How to become a data analyst in 2020

The modern world is currently at its peak in terms of technology and technological advancement. There is very little left that is not fully or partially technology driven. Even the most basic human tasks such as bathing have also become to a large extent technology driven and the very basis of technology leading the world towards a sustainable future is a simple 4 letter word; a word we have all studied in elementary computer science and I bet we all took it as the least important concept in the world of computers. So, do you want to know what is this word? Are you ready for it? Wait. Drum rolls. The answer is the data. Yes; this is the same data that we used to define as “pieces of facts and figures that are not processed and make no sense.” But did any of us ever think that this unprocessed information would be leading technological pioneers in the not-so-distant future? Of course not.

But the big question here is how something so insignificant attests to such a meteoric rise! It was 2012 and the world of big data was starting to gain traction. As the internet has become relatively cheap, more and more people have connected to the world’s largest network, resulting in an exponential increase in the data generated by the world’s population and even the biggest IT giants have had a hard time. hard to cope. such data streams flowing into their servers; difficult in terms of storage and processing. This is where Big Data came in force to offer its services through its plethora of tools and applications that offered pragmatic solutions to the aforementioned problems. As Big Data gained a foothold in the industry with almost every IT company using its features, a new model or trend, as you may know, was discovered. Processing customer data, companies and tech giants have come to the solid conclusion that the same data holds answers to many of their problems. But the problem with this discovery was that the dataset was too large to be analyzed at one time. Another problem was how to filter out the relevant pieces of these colossal data stacks, and furthermore, what to do with the results.

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The same problems have led to a whole different field of computer study to deal with the same. This field is what we now call “data science”. Data science, as the name suggests, is all about data; in fact, this is the same data that we discussed recently. Data science as a concept is to use large sets of customer data to find a pattern of behavior according to the needs of the business and then use the discovered patterns to solve the particular business problem.

What does it take to be a data analyst?

In today’s tech world, being a data analyst is the most rewarding job both in terms of growth and money. But the grass is always greener on the other side. On paper and in theory, becoming a data analyst seems like too easy a task and to be quite honest taking a data analysis course and calling yourself a data scientist is actually easy. But what differentiates a good data analyst from a mediocre one is the mastery and mastery of the different tools and applications that a data scientist uses on a daily basis. So to become a data analyst not just for the name, but to become such that you become a standard by yourself in the industry, here are some of the requirements that you should be affiliated with from the inside and out. outside:

Programming language

As mentioned earlier, data science is all about finding logic and patterns under a mountain of data. Sifting through this “mountain” is simply not possible through human labor alone, and the only logical answer to derive these models is to look to the powerful computers of today. But even computers cannot function on their own! Even they need a set of instructions on which they can act accordingly and achieve the appropriate results.

These sets of instructions are given to computers using a piece of code written in a high level language. Currently, the most powerful and advanced languages ​​for designing models for data science and handling the sophisticated level of statistics involved in data science include Python, R / R-Studio, Java, SQL, MATLAB, etc. Of these languages, the most popular Python is among data analysts because of its dynamic behavior and a huge range of powerful libraries that perform even the most complex calculations in a jiffy.

Statistics and aptitude

Data science designs models and writes code later and logical reasoning, math, and numbers before. The problem with data science projects is that they are all unique in their own way, and the purpose of each is also separate, which means that for the same dataset, 2 projects different require 2 different approaches and to design these 2 different approaches, distinct numbers and digits in the dataset have to be looked at from a whole different point of view and that is to look at things from a different perspective, a A data analyst must constantly think outside the box, which can only be made possible when the brain has been trained to think in this way.


Data is stored in databases (or in data centers depending on current technological needs) and to continuously process such datasets, a data analyst must have the basic concepts of database management at hand. To work and communicate with the data provided, a data analyst must be proficient in certain database languages ​​(such as SQL, NoSQL, Swift, C #, etc.) and perform the desired analyzes.

Another reason data analysts need to be great with databases is that continuously retrieving and modifying data, and then writing the changes to the physical database, takes both time and effort. Energy and data analysts are also responsible for making this process efficient. (time and energy).

Machine / Deep Learning and AI

Machine learning can be thought of as a subset of data science. It is the emerging and fastest growing technology in the modern world and offers services that can make your job much easier by automating a given task to its fullest. As the name suggests, Machine Learning is about machines that learn something. This learning is again made possible by code written in programming languages ​​and if the designed model works as it is supposed to, it can make your job easier, more efficient and more precise than big data and cloud solutions. .

Data visualization

Once a project is completed using the models that have been designed, algorithms that have been used and all other elements necessary for the successful execution of a project. However, there is a major hurdle in the end, which is to make your customers and end users understand the results and conclusions of your work. But what’s the obstacle here, you may ask. Well, the barrier is that you are a data analyst, but not your customers. They don’t understand what your template means or what your code is trying to convey. They can only understand the results if they are transmitted in a human readable format. This is where data visualization comes in. Using tools like Excel, data analysts should view the project conclusion as bar charts, pie charts, etc. in an accessible format to understand trends and patterns identified in the data. .

Data gathering

Before a data analyst can begin their job, there is an important task to accomplish. The data on which the analysis needs to be performed is very large, as we have discussed many times. But what we haven’t noticed is the randomness and the lack of structure in the same. These 2 factors make reading and understanding data more difficult than it already is. Putting this raw data into a format for the purpose of making it more valuable is called data tampering.

Sean N. Ayres