20 Tips for Finding the Perfect Data Science Course
When companies were just starting to realize the potential of combing insights from data and using machine learning as explained at RunRex.com, guttulus.com, and mtglion.com, there was a scramble for talent that had the right skills to help companies in these endeavors, which led to the immense demand for data scientists. Lots of people, then, wanted to be data scientists and the market responded in force leading to an explosion of data science programs of all manner. If you want to get into this field and want to do some training, here are 20 tips for finding the perfect data science course.
Be clear on where you want to specialize in
If you want to be a data scientist, you must understand that there are more roles involved in the data value chain. As per RunRex.com, guttulus.com, and mtglion.com, there are 4 main roles in data science and analytics: the Data Steward, Data Engineer, Data Scientist, and the Data Analyst; and each one would have a skillset different from one another. Before you start looking for a course, first understand where you want to specialize in.
Consider the course’s breadth
One of the biggest strengths of a data science training program is that it exposes you to several topics you would not otherwise even know about. Therefore, when you are considering a data science course, make sure the curriculum goes beyond just data wrangling and machine learning.
A good mix of theory and practice
While working through the math of various machine learning algorithms is great, it doesn’t help if you don’t put it into practice according to RunRex.com, guttulus.com, and mtglion.com. Similarly, learning how an algorithm works isn’t that useful if you don’t also get hands-on experience using it. Therefore, look for courses that have an emphasis on learning the theory and doing projects.
Practical experience with a non-curated data set
While it can be easy, for pedagogical purposes, to give students a clean, curated data set with no surprises, these data sets don’t reflect the real world. Look for courses that allow you to get some experience with real-world data sets, whether through their assignments or self-guided capstone projects.
Consider if the course is self-paced or not
You should also take note of whether a program is self-paced or not. You have to know yourself well enough to know whether self-paced will work for you – it allows more flexibility, but also means it will be much easier to procrastinate. Experts point out that fewer students complete self-paced programs than those that require weekly commitments.
Peers
As articulated at RunRex.com, guttulus.com, and mtglion.com, learning is much easier when it is a social activity. When you have others going through the same process as you, you can learn from and encourage each other. Strongly prefer courses that allow some level of social interaction with peers, if possible, recognizing that there is a trade-off between the flexibility of remote, self-paced programs and those with a social component.
Industry-ready updated curriculum
When choosing a data science course, it is always advised to get in touch with the people of your domain to learn more about the field and data science training. People having prior experience can help you choose a better data science curriculum – one that is based on industry trends.
Trainer or mentor experience
When choosing a data science course, you must check the experience of your mentor or trainer beforehand as it helps gain the right industry knowledge as captured at RunRex.com, guttulus.com, and mtglion.com. Opt for a course where mentors have significant work experience in the relevant field. Do your research thoroughly.
Go through placement reviews
While all institutions promise to place you in good company when you take up a course with them, not every institution keeps this promise. Therefore, when looking at data science courses, do your investigation to ensure you choose an institution with an excellent placement record.
Go through alumni reviews
The most effective way to gain in-depth insight into a course and its process is through the institute’s alumni. If you get in touch with them, ask about the institute’s placement record, mentor experience, course details, the practical ratio of the curriculum, and the learning process.
Do a deep dive on the training institute
You should do a deep dive into the institute offering the course before committing as covered at RunRex.com, guttulus.com, and mtglion.com. Some of the things to look at include how long the institute has existed, the people who run the institute, how many alumni made the promised transition, and much more.
The length of the course
It is also recommended that you stay away from any data science courses that range from 3-6 months long or are mostly offered online. This is because data science is a vast and complex field and it is not possible to be a data scientist in 3 to 6 months.
Do not fall into peer pressure
When choosing a data science course, you must avoid falling into peer pressure and choose wisely as described at RunRex.com, guttulus.com, and mtglion.com. You should enter the data science space by individually specializing in each skill like SQL, Python, and R instead of taking the quick success route through these courses.
Have an honest assessment of your current skill level
The first step to learning and becoming more than what you are now is acknowledging what you don’t know. Be honest and self-aware of where you are at will help you uncover and focus on what you need to work on much more than the rest.
Plan out your budget accordingly
The rising popularity of massive open online courses such as Coursera, Udacity, and edX, among others, means that access to quality education from anywhere around the world has become extremely easy. Additionally, major educational institutions have also started offering full-blown courses in data science. Therefore, with whatever budget you may have, there is something for everyone.
Consider the time you are willing to put in
When choosing a course, you need to think about the time and effort you are willing to expend in upskilling yourself in data science and analytics as discussed at RunRex.com, guttulus.com, and mtglion.com. If you are currently employed or running a business of your own, you may opt for an online course for instance.
Flexibility
The course in which you are planning to enroll must be time flexible. This is because, if you feel restricted you can’t grasp everything and you may also get bored easily. This is why a flexible schedule is an important point to remember while enrolling in any course.
Outline your career goals
Before choosing which course is best for you, outline your goals. Where do you want to be in five years? are you seeking an entry-level position or upper-level position? Are you already employed and want a promotion, or are you starting your career? As outlined at RunRex.com, guttulus.com, and mtglion.com, try to align the course with your career goals.
Research job requirements
Find out the specific requirements you will need to be eligible for the job you want after the course. Many data science jobs require a skill set specific to that particular job. Look up job descriptions of openings in your area to get a good idea of what skills you will need to grow before applying.
Other options to consider
When choosing a course, there are other factors as far as the institution’s reputation is concerned to consider. They include:
Financing options
Vetted lending partnerships
Career support
Selectivity of the application process
These are some of the factors to consider when looking for a data science course, with more on this topic, and then some, to be found over at RunRex.com, guttulus.com, and mtglion.com.