Published 6/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 545.70 MB | Duration: 2h 16m
A walkthrough from the essentials of 182 data science interview questions from linear regression to advance analytics
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.*Updated with Bonus: Machine Learning, Deep Learning with Python – Premium Self Learning Resource Pack FreeLearn the skills of tomorrow, the silicon valley wayFocus 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.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Data Science Interview Q’s – I. 38Questions
Lecture 3 Data Science Interview Q’s – II. 28Questions
Lecture 4 Data Science Interview Q’s – III. 35Questions
Lecture 5 Data Science Interview Q’s – IV. 36Questions
Lecture 6 Data Science Interview Q’s – V.37Questions
Section 2: Bootcamp:- A – Z Data Science Course
Lecture 7 Scribd Link
Lecture 8 SlideShare
Lecture 9 Data Science Bootcamp: Types of probability distribution. Lab 1
Lecture 10 Data Science Bootcamp: Types of probability distribution. Lab 2
Lecture 11 Data Science Bootcamp: Types of probability distribution. Lab 3
Lecture 12 Data Science Bootcamp: Types of probability distribution. Lab 4
Lecture 13 Data Science Bootcamp: Hypothesis Testing – Lab. I
Lecture 14 Data Science Bootcamp: Hypothesis Testing – Lab. II
Lecture 15 Data Science Bootcamp: Hypothesis Testing II – Lab. I
Lecture 16 Data Science Bootcamp: Hypothesis Testing II – Lab. II
Lecture 17 Data Science Bootcamp: Multiple Sample Test – Lab. I
Lecture 18 Data Science Bootcamp: Multiple Sample Test – Lab. II
Lecture 19 Data Science Bootcamp: Multiple Sample Test – Lab. III
Lecture 20 Data Science Bootcamp: Multiple Sample Test – Lab. IV
Lecture 21 Neural Network using excel
Lecture 22 Python Premium Learning Pack
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
Homepage
https://www.udemy.com/course/data-science-advanced-analytics-interview-preparation-kit/
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