Data Science Advanced Analytics Interview Prep. Kit 182+

Data Science Advanced Analytics Interview Prep. Kit – 182+, available at Free, has an average rating of 4.44, with 22 lectures, based on 9 reviews, and has 2661 subscribers.

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You will learn about 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. This course is ideal for individuals who are Anyone looking for a career to machine learning and artificial intelligence. or Anyone looking for a career to Big Data Engineer or Anyone looking for a career to Data Scientist, Business analyst, Data Engineer, Analyst It is particularly useful for Anyone looking for a career to machine learning and artificial intelligence. or Anyone looking for a career to Big Data Engineer or Anyone looking for a career to Data Scientist, Business analyst, Data Engineer, Analyst.

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Summary

Title: Data Science Advanced Analytics Interview Prep. Kit – 182+

Price: Free

Average Rating: 4.44

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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

Target Audiences

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

The Course is Designed from scratch for Beginners as well as for Experts.

*Unlimited update on Questionnaires every month*.

*Updated withBonus: 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?

? TwitterSince 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.

Course Curriculum

Chapter 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

Chapter 2: Bootcamp:- A – Z Data Science Course

Lecture 1: Scribd Link

Lecture 2: SlideShare

Lecture 3: Data Science Bootcamp: Types of probability distribution. Lab 1

Lecture 4: Data Science Bootcamp: Types of probability distribution. Lab 2

Lecture 5: Data Science Bootcamp: Types of probability distribution. Lab 3

Lecture 6: Data Science Bootcamp: Types of probability distribution. Lab 4

Lecture 7: Data Science Bootcamp: Hypothesis Testing – Lab. I

Lecture 8: Data Science Bootcamp: Hypothesis Testing – Lab. II

Lecture 9: Data Science Bootcamp: Hypothesis Testing II – Lab. I

Lecture 10: Data Science Bootcamp: Hypothesis Testing II – Lab. II

Lecture 11: Data Science Bootcamp: Multiple Sample Test – Lab. I

Lecture 12: Data Science Bootcamp: Multiple Sample Test – Lab. II

Lecture 13: Data Science Bootcamp: Multiple Sample Test – Lab. III

Lecture 14: Data Science Bootcamp: Multiple Sample Test – Lab. IV

Lecture 15: Neural Network using excel

Lecture 16: Python Premium Learning Pack

Instructors

Rupak Bob Roy
Data Scientist For Advanced Analytics

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4 stars: 3 votes

5 stars: 4 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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