The modern world is currently at its peak in terms of technology and technological advancements. There are very few things left that are not completely or partially technology-driven. Even the most basic of human tasks such as bathing too have become technology-oriented to quite an extent and the very basis of technology driving the world to a sustainable future is a mere 4-letter word; a word that we have all studied in elementary computer science and I bet that we all took it as the most inconsequential concept in the world of computers. So do you want to know what that word is? Are you ready for it? Wait. Drumrolls. The answer is data. Yes; this is the same data that we used to define as “pieces of facts and numbers that are unprocessed and make no sense”. But did any of us ever think that this unprocessed information would be at the helm of technological pioneers in the not so distant future; of course not.
But the big question over here is how something so inconsequential witness such a meteoric rise! The year was 2012 and the world of big data was starting to gain traction. With the Internet being made relatively cheap, more and more people were getting connected to the world’s largest network as a consequence of which the data generated by the world population was rising exponentially and even the biggest of IT giants were finding it difficult to cope up with such large streams of data flowing into their servers; difficult in terms of storage and processing. This is where Big Data came in strong to offer its services through its plethora of tools and applications that offered pragmatic solutions to the aforementioned problems. As Big Data gained its footing in the industry with almost every IT company taking help of its functionalities, a new pattern or trend, as you may know, was discovered. Processing of customer data, companies and tech giants came to a solid conclusion that the same data contained answers to a lot of their problems. But the problem with this finding was that the dataset was too large to be analysed in a single go. Another problem was how to filter out the relevant stuff from such colossal piles of data and furthermore, what to do with the findings.
The very same problems led to a whole other domain of computational study to deal with the same. This domain is what we refer to as ‘data science’ these days. Data science, as the name suggests, is based completely on data; in fact, this is the same data that we discussed just recently. Data science as a concept is all about using large sets of customer data to find a behavioural pattern as per the needs of the company and subsequently use the discovered patterns to solve the particular business problem.
What does it take to be a data analyst?
At present, in the technical world, being a data analyst is the most rewarding of professions both in terms of growth as well as money. But the grass is always greener on the other side. On paper and in theory, becoming a data analyst seems to be too easy a task and to be very honest, completing 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 command and the proficiency that one holds over the various tools and applications that a data scientist uses daily. So, to become a data analyst not just for name but to become one such that you become a standard in yourself in the industry, following are some of the requirements with which you should be affiliated with from in and out:
As mentioned earlier, data science is all about finding logics and patterns underneath a mountain of data. Sieving through this ‘mountain’ is just not possible solely by human labour and so the only logical answer to derive said patterns is to turn to the powerful computers of today. But even computers cannot function on their own right! Even they need a set of instructions on which they can act accordingly and derive the appropriate results.
These set of instructions are given to computers with the help of a piece of code written in a high-level language. At present, the most powerful and advanced languages to design models for data science and deal with the sophisticated level of statistics involved in data science include Python, R/R-Studio, Java, SQL, MATLAB, etc. Amongst these languages, the most popular among data analysts is Python owing to its dynamic behaviour and a vast range of powerful libraries that do even the most complex of calculations in a jiffy.
Stats And Aptitude
Data science is designing models and writing code later and logical reasoning, maths, and numbers before. The thing with data science projects is that all of them are unique in their own way and the purpose of every single one of them is distinct too, which means that for the same dataset, 2 different projects need 2 different approaches and to devise these 2 distinct approaches, numbers and figures in the dataset have to be viewed from a whole other perspective and it is to look at things with a different perspective, a data analyst needs to constantly think out of the box which can be made possible only when the brain has been trained to think that way.
Data is stored in databases (or in data centres as per the current technological needs) and to continuously deal with such large sets of data, a data analyst needs to have the basic concepts of database management on their fingertips. To work and communicate with the given data, a data analyst needs to be proficient in some database language (such as SQL, NoSQL, Swift, C#, etc.) and carry out the desired analytics required.
Another reason why data analysts need to be excellent with databases is that continuous fetching & modification of data and subsequently having to write the changes to the physical database is both time as well as energy-consuming and data analysts are also tasked with making this process efficient (time and energy-wise).
Machine/Deep Learning & AI
Machine learning may be considered as a subset of data science. It is the fastest growing and emerging technology of the modern world and offers services that can make your work a whole lot easier by automating a given task to its maximum extent. As its name suggests, Machine Learning is about machines learning something. This learning of theirs is again made possible by code written in programming languages and if the designed model works the way it is supposed to, it can make your task easier, more efficient, and more accurate than Big Data and cloud solutions.
Once a project has been completed with the help of the models that were designed, the algorithms that were used, and all of the other stuff that goes into successfully executing a project. However, there occurs a major obstacle in the end, which is to make your clients and the end-users understand the results and findings of your work. But what’s the obstacle over here, you may ask. Well, the hurdle is that you are a data analyst, but your clients are not. They do not understand what your model means or what your code is trying to convey. They can only understand the results if it is conveyed in a human-readable format. This is where data visualisation comes in. Using tools such as Excel, data analysts need to display the conclusion of the project in the form of bar graphs, pie charts, etc. in an accessible format to understand the trends and patterns identified in the data.
Before a data analyst can start with his work, there is a major task to be accomplished ahead. The data on which analytics has to be done is very large as we have discussed a lot of times. But what we haven’t noted is the randomness and lack of structure in the same. These 2 factors make reading and understanding the data more difficult than it already is. Putting this raw data in a format with the intent of making it more valuable is what is called data munging.
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