Data science is the most in-demand field in this digital era. In almost every interaction with technology, data is interchanged. A data scientist’s role is to analyze this data and interpret the results to implement them for organizational benefits. In the modern world, about 2.5 quintillion bytes of data are processed every day. A data scientist can organize and analyze this huge amount of data to make it accessible to lead a profitable business. But getting hired as a data scientist is not easy in this competitive world. On that note, this article lists 7 ways to impress your data science, the hiring manager.
A Crisp and Effective CV
Many hiring data science project managers have reported that data scientists don’t know what companies are looking for and hence send a CV, which is not effective. While there are a lot of things to jot down in a data scientist’s CV, most of the data scientists end up writing a 4–5-page CV. That is a big no. Truth be told, nobody has time in today’s world to go through such an in-depth CV. So, the trick here is writing crisp CVs with necessary details. You should focus more on highlighting your knowledge of data science in project management and your solutions for real-world use cases. Employers hiring a data scientist would want to know how capable you are of bringing up creative and innovative solutions.
Data Cleaning Project
A data cleaning project shows a hiring manager that you can take disparate datasets and make sense of them. This is most of the work a data scientist does and is a critical skill to demonstrate. This project involves taking messy data, then cleaning it up and doing analysis. A data cleaning project demonstrates that you can reason about data, and can take data from many sources and consolidate it into a single dataset. Data cleaning is a huge part of any data scientist job, and showing that you’ve done it before will be a leg up.
While this may seem like the most common skill across all job roles, to be honest, it is one of the crucial skills a data scientist should have. If we look at core elements of data science, it is about programming, maths and science, and business acumen. Here, business acumen implies knowing the business or the industry you wish to work for. Moreover, every data science hiring employer has a different agenda and objective for hiring data scientists. Some may want to drive data-driven decision making whereas some may wish to solve a business problem.
It’s important to be able to understand and explain complex data science concepts, such as machine learning algorithms. This helps a hiring manager understand how good you’d be at communicating complex concepts to other team members and customers. This is a critical piece of a data science portfolio, as it covers a good portion of real-world data science work. This also shows that you understand concepts and how things work at a deep level, not just at a syntax level. This deep understanding is important in being able to justify your choices and walk others through your work.
Right Product Knowledge and the Ability to Learn
If you are applying for a data science job you must know that the hiring manager will throw some use-cases to test your product knowledge and problem-solving skills. Well, of course, a hiring manager would do that because one would want to test and see if the projects and your work experience is written in the CV are true or bluffed. Hence, you need to do thorough research on prospective company’s products, services, and solutions, try to understand from the job description what they are looking for, and frame your answers accordingly. Be prepared that the use cases might be related to the company’s products or the problems they wish to solve by hiring a data scientist. If you wish to get everything right, make sure you brush up on your necessary programming, statistical knowledge for the interview. One should be prepared for anything that the prospective employer can ask.
Data Science Competition
Data science competitions involve trying to train the most accurate machine learning model on a set of data. These competitions can be a great way to learn. From a hiring manager’s perspective, a data science competition can demonstrate technical competence if you do well, initiative if you put in a good amount of effort, and collaboration if you work with others. This overlaps with some of the other portfolio projects, but it can be a nice secondary way to stand out.
Learn The Important Programming Languages
To be a data scientist, it’s important to know and master the necessary skills rather than getting a shiny degree from a university. The interview process is skill-based and these are the languages you need to master:
Python– Knowing this will help you filter and transfer big data and unstructured data. Python can be used for web development, software development, deep learning, and machine learning.
R– An open-source programming language, R is useful to calculate complicated mathematical and statistical problems. It will also help in data visualization.
SQL– This is a relationship management tool through which you can query and join data across multiple tables and databases.
SAS– Large corporations use this tool for statistical analysis, business intelligence, and predictive analysis.
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