Data Science Today
In 2012, Harvard Business Review dubbed "data scientist" to be the sexiest job of the 21st century. Though it is still one of the hottest job titles, the battle to fill in the lacunae is still fierce. Those that have failed to implement data science into their developments may be putting themselves at a competitive disadvantage, as several organizations are now turning to data-driven cultures.
With technologies producing larger data sets, organizations are now facing challenges in unlocking the value of data. As a result, the demand for data scientist skills is limitless. Looking at the current crunch for data science talent and the shortage of supply, the U.S. itself projects to have a shortfall of 250,000 data scientists by 2024. To fill in the data science and analytics job roles, it generally takes around 45 days, which is longer than the average duration in the U.S. job market. Owing to this reason, filling in job roles, which are taking so much time, might affect projects and deliverables causing hindrances to enterprises’ efforts. However, due to the number of trends that are coming into effect, it has changed the way online education platforms are coming up with strategies and structured learning paths to alleviate the data science talent bottleneck.
Data is present everywhere. Almost every industry and business are making use of data one way or another. But there’s a huge mismatch with the demand and supply of tech experts with data scientist skills; the major reason that led to the mushrooming of several online data science certificate programs. With so many programs to choose from, and the providers available online, the quality of the content may not be consistent. In such cases, it is important for the candidate to choose the program that best fits their requirements. Checking the credibility and the accreditation of the certification body is mandatory.
Select The Right Career Path
To get into data science, the candidate must learn the right skills by selecting a relevant career path. Due to the deficit of data science talents, organizations are now attracting candidates by offering huge salary perks. Today upskilling is the key to landing a job in the data science field. Mid-career techies are reskilling themselves, and this trend has been followed for quite some time now and will continue for a while. Since the skill gaps are huge, it is not mandatory for the candidate to get a degree in data science itself. Although earning a degree in data science is an added advantage, it is not mandatory.
The battle for talent can be won by taking up online certification programs that are proven valuable by the job market. Learning data science could easily be done by learning from some of the best industry experts; a few are Hortonworks, Data Science Council of America (DASCA), and Coursera. These certification bodies are deemed to have some of the best data science certifications and are recognized globally.
Prerequisites To Learning Data Science And Your Current Expertise
As a beginner in data science, you must first understand that data science itself is a vast field. In simple terms, data science is a multidisciplinary field that uses data, algorithms, and technology to solve analytically complex problems. You can say it is doing most of the things that involve data to further solve problems and later come out with business value or growth.
You can start with the basics first; for instance, statistics, programming in R and Python. Learning algorithms should be at your fingertips, if you’re prepping yourself to work on huge amounts of data, you must ensure your concepts in statistics and machine learning are at your fingertips. Other skills such as machine learning, data visualization, predictive modeling, deployment and extensive knowledge in databases will help you land a career in data science faster than you could have expected.
Besides this, ensure you have built your portfolio of projects to demonstrate your skills to potential employers. As a fresher, you’d need to have information in your mind, build projects once you have learned these skills, and, only then will you become a successful data scientist.
