The volume of information we encounter daily through various media is vast in this age of easy access to high-speed networks, but the reality is that most of what we encounter has a lot of clutter added to it. People readily skim through several sources of information without confirming whether or not the source is reliable. Buzzwords that are completely unrelated to the issue are frequently employed to draw attention to the (mis)information, which frequently leads to reader confusion. Under the guise of buzzwords, this muddled information and deception have a complex effect on a reader’s decision-making skills, leading them down a path that is quite different from what they planned. This tendency may also be seen in the field of Data Science at the moment.
Data Science has been surrounded by a lot of myths and misunderstandings as it has evolved into the most demanding type of “talk of the town” profession from where it was a decade ago. We at Algoscale expose and debunk a few myths, which are relevant to both data scientists and organizations. These might be useful for those who want to be a part of it in the future.
#1 Myth: Data science is only for statisticians and mathematicians.
Reality: Data scientists are better when they are multidisciplinary experts with a desire to learn new things.
To begin, we’d like you to understand that Data Science is not limited to a few disciplines. It can be compared to a large square in the heart of a bustling city, through which roads from many disciplines like Mathematics, Statistics, Computer Science and Programming, Data Modeling, Visualization, Technology, Domain Knowledge, and so on pass.
While a statistician or mathematician may have a leg up, cross-disciplinary professionals have the advantage of working through several areas in tandem as a result of their previous experiences.
There is a lot to learn and explore in Data Science, which has grown dramatically in a short period of time. What distinguishes a skilled data scientist is the desire to learn more and the capacity to manipulate events in one’s favor in order to achieve a certain goal. You must keep up with the quick speed of change in this field in order to gain a competitive advantage over your competitors.
Overall, Data Science will be a cakewalk for you if you have the appropriate aim to learn, explore, and excel.
#2 Myth: The second myth is that data science is a science.
Reality is an amalgamation of art and science.
It may appear at first glance that data science is all about applying the scientific approach to tackle practical challenges in the commercial world. The issues could be something like this: “What steps can we take to reduce customer churn by 50%?” or “How much of our inventory loss is attributable to fraud, and what can we do to cut it down?”
It’s vital to remember that knowing about statistical learning or machine learning approaches isn’t enough to answer these concerns. You’ll also require a variety of skills, expertise, and a certain level of logic, reasoning, and storytelling (yes, you read it correctly!!!) abilities.
This is where data science distinguishes itself as a practice rather than a single ability.
A data science project has a lifespan, similar to software development. When writing code to collect and clean data, running traditional statistical analysis to verify that your data can answer a given question, building predictive machine learning models, visualizing the data in creative and expressive ways, and building a data story to explain the results to clients who are eager to know what you’ve discovered, the science aspect comes through.
The art aspect of data sciences, on the other hand, shines from the start through your creative problem-solving thinking and continues in many ways when you confidently weigh the subjective benefits of a decision against the quantitative benefits of techniques to make the best decision based on your experience. The decision could be related to the statistical tool you use, the output format that specific organizations prefer, or the underlying assumptions you make when solving a business challenge.
The ability to turn subjective logic and creative reasoning into a real result using statistical and machine learning methods demonstrates that Data science is a mix of art and science.
#3 Myth: Finding Data Science talent for your company is difficult.
Reality: Anyone can learn to be a skilled Data Scientist and use what they’ve learned.
Every data scientist, whether fresh or experienced, qualified or uncertified, learns on the job. This also applies to persons having a Ph.D. in Mathematics or Statistics.
Outside of the organization, resources have always been sparse or expensive. Identifying cross-disciplinary expertise with an analytical bent and helping them master data science methodologies by offering adequate tools would be a sensible option for an organization instead of wasting time and money on hiring external talent. Looking at focused and disciplined software development teams is a good place to start. These teams specialize in delivering value-added business solutions, so repurposing one to focus on data science would not be an unreasonable request.
In 3-6 months, one can become a data scientist with well-structured and hands-on learning modules and guidance from a community of aspiring data scientists. More information is available here.
It’s vital to remember that data scientists interact with departments on a regular basis. A pre-existing team would have established the essential rapport to get over the inherent bureaucracy in all departments and move work along quickly. Furthermore, your existing staff will have an easier time appreciating the breadth and depth of the business environment than a new one.
One method to develop a strong data science talent pool is to look within.
#4 Myth: Data Science entails a lot of complicated coding and the use of various tools.
Reality: Data science is all about figuring out how to solve problems.
It would be beneficial if you had some good coding abilities, but it is not a need. What matters most is your capacity to turn a business problem into actionable insights, collect and comprehend solid data, and so on. Coding becomes a minor part of your overall adventure, and you may get by with beginner to intermediate coding skills.
Though a data scientist must have hard skills such as statistics and coding, his day-to-day job also requires less tangible hard skills such as the ability to look at data and understand bias, problem-solving with messy data, which is often created by third parties, validating findings, working in a team, and effectively communicating results in simpler terms.
Data Science will be gratifying if you enjoy playing with data, asking and answering critical questions, and translating your discoveries into a data story.
We hope that the above points have helped to clear the air and make data science more understandable.
