What is Data Science and why should I care?
The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was called the “sexiest job of the 21st century” by Harvard Business Review. Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.
Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can be used to guide decision making and strategic planning. Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.
Which technical skills would someone need, in order to be a data scientist?
The most important skills data scientists need are technical skills, such as maneuvering and wrangling massive amounts of data to make sense of it all. But there is also a need for interpersonal skills, since data scientists work collaboratively with business analysts and data analysts to conduct analysis and communicate their findings with stakeholders.
Programming languages, such as Python or R, are necessary for data scientists to sort, analyze, manage and manipulate large amounts of data. As a data scientist just starting out, one should know the basic concepts of data science and begin familiarizing oneself with how to use Python, R, SQL or SAS.
A must have skill for data scientist is to write high-quality machine learning models and algorithms, so you need to learn statistics and probability. For machine learning, it is essential to use statistical analysis concepts like linear regression. Data scientists need to be able to collect, interpret, organize, and present data, and to fully comprehend concepts like mean, median, mode, variance, and standard deviation. They need what probability distributions, over and under-sampling, Bayesian and frequentist statistics or dimension reduction are.
Another ability you have to get under your belt is data wrangling, the process of cleaning and organizing complex data sets to make them easier to access and analyze. Manipulating the data to categorize it by patterns and trends, and to correct and input data values can be time-consuming but necessary to make data-driven decisions. This is also related to understanding database management—you’re expected to extract data from different sources and transform it into a suitable format for query and analysis, and then load it into a data warehouse system. You need to be comfortable around database management tools such as MySQL, MongoDB or Oracle.
After these, you’ll want to immerse yourself in machine learning and deep learning. Incorporating these techniques helps you improve as a data scientist because you’ll be able to gather and synthesize data more efficiently, while also predicting the outcomes of future datasets. For example, you can forecast how many clients your company will have based on the previous month’s data using linear regression. Later on, you can boost your knowledge to include more sophisticated models like Random Forest.
Not only do you need to know how to analyze, organize, and categorize data, but you’ll also want to build your skills in data visualization. It is absolutely vital to use tools such as tableau, PowerBi or Microsoft Excel. Being able to create charts and graphs is important to being a data scientist. With strong visualization skills, you can present your work to stakeholders so that the data tells a compelling story of the business insights.
Is that all?
You might think that learning everything mentioned above must surely be enough. Well you are wrong! You’ll also want to develop workplace skills such as communication in order to form strong working relationships with your team members and be able to present your findings to stakeholders. Just as data visualization is important for communicating the data insights you uncover as a data scientist, so is being able to collaborate with teams successfully.
So, while trying to get your technical skills up, do not sleep on improving your active listening, public speaking, sharing feedback, leadership or even empathy.
But are all these efforts worth it?
These days, data science is in high demand. A data scientist's position is the one with the fastest growth. The number of jobs in this area is expected to grow to 27.9% by 2026, according to the US Bureau of Labor Statistics. I’m sure it does not surprise you to know that only a select few people possess the abilities needed for a position in data science. Consequently, compared to other IT sector employment, data science jobs are less saturated.
Now is data science a good career? Of course. Data science is a fantastic career with a ton of potential for future growth. Already, there is a lot of demand, competitive pay, and several benefits. Companies are actively looking for data scientists that can glean valuable information from massive amounts of data.
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