Data science is an exciting field which generates thousands of jobs every year. It attracts aspirants from all backgrounds due to its application in diverse areas.However, it can be challenging for a person who is getting started with data science to wrap their head around all the arcane concepts involved, especially for people who come from a background that does not involve programming or mathematics. An understanding of the full extent of data science will provide you the necessary confidence and make it easier to relate and describe your skill set with respect to the company.
The term “data scientist” is often used for jobs that are drastically different. Thus it is important to know the different kinds of jobs this highly interesting field has to offer, before applying for any jobs. Read any job description carefully before applying for the job. If you already have the requisite skills for the job, you may go ahead and apply. If not, you can understand what the job entails and develop any required relevant skills.
1.Who is a data scientist?
A “data scientist” can refer to either one of the following four main job roles :
1. Data science generalist
A generalist is basically a jack of all trades, who is equally talented in making bar charts, producing code, using regression tools and doing data analysis, etc. Generalists are usually required by companies that are into multiple functions and not just data. Sometimes such jobs are floated to fill in positions in teams consisting of specialists in other fields of data science.
2. Machine learning engineer
This job is ideal for someone with a background in core subjects like mathematics, physics or statistics who hopes to continue with further studies. The companies here produce data as their final product and offer services that involve quite large datasets.
3. Data engineer
A data engineer is someone who possesses skills in most of the commonly used software that can organize data and analyze it further. Such posts are required by companies with websites having a huge number of visitors per day. Thus they require someone who can handle the large amounts of data.Such positions give you ample opportunity of growth since they do not require advanced skills in statistics.
4. Data analyst
Many companies use the terms “data scientist” and “data analyst” to mean the same thing. The job of a data analyst is to translate data into meaningful information, which can then be used to make business decisions. You will be often involved in tasks like getting data from databases, using pivot tables in Excel or making bar charts and plots for projects.The job gives you a lot of flexibility and can be a great environment to experiment to advance your skills.
Once you have decided the job that you want to apply for, you would want to build a resume that is crafted to land you an interview. Our article on “How to write the perfect resume” will arm you with the best tips to guide you through the resume writing process. So go ahead and make your resume awesome!
2. How to approach the interview : some tips and tricks
So your resume lands you an interview, and it might seem quite intimidating and exciting at the same time.Many people here start overthinking and analyzing too much.Keep in mind that an interview is not an exam. Treat the interview as a conversation between two people who are looking for the same thing – a good data scientist who is up to the task.
Because of the large number of people applying, the interview process is going to be intense and competitive. The interviewers will be interested in your technical knowledge as well as communication skills. You need to prove that you are culturally fit for the organization in two or three rounds of interviews. To clear these rounds you need to prepare well to tackle common questions from data science and machine learning backgrounds.
You should look at the interview as a dialogue. Thus, rehearsing answers to typical behaviourialinterviewquestions is a strict no no. Instead, pitch your selling points to the interviewer with the job description and information about the company in mind. Follow up each point with an example to provide contextual reference.
If the interviewer says something interesting about the company or the role, be curious and ask questions about it. Genuine curiosity is also an opportunity for you to display what you know to the interviewer. Don’t be afraid to pitch your point to the interviewer.
Lastly, be confident and show the interviewer that you are a positive person. No one likes to hear a complainer. So take charge and demonstrate your positivity in every answer that you give. Be prepared for the questions that might evoke a negative answer and approach them in a positive manner.
3.How to approach technical interview questions?
The technical interview can be grueling, as the interviewer will test you on a wide variety of topics starting from data science, statistics, machine learning and different programming techniques. But fear not! We include below an exhaustive list of commonly recurring questions on different topics asked in data analyst and machine learning interviews. These will not only strengthen your foundational knowledge on these topics, but also help you in determining the precise skills you need to have for the job you are applying to. We have also included links to additional references which you can follow if you are interested in knowing more about these topics.
So let’s get started!
3.1. Questions asked in data science interviews.
Here we will tackle the questions on topics that are raised in a data science interview. Regression analysis, basic methods of curve fitting, correlation coefficients and probability are some of the common topics that we will deal with here.
3.1.1. 25 common questions asked on statistics
We list here some of the most commonly asked questions on statistical topics from correlation to regression analysis to data fitting techniques. These questions will guide you through common pitfalls in statistics and help build your confidence in answering questions related to statistical concepts.
18.104.22.168 questions asked on probability for data science
Uncertainty and randomness is a common aspect of our daily life and a good grasp on probability concepts is required while dealing with such problems as a data scientist. The questions listed here will help you remove any uncertainties associated with your preparation of probability!
3.1.3. 19 questions on regression for data analysts
Regression is used to design algorithms to fit data. Knowledge of regression is critical in developing models to fit data from various sources like medicine, sports, politics, etc. to help with predictive modeling. Our guide to interview questions on regression analysis will ensure that you answer each question on regression aptly.
3.2. Questions asked from machine learning
Machine learning has diverse applications from facial recognition to surveillance to designing virtual assistants that are so common these days. In this context, your computer science fundamentals and programming techniques, probability and statistics, data modeling and evaluation, application of machine learning algorithms and will be tested.
3.2.1. 20 questions on natural language processing for the machine learning engineer
Natural Language Processing (NLP) teaches machines the language we humans use to communicate. There are many challenging areas where NLP is being used to improve the language processing ability of computers.As an upcoming and exciting field of machine learning, questions from NLP should definitely be in your list of interview questions to prepare.
3.2.2. 24 questions on tree based models for a data scientist
Decision trees are popular tools in machine learning used in identifying strategies that are most likely to reach a goal. Applications include decisions of chance event outcomes, deciding utility in economics and resource costs. These applications are meaningful in making business decisions and explaining them to relevant people, and so are important from an interview perspective.
3.2.3. 18 questions on clustering for the machine learning engineer
Clustering involves grouping of data points to classify them to a suitable group. As a method of unsupervised learning, it is a common technique for statistical data analysis used in many fields. When a clustering algorithm is applied to a data set, we can draw many valuable insights by seeing the groups in which the data points fall.
3.2.4. 18 questions on deep learning for the data scientist
Deep learning is a field in machine learning that is changing the way technology is being viewed. Numerous amazing applications of deep learning exist including self driving cars, automatic text generation, image recognition and predicting earthquakes. The relative importance of deep learning as a game changer in data science makes it a frequent topic of discussion during interviews for data scientists.
In conclusion, practice behavioral questions and get advice from software engineers and product managers who messed up their interviews before they got them right. Here we provide some resources which will be helpful in your interview preparation. That being said, go ahead and ace it!
· HackerRank: Website and forum that has periodic programming challenges
· Project Euler: This website has a lot of logic problems for practicing coding solutions
· Interview Cake: Practice questions and tutorials.
· Interactive Python: Many tutorials on most of the topics discussed and many more, along with Python examples and concept questions.
· Introduction to Algorithms by Charles E. Leiserson, Clifford Stein, Ronald Rivest, and Thomas H. Cormen
· Cracking the Coding Interview by Gayle Laakmann McDowell
· Programming Interviews Exposed by John Morgan, Noah Kindler, and Eric Giguere