9 Nov · 5 min read
Data analysis is one of the most in-demand fields in the twenty-first century, with people creating more and more data every day and propagating their footprint on social media and internet data analysis and stuff like Big data have become important. Many people believe that data analysis is a hard field to crack, a hard field to work in and master, however, there is still hope for you. It may be a hard field to master, but it is not a hard thing to learn. In this article, we will look at a whole three-month journey you can embark on to create a career in this field. We will break down the skills you need each month, so you have a general idea of how to embark on the journey.
While nobody can become the best at Data Analytics in three months, some people have dedicated their lives to the field, so it is going to be a journey for you, but these three months will give you enough skill in the field that you will be able to start, the last step is what comes after the three months, you keep doing that and then you have a set career for yourself in Data analysis.
You need to learn what data analysis is first before you even start to go to the technology stacks that are available for analysis. Foundational knowledge is quite necessary for all this, you create a basic skill set and then you build upon that, if it is not there you are doing something that’s just fruitless and won’t get you anywhere in the field
This exercise might take you two to four weeks depending on the amount of work you have to put in, a math major may not need any foundational knowledge, as most things already coincide with what they know. If not you have to spend some time learning the basics of it, you learn a few things about what data analysis is, you start there and then build on things like statistical techniques, and mathematical modeling stuff like this which require only conceptual clarity. You get those concepts before you move on to learning the technology stacks.
You have spent 4 weeks learning the basics of Data analysis, you have the concepts down for the statistical and mathematical aspects of data analysis. Then you focus on the technical aspect, you are now good enough to solve the problem on a piece of paper, now it is time to create proficiency in the technical aspect, the one which is going to get you hired, these skills will include:
Now here’s where things become a little complicated, there's no way you will be able to master R or Python along with SQL in 3 months, also while being able to create dashboards for the data you are working with. It is impossible, however, you’re not supposed to do all these things perfectly. It is 3 months, nobody is expecting you to deal with petabytes of data and generate world-changing insights from it in three months, the goal here is to become a data analyst in 3 months, one that can do basic tasks easily, so you focus on that, and not let other things distract you.
So the first step for you is to get your hands dirty with SQL, a lot of people don’t start there, but believe us, when we say it is the basics of every data application/ analysis work being done in the world. SQL is necessary to extract and transform the data that you are analyzing, and it is also not that hard. First, for 2 weeks you focus on SQL, the basics of SQL will only take you about 2 days to learn, the scope of the language itself is not that broad, but it requires a lot of skill to do things with it.
There are boot camps on websites like Coursera, and Udemy for SQL, these are generally 4-6 weeks courses, you enroll in them and start learning. The first two weeks are all you need to go through first in this journey. They build the basics for you to move on to the next step, but you should come back and finish the course because you will need the additional skills afterward in more professional settings. SQL will also help you in understanding a lot about data so you would be able to get more insights into how to clean the data with SQL as well as other tools during this time.
These two things will be the focus of your next 4 to six weeks, both Python and R are programming languages that have immense power, and you can have the power to do a lot of things, if you would just learn them, aside from data analysis applications. A safe bet would be to learn Python as it can help you in a lot of other areas as well, like web development, software development, IoT, etc. if you don’t see yourself becoming a data analyst, you will have the skill to utilize it somewhere else.
Just like SQL, there are Python for Data Science and R for Data Science courses available on Coursera and Udemy, you enroll in those, these courses will help you develop the necessary basis you need for Python or R in the field of data analysis. A six-week course for Python or R in data science would be enough for you to learn most things needed to get started, all the other stuff is about practice. Visualization is also key here, with Python you can use Jupyter notebook to visualize the data, and that is what you will learn in the courses as well, similar to R, R studio has integrated libraries for data visualization.
Once you have gone through the 12-week three-step Bootcamp you would be able to do minor data analysis exercises on your own by now. From there it is all about practice, you can either practice on your own, or join a company that will help you practice and learns while on the job. The skills you have gathered up till now are good enough for you to land an entry-level job. You practice with smaller data sets at first and then you build onto that.
Data analysis just like any other skill comes with practice, so you need to practice a lot. While we have highlighted a few ways through which you could become adept at data analysis work in 3 months, a disclaimer should be given that it is not enough time. It is enough to get started, which most of you are looking for, it will set you up very well for your career in data analysis. But if you’re thinking you’ll be good at it once done within the three months? I am sorry but that’s not going to happen.