Greetings! Happy Chinese New Year. May the Year of the Ox be the year of happiness and properity.
My name is Leihua Ye. I wear multiple hats. I’m a Ph.D. researcher at the University of California, Santa Barbara for the day and a Top Writer in Artifical Intelligence, Education, and Technology for the night.
I’ve been on the platform for over a year and created 40+ original content on various niches under the Data Science umbrella, including Statistics, Experimentation & Causal Inference, Machine Learning, Programming (R, Python, and SQL), and Research Design.
This portal post serves you to find the…
Online experimentation has become the industry standard for product innovation and decision-making. With well-designed A/B tests, tech companies can iterate their product lines quicker and provide better user experiences. Among FAANG, Netflix is the company most open about its experimental approach. In a series of posts, Netflix has introduced how to improve experimentation efficiency, reduce variance, quasi-experiments, key challenges, and more.
Indeed, online controlled experiments offer a high level of internal validity after controlling for all other external factors and only allow for one factor (the treatment condition) to vary. Unlike other statistical tools (e.g., …
Python is a versatile script-based programming language with a wide application in Artificial Intelligence, Machine Learning, Deep Learning, and Soft Engineering. Its popularity benefits from the various Data Types that Python stores.
Dictionary is the natural choice if we have to store key and value pairs, as in today’s Question 5. String and list are a pair of twin sisters that come together and solve string manipulation questions. Set holds a unique position as it does not allow duplicates, a unique feature that allows us to identify the repetitive and non-repetitive items. Well, tuple is the least frequently asked…
Python coding interviews come in different shapes and forms, and each type has its unique characteristics and approaches. For example, String Manipulation questions expect candidates to have a solid grasp of element retrieval and access. Data Type Switching questions test your understanding of the tradeoffs and unique traits with each type.
However, the math question is different. There is no consistent way of testing. Instead, you have to spot the data pattern and code it up in Python, which sounds daunting at first but totally doable after practice.
In this post, I elaborate and live-code 5 real interview questions…
Array and string manipulation are among the most heavily tested topics in Data Science and Soft Engineering interviews. This is the best type of interview question that tests candidates’ ability to think programmatically and coding fluency. To perform well, we have to be familiar with the basic operations of arrays/strings, matrix and its row/column structures, and Python syntax.
In two similar blog posts, I’ve touched upon the basics and live-coded several real interview questions.
Data Science Interviews cover a wide range of topics, and interviewers frequently ask us to explain the most fundamental concepts. It’s more likely to ask questions like why you choose L1 over L2 than building up a Machine Learning algorithm from scratch.
My Data Science professional network has told me repeatedly that they do not expect job candidates to know every algorithm. Instead, they expect a high level of familiarity with the fundamentals. It makes total sense. You can quickly pick up a new algorithm after establishing a solid ground.
Statistics and Machine Learning are inseparable twins, and these…
As many of you know, I’m transitioning my programming language from R to Python and have been practicing coding in Python for the past year. Not to brag, but I’m constantly amazed by my progress and quick turnover from the guy who only knows “Hello, World!” to the one who comfortably solves complex coding challenges on Leetcode.
For example, I live-code through fivedata science interview questions here:
Python offers so many data types, and dictionaries are on the top of the list. Mastering Python dictionaries helps us crack any coding interviews. …
Coding in Python could be daunting for junior data scientists. Trust me, as I’ve been there. Once, I struggled to figure out an easy level question on Leetcode and made no progress for hours.
For the past year, I’ve been deliberately trained to code in Python. My biggest takeaway is to know when I should advance to the next level and challenge myself with more advanced questions! There is no room for improvement if we stay in the comfort zone and practice old and familiar code.
To excel in Python programming, we have to master the basics and advance…
As the engineering culture keeps growing, Data Scientists often team up with other engineers to build pipelines and perform a ton of soft engineering stuff. Job candidates are expected to face extensive coding challenges in R/Python and SQL (Essential and Tricky SQL).
From my past interview experiences, simply being able to code is far from enough. What differentiates experienced programmers from code-camp-trained beginners is the ability to dissect the big question into smaller pieces and then code it up.
It’s my “aha” moment.
For the past few months, I’ve been deliberately practiced to dissect the code and walk through…
Let’s begin today’s topic with two questions.
What is your first impression of learning algorithms?
What is an algorithm anyway?
Nowadays, there are dozens of high-flying buzzwords like “bit,” “Intelligence,” “Artificial Intelligence,” “learning,” “Supervised Learning,” and “algorithm” is on the top shelf.
But few people understand what these words really stand for, less to say how they work.
“What it is?
What does it do?”
For me, an algorithm is a process of how we want computers to handle information, what comes first what second.
As simple as that.