A Step By Step Guide For Data Analytics Enthusiasts
The internet has made it incredibly easy to access information and even easier to get lost in the avalanche of options. Every search result is a door that may or may not lead you anywhere but all of them will take some of your time, the only currency that really matters. So I will cut the philosophy right there and try to help you find the stepping stones that will help you cross the moat and enter castle de analytics.
Some questions you should ask yourself
Data analytics is an expansive and to an extent elusive field. You should be looking for something or else you might end up finding nothing. So, first thing first. Why do you want to learn data analytics? What end do you want to meet?
We will move forward with three probable answers to this.
- You are an entrepreneur who wants to make use of data for his or her own company.
- You are a student who has read enough about a career in analytics to find it interesting.
- You are a professional who is looking for a change, a break.
If you belong to the first category, your task is both easy and hard. Easy because you will know how to approach your problem, what tools to learn and how much time to invest. Hard because you will have to find out the problems, and then bet a fair amount of money on yourself. However, this is the category I love the most because the possibilities are infinite. Let us suppose you own a small manufacturing unit and want to streamline the processes. A simple visualization tool might suffice for you. If you want to make the most of the data you will have to start with creating a robust data pipeline, hence get into a little data mining. Then you would want to add some flair to your marketing, so, marketing analytics is on.
Now, for the second category, the whole world is wide open. You guys need not worry much about anything. But there is a very simple method that you can use to choose the right educational path. I will come to that shortly.
Professionals usually develop a sense of what is required for their growth. Your desire to grow will guide you but that does not mean this article is abandoning you. I will catch you in a moment.
The background conundrum
A poet, a filmmaker, a chef and an architect, go to a bar and start a conversation. All of them are pretty upset because none of them seems to get the recognition he wants. They are pretty good at what they do, then why can’0t they get enough traffic? They are divided by backgrounds and professions but united by their lack of data eccentricity.
The poet can run a survey on what it is that people are reading, the parameters can be anything from theme to meter. An architect can use some algorithms to take some AI assistance in reviving his creativity. The chef can run his restaurant data through some filters to find out more about his visitors than just what they are ordering the most. A filmmaker can literally use AI for pretty much everything.
The reason I took up the painstaking task of narrating this is to establish one simple fact that people tend to miss. Data analytics can help each and every enterprise in some way or the other, then why should a data analytics certification course be only for the STEM people? It should not and it is not. Be mindful of where you come from, use that information to chalk out a path for yourself but never treat it like a disability.
Choosing the course
You have to make two choices. First the course and then the institute. Do not topple this order. Now, that you know your goal as well as what you know, you can choose a suitable analytics course with the help of some quora posts, reddit discussions, posts such as this, and some friends.
When it comes to choosing the institute, check these three parameters and in this order.
- The curriculum: It should have enough scope for practical experience and some sort of evaluative programme. Also make sure that the certificate they provide is well recognized.
- The faculty: Despite having tonnes of online material you cannot really replace a good, experienced teacher. Do not forget to go through the faculty profiles. Some industry experience under their belt cannot hurt.
- The price: Do not start with this but do not ignore it either. It is a professional investment that you are making.
Read more about why to learn data analytics.
Remember your career will not depend upon one course. But the first one surely does matter because it gives you direction.
