Regardless of your field, the processing of job hunting from start to end can get very time-consuming, stressful, and emotionally exhausting. It can be even more demanding psychologically if it is your first time applying for a job just freshly after graduating or if it’s your first time applying after changing career paths.
Because it is your first time, you feel completely in the dark about what companies are looking for, how to make your resume as appealing as possible, what kind of questions you should expect if you got an interview, and what your portfolio should include.
Data Science is one of the tech fields where the demand for new data scientists with specialties in different aspects of the field rises daily. Despite that rising demand, the supply for opening jobs often outnumbers those available positions. To get a job in data science, even an entry-level one – which doesn’t always exist – can be a very lengthy and challenging task.
That being said, if you have a strong portfolio with amazing projects that you used various core and advanced techniques in, your chances of getting an Interview get higher. But, getting to the interview phase is not the end of the line; it’s just another step to prove your capabilities to your – hopefully – future employer and land your dream role.
Then, another stage of preparation starts; you need to be fully prepared for the interview. You may have eye-catching projects in your portfolio, but that won’t be enough if you couldn’t prove that you do have the knowledge needed for the job during your interview.
Luckily, despite the broadness of data science, interviews for various data science roles mainly focus on the field’s core fundamentals. Because if your fundamentals are strong, you can learn and build on that any amount of new knowledge. This article will go through 6 fundamental, technical data science questions you should expect to be asked during your future data science interview.
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1. Regression
If I were to choose just one concept that I would consider the core of data science, that concept would be regression. No matter what’s job role you’re applying for, chances are you will be asked at least one question about regression. So, to be on the safe side, make sure you know everything about regression, both the theoretical aspect of it and the practical one.
Questions about regression include linear regression, logistic regression, the differences between the two, and when to use each of them, how to interpret your problem coefficients into variables and make assumptions in your code. Calculating the p-value and what it means and applying residual analysis, and differentiating between L1 and L2 values.
2. General machine learning models
Basic machine learning models are the next on our list of must-to-know concepts to pass your data science interview. You should be familiar with general predictive models. You need to be able to read a problem, understand it, and choose a model that will fit it best.
To do that, you need to know the basic models, their fundamental theory, how they work, and their best practices. You also need to train them, test their accuracy, validate their results, and perform different analyses on them. How to perform cross-validation, and when. What are the criteria you base your model parameters on? How to choose the best predictor to solve the problem at hand.
3. Random Forest
One of the concepts that are asked about almost 100% of the time in data science interviews is random forests. Questions about random forest include one like, How do you grow a data tree? Why use random forest to start with? When to use it and how to make it efficient?
You also need to be able to justify why choose a specific subset of variables for each split of the tree, how to detect over-fitting in your tree, and then prune it optimally. The interviewer may also ask you why you chose to solve your problem using a random forest and not a gradian boosting machine, so knowing the use cases and difference between those two techniques is important knowledge to have.
4. Clustering
Clustering algorithms are one of the core algorithms of machine learning; even if you’re not a data scientist, chances are you heard and know the theory of clustering. Most data science interviews often include a question or two about clustering algorithms, usually the k-means algorithm.
The k-means algorithm is a simple yet efficient and potent machine learning algorithm. The interviewer can ask you all kinds of questions about this algorithm, starting with why k-means? How is the number of clusters chosen? How many times do you need to iterate over the algorithm for optimal results? What is a loss function, and how to implement it to test your algorithm performance? And how to optimize the convex for best results?
5. Core Maths and Statistics
Although data science is all about the data, how you collect it, clean it, analyze it and use it to predict future data. Math and statistics are the gears running the entire operation. So, your maths and statistics knowledge need to be spot on.
You can be asked about the different probability distributions, how to perform a T-test or get the Z-scores. What is the Chi-square test? When to use it, and what does it mean? How to calculate the covariance and correlation between variables and distributions.
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6. Advanced topics
In some cases, based on the role you’re applying for, you will get more questions, such as ones about matrix manipulation and factorization. Or how to deal with time series, how to choose the p, d, and q parameters, and how to perform box tests.
Sometimes, the interviewer may test your SQL knowledge by asking you to interpret different SQL queries or write one based on different scenarios. You also need to expect questions about data visualizations, how to choose the best representation to communicate your results simply and efficiently.
Final Thoughts
Getting a job in today’s tech world is one of the most challenging and nerve-wrecking experiences anyone can go through. From preparing a resume and a portfolio to looking for the right roles to applying and waiting anxiously to hear something back, which doesn’t always happen to prepare for interviews and finally get an offer, how can someone overtake it arguably – flawed stressful process?
The bad news is, there’s no magical formula of how to land the job. However, there are things you can do to help your chances of getting the job. One of the important steps that you need to make, especially if you make it to the interview stage, is to be fully prepared for the questions often asked in technical interviews.
In this article, we went through the 6 types of questions – concepts – that are often asked in data science interviews regardless of the specific role you’re applying for. Because in the end, all the subfields of data science roll down to a couple of basic concepts that if you know, then you’re a data scientist.