What is Data Science?
Data science is a branch of computer science that deals with the mining or extraction of knowledge or valuable insights from the large-scale processing and analysis of raw data. The beauty lies in the limit of Data Science, which is boundless, as it can be applied to almost any area where there is data, and thus the core foundation of Data Science is laid down by various technologies like Automated Machine Learning, Artificial Intelligence, Big Data, Cloud Services, Statistics, Data Analytics, etc.
Why should one choose Data Science as a Career?
This modern era is evolving at a very rapid pace in dealings with data and it has become the new crude oil of the 21st century as on processing, it gives something enormously precious than gasoline or petrochemicals without which any company, irrespective of its net-worth, is blind and deaf wiz information. A true saying from W. Edwards Deming, “Without data you’re just another person with an opinion” determines the power hold of information. By now you must have understood the basis of information and how lucrative it is. I am sure you would agree that a business builds on information and this is where comes the role of a Data Scientist to withhold a company via their expertise to visualize relationships and patterns in organized and unstructured data to come out with useful insights. Being such a humungous field grants a Data Scientist exposure to get their hands on to diverse domains and technologies to solve real-life problems and due to its great demand, it has become the most lucrative job of the 21st century. You can also check out Data Scientist Salary in India for more insights into the popularity of this field.
Data Science is the future of everything. If you are looking for a career that will be in demand for decades, Data Science is it as data will last longer than the system itself. In this section, we will explore why Data Science is so popular and why it is the perfect career choice. Data science is one of the most in-demand jobs in today’s world. In fact, according to a report by McKinsey Global Institute, data science skills are now more valuable than traditional computer programming skills. There are many reasons to choose Data Science as your career. For example:
- Demand for data scientists has increased by 67% in just two years
- Ranks no. 3 best job in the U.S. on Glassdoor.
- There will be around 11.5 million data science jobs globally by 2026
- The median salary of a data scientist is $1,20,000 as of 2021.
Some of the largest enterprises based on data science are Google, Amazon, and Facebook using data science to create algorithms to maximize profits and improve user satisfaction by ranking the required web pages on top, recommending products based on consumers’ past behavior and interests, and through targeted ads respectively.
Data Science Interview Preparation
You are now aware of the various aspects of Data Science and if you wish to prepare for every single topic for the interview, especially within a limited period, it could be hard and excruciating. The following article is a detailed stepwise illustration guiding you through the most important topics and skills for acing the data science interview process.
a. Basics of Data Science: Some extremely significant basic concepts and initial skills are distributed computing and data structure, data mining, data visualizations, Business Intelligence, and libraries such as pandas, Matplotlib, scikit-learn, and TensorFlow which will help you in dealing with a range of projects and questions.
b. Mathematical concepts: Grab on to the concepts of probability and statistics, dimensionality reductions, linear algebra, and concepts on activation functions and optimizers.
c. Programming Languages: You should focus on Python and SQL as Python is the simplest and most widely utilized language for solving most of the complex tasks in Data Science and machine learning and on the other hand, SQL is used to construct large databases for solving complicated tasks in Data Science.
d. Algorithms and Visualization Tools: Finally, the most important thing you cannot miss is Machine Learning concepts and algorithms like K-nearest neighbors (KNN), random forests, decision trees, linear and logistic regression, clustering algorithms, and other signature algorithms plus knowing the working methodology of data visualization tools like Tableau, Google charts, and Qlik will surely help you stand out among most of the candidates.
Job Roles in Data Science
Data science is a field that has been rapidly growing in the past few years. It has become an important part of many industries including finance, technology, healthcare, and education. There are several types of jobs in data science that provide opportunities for people with different interests and backgrounds such as Business Analysts, Data Engineers, Data Analysts, Data Architects, Big Data Consultants, Analytics Engineers, Machine Learning engineers, etc.
Business analyst: These professionals are responsible for analyzing the company’s data to understand what is happening in the market. They also come up with recommendations on how to improve the company’s performance.
Data engineer: These professionals work closely with software developers to create new tools or modify existing ones to analyze enormous amounts of data. They also have expertise in building pipelines that extract useful information from raw data sources.
Data Analyst: A data analyst is a person who gathers, analyzes, and interprets data. They are responsible for making sense of the data and presenting it in a way that is easy to understand for others.
Data Architect: A data architect is a person who designs and manages the data of an organization. They are responsible for designing the physical database that will store all the data, as well as designing and maintaining the logical structures that will enable access to this data.
Analytics Engineer: Analytics engineers are responsible for designing, implementing, and maintaining an organization’s data collection and analysis. They need to have a keen understanding of the business to fully comprehend what is needed from the data. They are also responsible for collecting data that is relevant to their organization. This includes keeping up with modern technologies and using them to their advantage. They also need to be able to interpret the data so that it can be used by other departments within the company.
Big Data Consultant: The main responsibility of a Big Data Consultant is to design reports that analyze and identify trends from the data collected by the company and provide actionable insights. They also work with other departments in the company such as marketing, sales, and customer service teams, providing them with analytics on customer behaviors and trends. The role of a Big Data Consultant has increased in demand as companies are now collecting more data than ever before. Companies want to know how they can use that data to make better decisions, which is where the Big Data Consultant comes in.
Machine Learning Engineer: Machine Learning Engineers are the ones who create, train, and deploy machine learning models. They are also in charge of the data pipeline processes that are necessary for these models to work.
Tips to Crack Data Science Interview
The data science interview is the most important part of the hiring process. It is a way for companies to assess your skills and experience in data analysis, machine learning, and programming, and if you are thinking “How to crack Data Science interview?” The following are 5 most important tips for the same:
a. Be aware and updated: One should keep themselves updated with all the latest technologies to stay on top of their game and similar applies to the diverse field of Data Science. You need to know about AI, machine learning, and data visualization tools because these are the ones that will change how we do things in the future, and you can surely expect questions related to modern technologies as Data Science is always an emerging field.
b. Be confident and conscious: The Data Science field is an extremely competitive one. It is a field that requires talent and creativity. To get hired, you need to be confident in your skills and abilities. You need to know what you are talking about and be able to back up your claims with data.
c. Be thorough with your resume: Data science is a great field to work in. It is not just a job; it is a way of life and with the right resume, you can make sure that you get the job you deserve. It is important for data scientists to be thorough with their resumes because it is not just about how well you know the subject but also how well you can present yourself to stand out from the competition and get noticed by potential employers. It should be something that the interviewer would want to look at and read through. It needs to be clear and concise with an emphasis on your skills, qualifications, achievements, and experience to show that you are qualified for the job you are applying for. With these details, employers will know exactly what they are getting when hiring you.
d. Precisely focus on your Data Science projects: Prioritize your project well because like other key details you mention in your resume, you should be aware of which projects to talk about in your interview. Portray the best of your experience through projects because they speak for you but also be prepared to answer questions coming your way based on the projects.
e. Know the Job Profile and the Organization: The obvious benefit of researching the job profile is that you can streamline your preparation based on the requirements for the role. It is also important to research the job profile and the organization because it will help you to understand what the company does, who its competitors are, what its target audience is, and how it wants to be seen in the market. Researching will also help you understand what are the tasks that you would be responsible for and what skills are required for this position which you can accordingly portray in the skills and projects section of your resume and get yourself prepared for any outbound questions.
Despite the wide diversity of concepts that you will have to cover in Data Science for a job interview, you can be confident of the tips provided in this article to deliver you a higher success rate in acing your interview. According to a Forbes report, companies collect more data than ever before as they have raced to transform their businesses and make data-driven decisions and hence increasing the opportunities in the field of Data Science which is justified above in this article as well. Data science is one of the fastest-growing fields in the world as it offers a variety of job opportunities and roles at various levels in the company. It will be used to solve complex problems and make predictions about future trends.
A data scientist can be a researcher, an engineer, a business analyst, or a product manager. A data scientist can also be an executive in charge of making strategic decisions for their company and thus considering Data Science as one of the best careers is non-negotiable and the question “Is Data Science a good career?” becomes non-questionable. It speaks for itself with the data that Data Scientist is now declared “the most promising career” by LinkedIn and the “best job in America” by Glassdoor.