I don’t care who you are; you have fifteen minutes a day that you can devote to learning something new. You may be under-the-gun, pressed-for-time due to one deadline or another. But, fifteen minutes every day isn’t going to cause you to miss those deadlines.
Now that I have freed up fifteen minutes per day for you, it should be simple to start learning, right? Unfortunately, you need to commit to learning something specific. If you spend your fifteen minutes trying to figure out what to learn, you’ll waste that time and get nothing accomplished. You will also pass off this article as hokum. Of course, you may end up doing that anyway! Ah, such is life!
Data science requires learning, and in many cases, it doesn’t matter what level you are at in your data science career progression. It is a field that requires constant learning. But, you can’t take a course her or there casually. You need to have a plan, and you need to follow through with that plan.
I am not going to suggest a plan for you in this article. The coverage of such a topic would take up at least a few articles in length, perhaps more. Besides, there are plenty of legitimate resources between websites and forums that will help you define what you need to know.
Here is one suggestion I will give in this article. When you find qualified resources that suggest what skills you need to acquire or have, you should list the skills on a sheet of paper. Use several of these websites to fill in the gaps concerning skills. When you have a good selection of skills, rate where you stand with each of the skill sets. If you are an expert in statistics, then this would receive a higher rating than programming in Python, if you aren’t good at that, etc.
It wouldn’t be the worst idea to find a few senior people in the data science field to run the list by them. Ask them if the list is complete and what they would add or subtract from it. This is terrific insight that can reduce needless learning of skills. All education is good, but if you have an objective to get into data science, you’ll want to streamline your learning efforts.
As I have stated in other articles, you should follow several job-posting websites that list data science positions. More of these websites are listing jobs in this field. But, the objective here is to find out what skills companies are looking for. Add these skills to your master list and reevaluate where you stand concerning each.
You will find several resources for potential interview questions. The benefit of having these questions, at least in theory, is you’ll be prepared for any interview you attend. I was on the fence as to whether to include this section or not. I believe there is value to reviewing interview questions employers may be asking. However, you cannot just memorize the answers and expect every potential interviewer to ask the same questions. That won’t happen. They may ask similar questions. But then again, they may ask questions that are not on the list at all. Will you be ready for that?
Interviewers aren’t stupid (most of them, anyway!) They are aware that sets of canned interview questions circulate internet and have downloaded these questions themselves. The interviewers will review the questions and will likely avoid asking them due to what I like to call a knowledge bias. They want to test your thinking ability in the context of data science. They don’t want people who can rattle off answers to predetermined questions. They have machine learning algorithms for that! I have been down this path before myself as an interviewer. These tricks may work in the beginning, but you catch onto them eventually.
I will state that I memorized the answers to a Java programming quiz given by Oracle and on one interview I was given the exact quiz. I aced it! But, don’t expect this to happen often. It was a fluke. In statistics terms, it was a random event. The null hypothesis was safely protected in this instance.
In case you think I don’t practice what I preach, realize that I study at least two hours per day. I am committed to learning to be the best in the data science field. Mastery of any field requires constant learning, and I have committed to making that happen.
Commitment is a strong word. But, I feel it is something that can help you with obtaining your objectives. I know that some people will counter that this is the same as goals. Perhaps. However, it works for me to list out my commitments for each month and then each week. I have a stigma with the word goals and have never been able to get them to work for me. When I plan for my commitments, this gets me motivated.
Whatever you decide to call it, i.e., commitments or goals, make the commitment to accomplish them. Put on your list any learning you’ll need to do to reach our objectives. Get specific as to the courses you will take.
At this point, you may feel that going to college for a degree will satisfy this learning commitment. It will. However, not everyone has $50,000+ to spare to earn that degree. Further, if you think finding 15 minutes is tough, wait until you have to spend three or four days a week in a classroom setting followed by hours of homework and assignments.
I am not knocking a formal education in data science. In fact, it will be structured and will likely be a viable way to get your foot in the door. Most schools have placement programs to help students make connections. Several schools also have their reputation behind them. That speaks volumes when it comes to looking for work in any field.
Many schools are now offering online access to the classes, which is helpful. However, there isn’t much of a cost reduction with this path. You can expect to spend tens of thousands of dollars. One Master’s program I recently evaluated projected a cost of $62,000 for the program.
Even enrolling in a formal program will require a commitment on your part. Be prepared to give up your evenings and weekends. Data science is a demanding field. It’s not like you can read a few chapters and expect to ace any test you are given. They’ll be plenty of project work that will consume your time. The most significant advantage of formal education is the structure. You don’t get that when setting out on your own. You need to define the structure of your self-teaching.
One thing is sure; if you genuinely want to work in data science, you’ll need a commitment of some sort. You will need to learn what you need to learn, which is tricky. Then, you will need to learn what you learned you needed to learn. Most other fields have well-defined paths, for the most part. The field of data science is evolving rapidly.
While the field is wide open, it’s closing fast, and it’s becoming competitive quickly. There are no shortcuts, and you will have to carve out a place for yourself to rise above that competition.
Click on the button below to learn how you can get started in this exciting field called Data Science. This resource has a structured program and is quickly becoming the de facto standard. Edx.org uses this engine for their training, which is huge. You can get started right now!
James is a data science writer who has several years' experience in writing and technology. He helps others who are trying to break into the technology field like data science. If this is something you've been trying to do, you've come to the right place. You'll find resources to help you accomplish this.