Appearing for a Data Science interview process is an intimidating task in itself. You never know what the interviewer might ask you. He or she might test your statistical concepts, Data Visualization and Data Mining knowledge or ask you to solve a puzzle even! Thus, you have to prepare for everything. A person having a firm grasp of the topics should not be afraid under any circumstances.
But, there’s much more to the Data Science Interview process that one forgets to consider. Surely, a person having completed one or two projects than the rest of the candidates will have an edge. Thus, let us delve into the whole preparation process.
Comprehend the Various Skills and Roles
Have you gone through the various roles and prospects that are available in Data Science? The first thing you need to understand is that there are a variety of roles in the data science ecosystem. Usually, a data science project requires the contribution of people having expertise in different fields of Data Science. Henceforth, you get to see roles such as:
- Data Engineer
- Data Visualizer
- Data Architect
- Business Analyst
- Data Analyst
- Data Science Manager
- Statistician
- Machine Learning Engineer
- Computer Vision Engineer
The list goes on. Thus, you need to go through each of the job roles and check the requirements in the market. You might need to complete one or two courses in order to be eligible for a particular job. For instance, you need to have strong Python and Software Engineering background for a data engineer role. On the contrary, if you want to get into a Business Analyst role, you should possess good problem solving and communication skills.
As per the statistics, 11.5 million jobs will be created by 2026, as per the US Bureau of Labor Statistics. So, if you aim to pursue a job in Data Science, prepare well. And study thoroughly as per the field you want to venture into. Thus, you must have domain knowledge, expertise in Python, Java, at least two projects under your wing and an in-depth idea of Machine Learning and Neural Networks.
Also read: 7 Major Benefits Of Choosing Online Learning
Create an Online Presence
Most often, it is observed that students do not get into top-rated firms, despite having good qualifications in Data Science. In this digital age, you have to advertise yourself and have a strong online presence. You must have a:
- GitHub Account
There is nothing more convincing than a well-documented code on GitHub. If you upload your code and projects to GitHub, it helps the recruiter see your work first-hand.
- LinkedIn Profile
You can directly interact with company hiring managers and HR on LinkedIn. This shouldn’t be a surprise as there are 30 million companies on LinkedIn.
- Quora Presence
You have to regularly answer questions on Data Science on Quora. This would help the company recruiters know that you are helpful and that you have a strong command over the subject matter.
- Blog Site
If you are learning something new, you can share it on your blog site. You can ask the community for their feedback. Online bibliography maker This is how you build credibility and enhance your chances of getting an interview.
In the meantime, keep honing your coding skills and enhancing your knowledge. If you are solving a Python assignment and you wonder, “Can anyone do my assignment?” you can take the help of the professional experts.
Look for Interview Questions on the Web
The more you practice different types of questions, the more you will get to know about Data Science or its components? If you are applying for a particular company, type “XYZ company interview questions” and you get to see a list of Data Science interview questions. But, there are some topics that you need to focus on, such as:
· Differences Between Supervised And Unsupervised Learning
· Explain Logistic Regression
· Steps Involved In Decision Tree
· Avoiding Overfitting Of Model
· Build a Random Forest Model
· Differences between Univariate, Bivariate, And Multivariate Analysis
· Dimensionality Reduction and Its Benefits
· Calculate the Euclidean Distance in Python
· Calculate Eigenvalues and Eigenvectors Of 3*3 Matrix
· Recommender Systems
· Finding RMSE and MSE In A Linear Regression Model
· Calculating the precision and recall rate
In addition to this, you will be asked to build classification model based on a given dataset, and give alternatives. It is imperative that you go through a list of questions.
Do Projects and Prepare for Tough Questions
Doing projects is not enough. You have to prepare for a set of tough questions as well. First of all, you need to do projects as per your job role. For instance, if you apply for a Machine Learning Engineer role, you should do a project on Movie Recommendation system or designing a prototype chatbot with unique aspects.
Thus, you need to know about abstract click method, the algorithm and framework that you have used to execute the code. Moreover, you need to know about the bugs, multi-source problems, you can expect from the program. You might also be inquired regarding hierarchical clustering algorithm, Python packages, libraries and functionalities.
Similarly, if you are applying for a Data Analyst or Data Science Manager role, you need to have the appropriate information at your disposal. If you apply for the analytics manager position, the most difficult job role, it would take you approximately 53 days to fill in. Meanwhile, if you wonder, “Who can do my assignment or project online?” you should seek professional assistance from the experts.
Your next would be to submit resumes in different companies and wait for an interview. Since we are under lockdown, you can expect telephonic screening or Skype calls. If you pass this round, you will be asked to solve an assignment within a given time. If you are well-versed with the topic, it should not take you long to solve the questions.
If you accomplish the task, you will get into your desired company. So, if you had been wondering how to prepare for the Data Science interview; hopefully, you have an answer to your question now.
Also read: Artificial Intelligence and its Impact on Future of Project Management
