A walkthrough from the essentials of 182+ data science interview questions from linear regression to advance analytics
Free tutorial
Created by Rupak Bob Roy
English
What you’ll learn
- Classifiers: Learn in depth confusing questions asked by the interviewers
- Regression: Learn the internal mechanism of a regressor to answer the toughest questions
- Clustering, Dimensionality Reduction, Wrapper function, Hypothesis Testing.
- Probability, Joint Probability Distribution, estimators like likelihood, MLE, cumulative response curve.
- Ranking algorithms, Power Analysis, Box Cox Transformation. TF/IDF vectorization, Eigenvalue and eigenvector
- Law of large numbers, Drawbacks of Linear Model and more.
Requirements
- Basic Math Skills
- Basic computer skills
- Basic Structured Query Language for faster understanding
- Basic Data Science understanding
Description
The Course is Designed from scratch for Beginners as well as for Experts.
*Unlimited update on Questionnaires every month*.
*Updated with Bonus: Machine Learning, Deep Learning with Python – Premium Self Learning Resource Pack Free
Learn the skills of tomorrow, the silicon valley way
Focus on extracting insights from data of any form or shape using a multitude of statistical disciplines for the purpose of creating new products & services or improving the existing ones by predicting the probability in an event. And as the enormity of data is on the rise, there is a desperate need for professionals with data science skills to get valuable insights into it. According to NYTimes there are fewer than 10,000 qualified people in the world and universities are only graduating about 100 candidates each year.
Why data science is so important?
• Twitter Since 2015, the number of posts increased 45% to more than 850,000 tweets per minute.
• YouTube usage has more than tripled in the last two years with users uploading 400 hours of new video each minute of every day.
• Instagram users like 2.5 million posts every minute!
• Google Around 4 million Google searches are conducted worldwide each minute of every day.
• Finally, data sent and received by mobile internet users 1500 000TB.
So, with the above examples of how much data gets generated, now how many hidden insights and patterns for accurate future predictions that we can actually achieve by using data science.
According to Forbes, the annual demand for Data Scientist jobs in the United States itself will increase by 364 million by 2020.
The average salary for a Data Scientist is $170,436.
What is the career progression path for data science professionals?
• Data Scientist: with a vast knowledge of Data Science, Machine Learning, and Business Intelligence tools. Data Scientist stands high as Everest.
• Data Analyst: in 2022, the world will generate data 50times more than now, and with each day that passes by the data generated is infinite with that to analyze those data, data analyst jobs will never have to see the face of recession. On LinkedIn itself, there are average 400 new jobs every 12 hours.
• Data Science Trainer: in this present date a lack of knowledge of these advanced data science techniques gives a vast opportunity to become the fountain of data science for others.
• Business analyst: with the role of defining and managing the business requirements, the business analyst takes the lead in every business decision-making process of the organization.
Who this course is for:
- Anyone looking for a career to machine learning and artificial intelligence.
- Anyone looking for a career to Big Data Engineer
- Anyone looking for a career to Data Scientist, Business analyst, Data Engineer, Analyst
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Course content
2 sections • 22 lectures • 2h 16m total lengthCollapse all sections
Introduction6 lectures • 1hr 15min
- Introduction00:09
- Data Science Interview Q’s — I. 38Questions12:17
- Data Science Interview Q’s — II. 28Questions08:25
- Data Science Interview Q’s — III. 35Questions16:55
- Data Science Interview Q’s — IV. 36Questions13:57
- Data Science Interview Q’s — V.37Questions22:51
Bootcamp:- A – Z Data Science Course16 lectures • 1hr 2min
- Scribd Link00:01
- SlideShare00:01
- Data Science Bootcamp: Types of probability distribution. Lab 106:10
- Data Science Bootcamp: Types of probability distribution. Lab 206:07
- Data Science Bootcamp: Types of probability distribution. Lab 301:48
- Data Science Bootcamp: Types of probability distribution. Lab 404:28
- Data Science Bootcamp: Hypothesis Testing – Lab. I05:50
- Data Science Bootcamp: Hypothesis Testing – Lab. II03:33
- Data Science Bootcamp: Hypothesis Testing II – Lab. I06:37
- Data Science Bootcamp: Hypothesis Testing II – Lab. II02:19
- Data Science Bootcamp: Multiple Sample Test – Lab. I02:45
- Data Science Bootcamp: Multiple Sample Test – Lab. II03:21
- Data Science Bootcamp: Multiple Sample Test – Lab. III05:25
- Data Science Bootcamp: Multiple Sample Test – Lab. IV04:51
- Neural Network using excel08:54
- Python Premium Learning Pack00:01
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