Tip #: Choose The Best Algorithm: Accuracy Vs Speed Vs Interpretability
I covered this implicitly in Tip #2 but any time someone asks you about the merits of using one algorithm over another, the answer almost always boils down to pinpointing which 1 or 2 of the 3 characteristics – accuracy or speed or interpretability – are most important. Note, its usually not possible to get all 3 unless you have some trivial problem. Ive never been so fortunate. Anyway, some situations will favor accuracy over intrepretability. For example, a deep neural net may outperform a decision tree on a certain problem. The converse can be true as well. See No Free Lunch Theorem. There are some circumstances, especially in highly regulated industries like insurance and finance, that prioritize interpretability. In this case, its completely acceptable to give up some accuracy for a model thats easily interpretable. Of course there are situations where speed is paramount too.
Tip #: Avoid Jargon Or Concepts Youre Unsure Of
This is hands down the easiest way to sabotage yourself. I see it all the time. Heres the situation. Youre deep in your explanation of how gradient descent works and things are going smoothly, so you decide to reach a little and mention Elastic Net when describing GD and regularization, even though youre not very confident in how Elastic Net works or exactly what it is. But things were going great and you want to show how smart you are. You can slip this in and nobody will notice, right? Not a chance! Thing is, you dont notice how painfully obvious it is to the interviewer that you dont know this term. Your voice quivers or your face contorts when you let slip the word Elastic Net. Its likely imperceptible to you but not to us. What happens? The moment youre done talking I effortlessly expose your weakness thus dismantling your explanation piece by piece.
Tactics That Will Be Used Throughout The Whole Process:
Answer questions in the form of stories. Think of 45 stories regarding projects youve worked on or work youve done and outline them in the STAR framework.
- Situation: Briefly set the stage for your story
- Task: What was the specific problem you were trying to solve?
- Action: Describe what you did to solve the problem
- Result: Discuss the impact your solution had.
This is a real example of an answer I gave to the question what is your greatest strength?
I was hired into my current role for my excel skills and my ability to deal with numerous data sources using excel. Unfortunately, the amount of data I needed to process was beyond Excels technical limitations. I asked other analysts at the company what tools I should look at and one of them recommended Python. I didnt know how to program when I started that job, but within 3 months I had started to automate large parts of my own workflow, and a month later I built an application that automated a business function that took about 10 hours of manual work a month.
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S In A Data Science Hiring
Are you feeling overwhelmed about how to begin your preparation journey for a data scientist interview? You are not alone! Organisations constantly seek ways to improve their hiring process to source and recruit talented data scientists. Lets dive in to learn about the step-by-step process of data science hiring.
- Getting applications
When the company lists its job vacancy, it receives hundreds and thousands of applications. In such cases, recruiters will analyse the CV and portfolio of the candidates to sort out the passionate enthusiasts with strong academics and proper experience.
Once the organisation decides the requirements of the job profile, it will plan a proper pre-screening test with basic questions about motivation and qualification. This simple assessment will reduce the application pool and enable them to understand whether the candidate can face highly demanding technical challenges.
- Technical assessment
The candidates who have cleared the pre-screening will be invited to participate in the technical test, which will have the basic operations regarding the role. In this process, transparency is inevitable since it will readily screen out candidates with minimum technical skills.
- Onsite interview
The onsite is the most challenging step before you achieve the job offer. It is more comprehensive than the former steps and typically combines 4 or 5 interviews in a single stage. The combination might vary based on the type of role you have applied for.
Preparing For A Data Science Interview Let Us Teach You How
Data science is a rapidly growing field that involves the analysis of large data sets. To be successful in this field, it is essential to have strong analytical skills and knowledge of statistics. In order to ace the data science interview, it is important to know how to ask questions and use data to solve problems.
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Before The Panel Interview:
LinkedIn research will be your best friend here as it will give you an idea about what each person will talk to you about. Craft questions appropriate for each interviewer based on their role. If one panel member is an executive or senior member of the department, ask them questions about the challenges the company is facing or emerging strategic opportunities. If theyre a fellow data scientist, ask them specific questions about the tech stack, and problems they currently face. The goal of the questions is once again to build trust. You want to present yourself as someone who took the time to get to know them.
If the interview is on site, write a brief generic handwritten thank-you note to each of your interviewers and pack them in your bag.
How To Ace A Data Science Interview: 5 Tips From Data Scientists At Facebook Uber Twitter And Amazon
In this article
Interviewing for a job as a data scientist can be a nerve-wracking experience. Between compiling a portfolio, completing technical screens and challenges, and going through rigorous interview loops, candidates are often put through the wringer as companies try to determine whether a candidate has the creativity, technical chops, and right attitude to join their data science and machine learning teams.
Fortunately, many data scientists have been through the interview process and shared their experiences both as hiring managers and job applicants. Read on for insights and advice from data scientists at Uber, Facebook, Twitter, Amazon, and Reddit.
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Future Of Data Science
Adel Nehme: Now, as we close out, I’d love to pivot to discuss more of the future and how you believe the data science workflow and skillset will change. What do you think are some of the major trends that will shape the role into the next few years? You saw over the past year, large language models like Codex, GPT-3, AutoML, how do you think this will impact how successful data scientist or data science applicant is perceived in general?
Kevin Huo: Yeah, that’s a good question. I think the short answer is that, again, the rise of a lot of more black box models will only accentuate the need for data scientists. There’s a great analog, I think back in, well, I guess a long time ago now, but the ATM was invented, and they thought tellers will go out of business and it turns out afterwards empirically, there were actually a lot more bank tellers. So in the same way, if we just look at the way technology’s progressing, there’s been a crazy amount of innovation in the last 20 years, people are talking nowadays about generalizable AI models and just super ML, if you will. I think that they really will be a blend.
Kevin Huo: And so I think it seems scary, again, GPT-3 is amazing. I don’t think it will displace all data science jobs, it will just make data scientists focus more on the strategic higher level decision making principles.
Adel Nehme: Finally, Nick, Kevin, where can listeners learn more about what you’re working on?
Adel Nehme: I was about to mention that.
Data Science Interview Preparationtip # 05
How do you actually prepare for a data science interview? This is one of the major challenges because there are a whole host of problems everywhere on the internet and you have to follow an organized and structured process in preparing for your data science interview.
How to prepare for a long-term data science interview thatâs two to three months out and short-term interview in terms of the night before?
How to prepare for a long-term data science interview?
For a long-term interview, I would suggest you break down the questions into several sections like :
- Machine learning models
- Data science questions
- Modeling questions
You have to clearly separate the questions like pre questions, post questions, and some videos and content in between that you can study. Then try the pre section, see how you do on them, where your weaknesses are, write some notes on them. Basically, the aim is to keep track of where you are weak, fast or slow so that you can get to know which part you need to practice more. If you are not keeping track of what youâve studied and where you are weak, itâs going to be really hard for you to improve because you have no idea where to improve. So, focus on the questions you get wrong to know where you need to improve.
How to prepare for a short-term data science interview?
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Tip #: Realize An Interview Is A Dialogue Not A Test
Most everyone walks into a technical interview thinking all they have to do is answer all the questions correctly and then theyre home free. Nope! Even if you magically pull that off , the interviewer is looking at more than just your technical knowledge. Theres a very good chance the interviewer could be your boss or co-worker, which means youll be spending lots of time together. That also means youre being evaluated for fit. Maybe youre high energy but the group is mostly introverted. That would mess with the dynamics so youre a no-go. It could be that the interviewer is one of those people thats always right and will not tolerate a challenge, even if its warranted. Who knows, maybe your personalities clash. It happens. But its better to find that out sooner rather than later.
So the first 6 tips built you up and now it appears Im telling you it may be hopeless after all. Far from it. The key here is that an interview is a two-way street. Can you be eliminated for any number of serious or silly reasons, even if you have all the technical skills? Absolutely. But the fact is that you have an equal amount of power. You should treat the interview not as a test but as a dialogue you are interviewing this employer as much as they are interviewing you. If the interviewer is a jerk or has a massive ego or you see some other red flag, dont discount it. You should step back and consider if you really want to work there.
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How To Attract Talent
Adel Nehme: That’s really great. And I definitely agree on the honesty aspect of it as well and letting candidates know what they’re getting into. I think in our conversation so far, it’s been clear that applicants need to think like marketers and they need to creatively think about how to get noticed. Similarly, there are a lot of data teams and hiring managers that need to think about ways to attract talent and compete with the fangs of the world. What are ways data teams can think like marketers to attract talent?
Nick Singh: Yeah, absolutely. I think one thing is we love companies that have good engineering blogs or data science blogs, because it gives candidates something to latch onto like, “Oh, this is the kind of work they do.” And it lets your own team look good. And I think ultimately, people want to work with other people. People don’t want to work at this nameless brand or company, they want to work with Joe or Bob or Sally. And having these technical blogs authored with like, hey, at the bottom like a call to action, “If you like this blog and you love thinking about transportation, come join our company and work with Joe. Joe previously worked here, here and here and he love solving this thing.”
Adel Nehme: It’s great as well and you give pointers based on company size, how to fund these activities. I’ve been with companies that fly you out and do all these fancy bells and whistles, but there are ways that you can compete with that even as a lean startup.
Iv Natural Language Processing
18. What are the use cases of NLP? It helps computers to understand languages with different tasks such as speech recognition, sentiment analysis, text summarization, text classification, translation, question answering, chat bots, and named entity recognition.
19. Provide the difference between bag-of-words and TF-IDV. Bag-of-words represents text using word frequencies without context or order, whereas TF-IDV measures word importance by multiplying the term frequency or occurrences with the inverse document frequency which eliminates common unnecessary terms.
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Breaking Down That Story:
- Situation: I was new in a role, hired to use a specific technology.
- Task: I needed to synthesize data and the tech was inappropriate for the task.
- Action: I learned a new technology.
- Result: In a short amount of time I delivered a valuable automation to the company.
The answer to my question is actually I am adaptable and learn quickly, but the story demonstrates that so much more vividly.
Statistics Problems Asked By Fang & Hedge Funds
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Solutions To Statistics Interview Questions
Problem #2 Solution:
There will be two main problems. The first is that the coefficient estimates and signs will vary dramatically, depending on what particular variables you include in the model. In particular, certain coefficients may even have confidence intervals that include 0 . The second is that the resulting p-values will be misleading – an important variable might have a high p-value and deemed insignificant even though it is actually important.
You can deal with this problem by either removing or combining the correlated predictors. In removing the predictors, it is best to understand the causes of the correlation . For combining predictors, it is possible to include interaction terms . Lastly, you should also 1) center data, and 2) try to obtain a larger sample size .
Problem #9 Solution:
For X ~U we have the following:
Therefore we can calculate the mean as:
\ = \int_^xf_Xdx = \int_^\fracdx = \frac \Big|_a^b = \frac\]
Similarly for variance we want:
\ – E^2\]
And we have:
\ = \int_^x^2f_Xdx = \int_^\fracdx = \frac \Big|_a^b = \frac\]
Problem #12 Solution:
Since X is normally distributed, we can look at the cumulative distribution function of the normal distribution:
To check the probability X is at least 2, we can check :
\ = \frac = \frac \approx 43 \space \text\]
Problem #14 Solution:
and the variance is given by:
Problem #20 Solution:
Assume we have n Bernoulli trials each with a success probability of p:
\ = \frac = p\]
Types Of Interview Formats Covered In Ace Data Science Interviews Course
We will help you understand what are guesstimates, how to go about solving them and provide you with multiple examples to practice the same
Business Case Studies
You can not be a data scientist without having good domain understanding and problem solving skills. This course provides you with live case studies identifying the problems people face and provides people with a framework to Ace These Case Studies.
This course covers popular puzzles used in data science interviews across several companies hiring for data science roles.
This course covers more than 200 technical questions along with answers used in data science interviews. Use it to your advantage before you go for any data science interviews