Data science is currently one of the hottest topics of the 21st century. Everyone is fascinated by data. Data science has quickly grown to be a multi-billion dollar industry. Data scientists use the data they’ve collected to identify patterns and use it to predict future outcomes. Companies greatly benefit by transforming these predictive models into valuable insights that help them hit all their goals.
As the world immerses itself more and more with technology and the virtual world, data science jobs are quickly becoming one of the most in-demand careers. The pay for these data scientists is not something to laugh at too. According to a 2020 Burtch-Works study, the salary of data scientists are as follows:
- Entry-level data scientist: The median starting salary for an entry-level data scientist is a whopping $95 000 or £69400
- Mid-level data scientist: The median starting salary for a mid-level data scientist ranges from $130 000 to $195 000 or £95000 to £142 000
- Experienced data scientist: The median starting salary for an experienced data scientist ranges from $165 000 or £120 000 and up.
Is data science sounding more and more interesting for you? Don’t worry. It’s not yet too late for you to switch fields. Even if you’re already established and a total beginner, you can still start a career in data science. The area is relatively young, and there are no gatekeepers that stop people from jumping aboard.
Additionally, you don’t necessarily have to switch completely. However, updating your CV and having some data science skills under your belt will help you in the long run. This also helps keep you a top prospect and asset to your company.
Two Ways on How You Can Learn Data Science Effectively
Now that we’ve established that data science is worth your time, here I have listed down the two ways on how you can learn Data Science effectively:
1. Online Courses
The most common way for established professionals to learn data science is through online courses. Taking an online class allows you the flexibility to study at your own pace during your free time. You get to insert it in between breaks instead of having to uproot your entire life to go back to school.
Additionally, online courses are cost-effective. Usually, Coursera, Udemy, and Shaw Academy go on sales, so you can definitely wait for a discounted course. This way, you don’t break the bank learning a few extra skills for work.
By taking online courses, you also get to select the specific skills you want to work on. You won’t be spending time and money on classes that you deem unnecessary.
2. Traditional College Route
As Data Science grows more in popularity, traditional universities are now starting to offer Data Science programs. It can be a full degree or just a specialization course. The benefit of “going back to college” per se is to enroll under a more rigid system. Online courses aren’t 100% effective. Some people learn better inside the four walls of a classroom. Getting some face-to-face with a professor to ask questions in real-time also helps students digest the lessons better.
Courses To Take For a Data Science Crash Course
Wondering which courses to take? I’ve listed some of the best courses that you can take as a crash course to Data Science.
1. Python and SQL
Python and Structured Query Language (SQL) are two of the important languages extensively used in data science. Start with the basics so you can easily understand the more advanced fields.
Python is an interpreted, high-level, and general-purpose programming language. It emphasizes code readability with its notable use of significant whitespace. On the other hand, SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system or for stream processing in a relational data stream management system.
2. Linear Algebra
Start getting used to math because it’s here to stay. You may have taken this way back in High School, but it’s best to take a refresher course. Linear Algebra discusses linear equations and their representations in vector spaces and through matrices. This will significantly help you with data science.
3. Machine Learning
Machine learning is a data analysis method that enables you to automate analytical model building. I recommend taking the first two courses first, so you grasp the topic before jumping straight to Machine Learning.
Machine Learning is a branch of AI that develops systems that learn from data sets, identify patterns, and automatically make decisions without a human controlling them.
Data Science is a complex field, but it is deemed to hold the future of companies worldwide. There is an ongoing agreement that Data Science is here to stay. You might as well jump on the bandwagon instead of becoming the outdated guy in the office.