If you want to be an IT business analyst and don’t know where you fit into the puzzle, then this article is for you
Terms like ‘Business Analyst’, ‘Data Scientist’, ‘Machine Learning Engineer’ has gotten extremely popular in the recent years, ever since Harvard University called it the sexiest job of the 21st century. These terms are used interchangeably, however, there is a vast difference between a Business Analyst and a machine learning engineer. Both these jobs are extremely well paying, and are highly in demand at corporations across every domain like entertainment, hospitality, Tech, service etc. Due to the large amount of data that is produced today, firms need highly skilled employees to make sense of the data by analyzing it, which is the job of a Business Analyst’, and using that data to build a complex model that can predict consumer behaviour or the weather, is a job for the machine learning engineer.
The scale by while AI is going to change the way the world works is massive. Many have called it one of our biggest discoveries since the internet, but it is still in its development phase and the existing conditions make it the perfect time to enter the field. New papers are being released almost every day. Large companies and even smaller start-ups are investing a tremendous amount of time, energy, and money into research among areas like computer vision, natural language processing, robotics, medicine, and more! An IT business analyst is only one of the many jobs you could work at once you understand how to manipulate different forms of data.
Now let us discuss the kind of skills you will require if you’re looking for IT Business Analyst jobs. Let us divide the entire category into 2 parts, skills that you NEED and skills that would help you stand out.
Skills that you need
- Python – knowing how to code utilizing an object-oriented language is the most basic requirement you will need to meet. Python is the best language to learn because it is very easy to learn and is generally the most used language in the field of data science. Python is open source because of while many developers keep posting new libraries which complement the already existing libraries that are extensively used. The main libraries you would need to be familiar with are Numpy, Seaborn, Panda, Tensorflow, Matplotlib, and OpenCV.
- R – Data science and analytics is a game of statistics and R is a language specifically created for statisticians. It is used for statistical computing, which can also be done in python, but is a lot easier in R. The language is used to create software that includes statistical computing. Having this along python significantly increases your chances of getting a job.
- Statistics: While we’re performing data analysis we never look at a single data point to draw a pattern. We are looking at thousands of data points spread out across multiple dimensions. To make sense of all this we plot the data on graphs, where trends are more visible. Using our knowledge in statistics, we can find out the mean, median, mode, standard deviation etc. Understanding scatter plots, histograms, bar charts, the curve all fall under the statistical domain of knowledge.
- SQL: All the data that is generated is stored in data warehouses which is a huge database, where data from multiple sources are combined and stored together. Our object-oriented languages like python don’t work with databases, so we need to use a querying language like SQL to manipulate and access the data stored in a warehouse. SQL is used to insert, delete, update. as well as, select rows and columns from the database. MySQL is the most popular querying language for relational databases. It has a large community for it, so getting started won’t be very difficult.
- Github: The only way employers will know that you are skilled enough to work for them is, if they see the code that you have worked on. Github is a site where you host you code for everyone to see. Making projects and putting it out there is the only way for people to see what you’re capable of and for employers to see that you know more than just theory. Github is a great community, full of experienced developers that will help you solve your errors and improve your code,
- Machine Learning: All the data that is generated is fed into a model, we call that training a model. Once the model is trained with the data we use it to make predictions. This is how algorithms predict which user would be interested in which particular advertisement, by accessing the user’s history data. There are plenty of algorithms but a handful of them are used majorly, which you would be expected to know how to use. These algorithms include Random forest, Decision tree, Linear regression, K-means etc, and are all available in the Scikit-learn library, free of cost.
Skills that will help you stand out
- Deep learning: Deep learning is the main reason why artificial intelligence has taken off in recent years. It is a highly complex form of machine learning which contains hundreds of hidden layers between the input layer and the output layer. Since there are so many layers and often huge amounts of input data, deep learning is computationally very expensive. It takes tremendous amounts of resources to train your model. It can take even take months, so it is a very difficult concept to grasp completely. Having projects that use deep learning is a pro in your resume.
- Tensorflow and Keras: Tensorflow and Keras are two frameworks built on top of Python that make building deep neural networks a cup of tea. They’re highly straightforward and significantly reduces the lines required to code. They also can work with GPU’s which can significantly increase the computational speed. They have a rather tough learning curve but it’s worth sticking with it
The most important section on your resume would be your projects. Do not show all the projects that you have work on but rather select the best 3 – 4 and focus on them. Elaborate what you learned through those projects and that should help you stand out in the huge pile of applications for IT business analyst jobs.