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Master data science concepts!
Extremely Hands-On... Incredibly Practical... Unbelievably Real!
This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.
In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it!
This course is for anyone who wants questions to practice for their next Data Science Interviews or want to test the understanding of their concepts.
The course covers a vast range of topics from probability and statistics to Data Science and Machine Learning Algorithms. This course doesn't waste your valuable time.
If you do not get the answers to a question, you can understand how to solve them from our well-detailed explanations.
Also, please note that the course will be updated in the near future.
This is the most comprehensive Test Series online which will help you ace your Data Science/Machine Learning interviews.
Being a data scientist is one of the most lucrative and future proof careers with Glassdoor naming it the best job in America for the third consecutive year in a row with great future growth prospects and a median base salary of $110,000. I have recently made the transition from being a PhD student in Computer Science to a Senior Data Scientist at a large tech company. In this course I give you all the questions and answers that I used to prepare for my data science interviews as well as the questions and answers that I now expect when I am giving interviews to potential data science candidates. The course provides a complete list of 150+ questions and answers that you can expects in a typical data science interview including questions on machine learning, neural networks and deep learning, statistics, practical experience, big data technologies, SQL, computer science, culture fit, questions for the interviewer and brainteasers.
What questions will you learn the answer to?
What is the bias-variance tradeoff?
How would you evaluate an algorithm on unbalanced data?
When would you use gradient descent (GD) over stochastic gradient descent (SDG), and vice-versa?
Why do segmentation CNNs typically have an encoder-decoder style / structure?
Why we generally use Softmax non-linearity function as last operation in-network?
You randomly draw a coin from 100 coins — 1 unfair coin (head-head), 99 fair coins (head-tail) and roll it 10 times. If the result is 10 heads, what is the probability that the coin is unfair?
Given the following statistic, what is the probability that a woman has cancer if she has a positive mammogram result? 1% of women have breast cancer, 90% of women who have breast cancer test positive on mammograms and 8% of women will have false positives.
Write a SQL query to get the second highest salary from the Employee table. If there is no second highest salary the query should return null.
What is the average time complexity to search an unsorted array?
Why do you want to work here?
How can you generate a random number between 1 – 7 with only a die?