I Don’t Like Math. Is Data Science For Me?
If math and statistics are not your strong suit, data science may nevertheless be a worthwhile option for you if you are willing to spend time studying fundamental maths principles.
An important point to keep in mind is that having a solid understanding of math is essential for being a successful data scientist. Mathematics is the foundation of all data science, as Practicum mentions. Even though math is usually necessary for any data science job, there are some sectors that make use of it to a greater extent.
An illustration of this is that data researchers working in higher educational institutions tend to pursue “theoretical” data science, which requires greater focus on mathematics. Professionals in the industry typically utilize “real-world” data science that does not require as much depth of mathematical understanding.
Often, you merely need to be aware of the means to exploit particular data science instruments, with no requirement to recognize all the mathematics for data science encompassing those instruments.
If you have previously studied mathematics at a high school level or are open to devoting effort to absorbing essential principles, being “poor” at math or not liking math should not be such a huge challenge. Keep in mind that it’s always possible to improve your math skills and grow to enjoy it.
Anton Eremin, Product Lead of data science programs at Practicum, advises not to turn away from a career in data science because of not having top-notch mathematical abilities. In order to be successful at most entry-level data science jobs, it is necessary to have a good grasp of a reasonable number of complex mathematical concepts. It won’t take long to master the amount needed as there is just a couple of months’ worth of theory and then extensive practice in Practicum during the program.
If you absolutely despise math and don’t want anything to do with numbers or equations, you should think carefully about going into this profession. In conclusion, math is a crucial part of Data Science!
Eremin suggests that math will not just be something that is learned during your academic years, but rather something that will stay relevant in your professional life and need to be continually developed.
How Is Math Used in Data Science?
Math is employed in data science to detect patterns in data, apply and construct algorithms, perform computations, construct predictions models, manipulate data, develop a more detailed comprehension of data and a great deal more.
What amount of mathematics is actually employed in data science now that you recognize its significance?
Depending on the role, you will likely need to know the following data science math skills:
- Statistics and probability: Very important in data science for making estimates and predictions from data. Also essential for correctly applying algorithms. This is the type of math that is arguably the most important for data science, and in most cases, you’ll need to know this type of math most intimately.
- Linear algebra: A mathematical discipline that involves vectors, matrices, and transformations. Many data science models are implemented using linear algebra.
- Calculus: Sounds scary, but you don’t need to know everything about calculus for data science. Understanding the key principles is often enough. Calculus is used in data science to calculate derivatives, minimize sums of squares, create algorithms, etc.
How to Learn Math for Data Science
What math skills are required for data science and how can they be acquired? What sequence should you use to acquire data science mathematical abilities?
It is important to not just focus on learning the theory of statistics before attempting any actual data science projects. Taking a gradual, step-by-step approach can be disheartening and too much mathematics for some.
Instead, try a top-down learning approach. Start off by getting into coding, comprehend the prime principles of data science, complete some enjoyable projects, then the mathematics will become more understandable in the correct context. According to Professor Jo Boaler from Stanford, students are most successful in learning math when they work on problems that they find enjoyable, instead of activities and drills that they find intimidating.
When it comes to grasping the mathematics associated with data science, you can begin your journey by enrolling in separate classes in areas such as linear algebra, used statistics, likelihood theory, and calculus. Examine the option of taking an online class or enrolling in a course at a local community college if you would rather learn in a face-to-face setting.
Skills Needed to Break into Data Science
So, we can agree that mathematics is a crucial part of data science, but what else do you need to be aware of to pursue data science as a career? Foundational data science skills include:
- Programming languages: The main data science coding languages to know include Python or R, and SQL
- Data visualization: Displaying data in graphs, charts, etc.
- Machine learning: Building smart machines and algorithms to help process data and learn as they go. Check out these 13 machine learning courses, and learn relevant libraries such as Scikit-Learn, XGBoost, PyTorch, and LightGBM
- Exploratory data analysis: Performing initial scans to detect patterns in data
- Data preprocessing: How to collect and clean data, handling missing and duplicate values, changing data types, etc.
It is essential to possess the soft skills necessary to excel in the job, such as a desire to learn since the workplace is ever-changing, excellent communication skills, being able to work well with others, etc. To obtain an amazing job, it is important to be someone colleagues enjoy working with!
Where Do Data Scientists Work and What’s the Environment Like?
Data scientists can be found in an assortment of offices and organizations, ranging in size from small startups to vast corporations, and spanning numerous sectors such as banking, automotive, tech, healthcare, retail and more. Consequently, this provides a wide range of work atmospheres and cultures. Many of the nation’s data scientists can be found in the leading technology centers – Silicon Valley, New York, Seattle, and Boston – but in the fight for talent, data scientists are sometimes afforded the option to work from a location other than the workplace.
What you are employed by can significantly change the function you take on as a data scientist. For instance, certain businesses have specific data science groups to provide expert advice and help the various business units comprehend the data and formulate effective models in alignment with the business plan. In that job, you might need to talk to various people who are invested in the situation, examine scenarios in which the product might be used, explain which way is the best, and afterward return to your workspace to construct the model before delivering it for implementation. In a different situation, in a colossal technology corporation, you would probably be part of a highly specialised team, who would work at enhancing certain components of models to accomplish a single job. It is important when interviewing with different businesses to inquire about the common duties related to that particular job, as these can vary considerably.
Can Data Scientists Work Remotely?
Heck yes. Data scientists need a computer and access to data. That’s about it. It is possible to work remotely with the necessary things. In practice, it all depends on the company. Organizations are promoting job openings that can be done remotely in order to attract data scientists. Others prefer in-person brainstorming sessions. No matter if you want to work from a distance or in person, you absolutely have the option to do so.
What Hours Do Data Scientists Keep?
The working hours for data scientists typically fall between 8 in the morning and 5 in the evening from Monday to Friday. Organizations are starting to offer a heightened degree of flexibility for their employees, providing them with the autonomy to set their own working hours, within a designated core period (typically from 10 AM to 3 PM). Typically, data scientists labor 40-50 hours per week and usually have a significant amount of control over their operations. Nair states that she typically has a 9 AM to 5 PM work schedule, but she is fortunate to have employers who are open to her having flexibility. “No one is micromanaging how or when I work.”
What Are the Job Prospects Like for Data Scientists?
Since 2012, the position of data scientist has been highly praised and sought after. It has been almost 10 years, and one may be wondering if the market has become, or will become, oversaturated. The answer is no. According to the U.S. According to the Bureau of Labor Statistics, data science is estimated to have a 31% surge in activity between 2019 and 2029, ranking as one of the most quickly developing fields. Data Science is thought to have an expansion rate that is almost eight times that of the normal rate of 4% for all professions.
How Much Do Data Scientists Make?
Data science is an ever-expanding profession and is one of the most lucrative roles on the job market. Statistics released by the Bureau of Labor Statistics indicate that the average pay for data scientists in 2020 stood at $103,930. As you acquire more experience, your earning potential will increase. As of May 2021, PayScale’s compensation data indicated that the wages for data scientists varied from $67,000 to $135,000, while senior data scientists were paid between $95,000 and $161,000, data science managers earned between $96,000 and $180,000, and the wages of data science directors ranged from $117,000 to $203,000. The job placement firm Burtch Works released research indicating that directors of data science receive a median salary of approximately $250,000 annually.
How Can You Become a Data Scientist?
Most data scientists have at least a bachelor’s degree, and many have advanced degrees such as master’s degrees or PhDs. It has been reported by 365Data Science that 8 out of 10 data scientists possess an advanced degree. Many jobs in the field of data science require one to have an advanced degree, usually a master’s.
However, you don’t need a specific “data science” degree. Individuals with various backgrounds may become data scientists, like having a Doctorate in Neuroscience or a Bachelor’s Degree in Mathematics.
Generally, there are a few routes to becoming a data scientist:
- Complete a B.S. degree in computer science, computer engineering, statistics, or mathematics. Learn typical data science frameworks (Python, PySpark, AWS, Azure, etc.) as part of your undergraduate coursework or through side projects. Get a job as a data scientist.
- Complete a BS degree in any field. Complete a data science bootcamp (there are many). You may be able to get a job as a data scientist right away, or you may start as a data analyst and work your way up.
- Complete an MS degree in data science, computer science, or related field. Learn typical data science frameworks within your program. Get a job as a data scientist.
- Complete your PhD degree in almost any subject area that involves analyzing data (bioinformatics, political science, astrophysics, etc.). While completing your PhD, learn typical data science frameworks—you’ll need them to analyze your data—and try for internships to learn “real world skills.” Upon completing your program, get a job as a data scientist.
Do you see a pattern here? It is imperative that you find a way to acquaint yourself with coding and resolving issues with the use of data. It is possible that one’s educational background may determine the degree of difficulty when attempting to enter the data science field. From my personal information, those who have degrees in computer science or mathematics are more highly valued than those who do not come from STEM fields, thus those people probably need to have more experience compared to those with STEM degrees to make up for the discrepancy.
People may say they’re an expert in data science, but to prove it, they must demonstrate the relevant talents and background. That’s where specific degrees or bootcamps come into play. As demand increases, educational institutions and businesses are starting to provide an abundance of opportunities to attain data science qualifications. This includes distant Master’s courses, nanodegrees, boot camps, plus certifications like Amazon Web Services or IBM. When deciding which path to pursue, be sure to inquire about potential employment options and professional connections.
Irrespective of your origin, having an online portfolio on GitHub could be of benefit to demonstrate how you have utilized your abilities to actualize specific projects.