companies, data scientists are supposed to use graphical user interface (GUI) applications to build machine learning models, so they practically don’t need to write any scripts. Finally, sometimes data scientists are meant to be data engineers whose main tasks are converting raw data to a usable form and designing and maintaining data storage infrastructure.

As with many other professions, the answer to this question strongly depends on the country where the company is located, i.e., on the standard of living. For example, according to PayScale, at the time of writing (February 2022), the average salary of a data scientist in the United States is $97,038 per year, while in India, where this profession is also very highly demanded, it amounts to ₹860,454 per year, which is equivalent to $11,521.
Another significant factor that influences the salary of a data scientist in any country is the level of their seniority. Taking the United States again as an example, a junior data scientist earns $76,213 per year while a senior data scientist – $129,446 per year, i.e., almost 2 times more (Indeed).
The salary of a data scientist also depends on the company profile (a small company or a multinational corporation), area of focus (business or academic environment), and type of contract (permanent or temporary).
You can check the average salary of a data scientist in your country and for your seniority level using specialized websites such as Indeed, PaySclae and SlaryExpert You could also research information on the average salaries of other data-related professions, such as data engineer, data analyst, and data journalist.

While it is true that for mathematicians, statisticians, and programmers, the process of learning data science could be smoother and quicker, it doesn’t necessarily mean that a career in data science is completely inaccessible to people with different qualifications. Indeed, there are plenty of inspiring stories of the success of people who have entered this sphere from completely unrelated professions, made fast progress, and are now happily employed.

However, it is not correct either to claim that there are no prerequisites at all for a person to start learning data science. To succeed in your studies, you will need to be fascinated by the data and what is hidden behind it, an exploratory mindset, a certain amount of creativity, and a high motivation to learn data science.

While there is nothing wrong with a university degree in data science, you have to keep in mind one important thing: time matters. If you have recently graduated from college and are deciding on your further education, then a solid, well-grounded university degree in data science could be a great choice. If you are a career-changer instead, you probably won’t want to spend at least two more years on your studies before being employed.

Fortunately, if you are from the second category of people, there is good news for you: you can learn data science on an online bootcamps at a sufficient level to be employed as a data scientist. Moreover, this approach gives you much more freedom to organize your learning process, manage your time, practice a lot, and accelerate your progress whenever you feel ready. 

In the world of work, it doesn’t matter how much time it takes for you to learn data science or whether or not you have a world-class certificate. What a potential employer really wants to see in a tech-competent candidate is a set of proven skills (confirmed by a portfolio of projects) relevant to a job position of interest.

The answer to this question depends on many factors, such as the way of learning you choose (book-based or video-based self-tuition, in a school, a boot camp, a master’s program, etc.), the curriculum you follow, how many hours you are ready to dedicate to learn data science, your initial background, etc. On average, to a person with no prior coding experience and/or mathematical background, it takes from 7 to 12 months of intensive studies to become an entry-level data scientist.

It is important to keep in mind that learning only the theoretical basis of data science may not make you a real data scientist. Whatever program you choose, you should pay attention to practicing your skills, making data science projects, creating your project portfolio, exploring data science use cases in various spheres, and experimenting with alternative approaches to solving the same data science task. All these activities, if conducted with diligence and persistence, can be rather time-consuming. However, this is the best way to master your data science skills and gain job-ready proficiency.

To accelerate your learning process, consider opting for an online self-study program with a well-balanced curriculum that covers the most important techniques and aspects of data science. This will help you efficiently manage your time, decide on the most comfortable and productive approach to learning the materials, and allow you to learn at your own pace from wherever you have a computer and Internet access. With Chools, you can select from fully-packed Career tracks for very beginners, specialized Skill Tracks to sharpen particular skills, and short courses to explore narrow-focused topics.

While coding is an essential skill for any data science job, expertise in programming is not mandatory to get started in this sphere. No doubt, a person who wants to land a job in data science should be familiar with certain programming languages and related technical tools, and the companies that hire data scientists usually require such skills. However, the coding toolkit of a data scientist is definitely not as extensive as that of, say, a software developer or a computer scientist. The choice of programming languages relevant to solving data science tasks is also quite limited, and learning the basic data-related methods and techniques of only one of them can be a great place to start.

Rather than being a purely programming-focused discipline, data science is a vast field of study that requires a diverse set of skills and competencies apart from coding, such as having an analytical mindset, understanding statistics, probability, linear algebra, efficient storytelling, and business domain knowledge.

There are three programming languages that are widely used in data science: Python, R, and SQL.

Python is an open-source, object-oriented, high-level programming language that was originally used for general-purpose programming in computer science, but later became very popular in data science. Its main advantages are an extensive standard library and a big collection of additional modules which are particularly helpful for solving data science tasks. In addition, Python is intuitively understandable and easy to learn and use, can be run in many operating systems, and is supported by a strong community.

R is a popular data science-oriented programming language and free software that is very powerful in statistical computing and data visualization. Just like Python, it provides many data science and machine learning libraries for solving different tasks, is operating system friendly, and has excellent online support. However, this programming language is considered to be less intuitive than Python.

While Python and R are mostly similar in their functionality, SQL (Structured Query Language) is used for a different purpose: to query relational database management systems such as tables with connected data entries. SQL has several flavors, all with a rather similar syntax; some of them are free and open source (e.g., MySQL, SQLite, PostgreSQL).

First of all, you don’t need any mathematical background to start learning data science. On the other hand, if you have decided to become a data scientist and are ready to make efforts for it, you will inevitably have to get familiar with some mathematical concepts related to data science. Apart from the very basics of math taught in a common school program, you will need knowledge of calculus, probability, statistics, and linear algebra.

However, this doesn’t mean that you must learn the above subjects from beginning to end. Moreover, the majority of math is already included in data science tools and methods, so many complex operations are just calculated by the machine under the hood based on the input parameters. A data scientist is, above all, a scientist, so he or she does have to understand how and why all those algorithms work behind the scenes to be able to select the best one, define the initial parameters, and adjust them properly. In Chools Skills tracks, Career tracks, career tracks and courses, you will be introduced on a step-by-step basis to all the necessary theory in mathematics that is applicable to solve various real-world tasks in data science.

The first place that comes to mind is free job listing websites. You can consider using both general job portals etc. (LinkedIn, Indeed, Google for Jobs, SimplyHired, AngelList, Hired,) and data science niche job boards (KDNuggets, DataJobs, Amazon Jobs, StatsJobs, etc.). There are also websites designed for searching for remote jobs : Upwork, Remote, JustRemote, We Work Remotely. You could also use specialized job boards, such as Outer Join that are dedicated to remote jobs exclusively in the data science sphere.

Apart from that, you can try to reach out to a company of interest directly. Find their official website, explore its home page, career page, and contact details. Read their values and mission, what their business looks like, and consider how you could be a perfect fit for this organization. Armed with this information, you can send them an email with your data science resume attached. This approach, while potentially more time-consuming, is more advantageous than the first one since it allows you to show a genuine interest in the company and stand out from the crowd.

To have more chance of finding a data science job quickly, it can be helpful to attend data science events and conferences (both live and online), connect with the right people on social networks, and communicate with data science professionals and learners in specialized data science communities. At Chools, you will find a friendly community of data science enthusiasts where you can get help and support and expand contacts in the world of data science.

The most basic technical skills that employers usually expect from a data scientist include:

• good command of Python or R (especially the popular data science modules of these languages)
• competence in SQL
• the ability to work with the command line
• understanding of statistical concepts,
• data cleaning, wrangling, analysis, and visualization skills
• predictive modeling and model estimation using machine learning or deep learning algorithms
• working with unstructured data
• storytelling
• web scraping
• debugging

This doesn’t mean that you would necessarily need all those skills for any data science position. To understand what each particular company wants to see in a data scientist, you should read the corresponding job description and make a list of the specific technical skills and tools they require.

As for the necessary soft skills for a data scientist, the most sought-after ones are:

• critical thinking
• team working
• business domain knowledge
• efficient communication
• decision making
• multitasking
• flexibility
• curiosity
• creativity
• ability to meet tight deadlines

The first thing is to have a prepared portfolio of projects. This is especially important for those candidates who lack real working experience in this sphere. Such a portfolio should include the projects that you completed as a part of your data science bootcamp or course. In addition, consider making 2-3 more projects that will make your portfolio unique. For an entry-level data scientist or a career-changer, it is perfectly ok if at the beginning your portfolio contains projects on mixed topics and techniques. However, when applying to a particular job position, try to figure out which of your works highlights the best of all your skills required for that job.

The next crucial point is your data science resume. Before applying for different job positions, consider creating a master version of your resume where you put all the information about your education, working experience (even if not data-related), courses, boot camps, projects, technical and soft skills, and any other achievements that could be relevant in any way. Don’t worry if this version of your resume is rather long, or is made up of multiple sections and subsections. Now, whenever you want to apply for a particular data science job, you can just use your master resume as a basis. Simply create a copy of it, remove all the redundant details and sections, and tailor your resume for that specific position according to the job description. Remember that adapting your resume for each submission is a necessary step in your job search process. If you need more tips on how to create an excellent, professional-looking data science resume, you can find this article helpful.

The third essential thing that you should be aware of is that you might not manage to find a data science job immediately. If this happens, don’t be discouraged. It is absolutely normal if your job search process takes time. Don’t let eventual rejections frustrate you and make you start thinking that you are not a good specialist. Instead, continue mastering your data science skills and try to analyze what can be adjusted in your resume, your portfolio of projects, and the application process in general. If you get some feedback from any of the companies you apply to, make the most of this information by improving on any of your highlighted weaknesses.