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With the massive amount of attention that data science is getting in the past years, many people are asking themselves whether they should choose a data science major.
Data science is a good major. A data scientist’s job is irreplaceable, and an expert in the field would stand out on any occasion. With rising competition in every industry, companies seek expert data scientists to help achieve tangible and measurable profits in their business models.
Data science is broad, and many of the developing education programs aim to create a curriculum that resonates with the industry’s needs and deliver in terms of quality, breadth, and depth of the data science knowledge and skill. You may wonder how hard it is to learn data science or the jobs a data scientist does.
This article looks at these and more to give you the information you need before choosing to major in data science.
Math, statistics, and substantial knowledge in computer programming characterizes data science. To most people, that’s hard!
Math has never had many friends. That explains the global shortage of data scientists and the increasing demand for skills in this field.
The technical requirements and the different programming languages and applications you must learn to make data science more challenging to learn than other technology fields.
Most technological disciplines allow you to learn at least one programming language, and you’re good to go.
But, with data science, you’ll need to combine the knowledge of several languages, thanks to its interdisciplinary nature.
Some of the programming languages you should learn as a data scientist are:
- R – It’s one of the most advanced data analysis software tools, with nearly every data visualization and statistical analysis application for any data analysis need. With so many features, it’s a little different from other software programs and, therefore, more tasking to learn.
- Python – Preferred by most scientists because of its accessibility, versatility, and a pretty manageable learning curve. In your quest to learn data science, start with Python.
- SQL – Like Python, SQL is a must-have for any data scientist. It’ll take you three weeks, at most, to have a good understanding of how it works. Its readable and intuitive nature makes it easy to update, edit, manipulate, and extract useful information from extensive data.
- Java – It’s less complex than C++ but more brutal than Python. It’ll take you a month plus to learn the basics of Java and an additional two weeks to practice the ideas. Once you’ve mastered the verbose syntax and its applications, you’ll seamlessly apply it for most data science statistical analysis.
- Scala – It’s the ideal programming software when dealing with a considerable volume of data. It’s flexible and user-friendly, but its learning curve is steeper than the other programs. It’ll take you several weeks to familiarize yourself with it, but its usefulness in large-scale machine learning and complex algorithms makes an effort worth it.
- MATLAB – Its usefulness extends into the academic field because of its ability to solve high-level mathematical and statistical problems like matrix algebra, Fourier transforms, image processing, and signal processing. You can learn it in about two weeks.
You may not need to use all these every day, but you’ll need to be familiar with each of them for you to have an easy time in your practice.
Complex as all these sound, a strong background in computer science, statistics, and math can make everything a bit easier.
The first language you learn will always be the hardest. Once you have one or two under your belt, you’ll be able to learn other languages at a faster pace.
Consider learning data science as a long-term investment that instills skill for a lifetime, not just in learning information but in improving on the already gained skills.
For example, you can improve your coding skills by learning a new programming language.
A data scientist is needed in almost every industry. With a degree, you can get a job in these positions.
- Data Analyst – They acquire information through surveys, analyze and interpret it, then use it to extract useful information on the specific topic. An entry-level data analyst earns about $61 329 as a basic salary per year.
- Data Engineer – They develop and translate computer algorithms into executable prototype codes. They also recognize trends in extensive data, as well as maintain and organize it. An entry-level data engineer earns about $92 428 per year as a basic salary.
- Data Scientist – They analyze data using complex algorithms to extract valuable information to improve profitability in the business model. An entry-level data scientist earns about $96 491 as a basic salary per year.
- Machine Learning Engineer – They solve software problems, create data funnels, and design and test machine learning software systems to improve their performance. An entry-level machine learning engineer earns about $112 595 as a basic salary per year.
- Statistician – They use data to identify relationships and trends that can help the organization make decisions and plan future strategies for the business. An entry-level statistician earns a basic salary of $76 884 per year.
- Data Architect – They create blueprints used in managing data to ensure databases are easy to integrate, centralize, protect. Data engineers rely on data architects for the best working tools and systems. An entry-level data architect earns about $108,278 as a basic salary per year.
From high-level government institutions to primary dating sites, every field requires a data scientist’s expertise. Therefore, data scientists are in heavy demand.
Even with automation, professionals who understand business models and can use data to identify a problem, find a solution, and implement it, are needed everywhere.
If you’re wondering why there is such high demand, here are some reasons why.
- Pilled-up data in companies – Since the IT boom in the early 2000s, many businesses shifted their base to online where more people could access it. Their online presence has led to a massive increase in data creation and collection, and therefore, data keeps piling. Companies need help organizing this data to make sense of it.
- Inadequate skills – Unfortunately, data science is not a field embraced by many people, so talent is scarce. The technicality of the area makes it a tough choice for most people.
- A plethora of roles – Qualified data scientists can work in different capacities in a business setting. For example, one can work as a data analyst, data engineer, machine learning engineer, or database administrator.
- Many acceptable job entries – A company will quickly choose to hire someone with a background in data science over someone with no such knowledge. This makes it easy for data scientists to get jobs in any industry.
With the growing technology and the use of AI to collect data, it’s clear that the demand for data scientists will continue to spiral.
According to BLS, the occupation of a data scientist gets a median wage of $98,230 per year. The pay differs according to the position held, and the bonuses given in a company.
Experience is one of the most important factors in determining a data scientist’s salary, according to O’Reilly’s 2016 Data Science Salary Survey. With every year of experience, data science professionals make anywhere between $2,000 and $2,500 more.
Based on a study of data science salaries conducted by Burtch-Works in 2020, the following trends were found:
- Junior Data scientist salary: the median starting salary is around $95,000.
- Mid-Level Data scientist salary: the median salary is between $130,000 and $195,000 depending on the company.
- Senior Data scientist salary: the median salary is ranging from $165,000 to $250,000 depending on the company.
If you follow the hype around the money data scientists make, chances are you’ll not get the proper training, or by the time you complete training, there’ll be thousands of people competing with you for a similar position, and the only way to stand out is to know your stuff.