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Sprint the Data Science Interview

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Sprint the Data Science Interview, available at $19.99, has an average rating of 4.05, with 66 lectures, based on 16 reviews, and has 102 subscribers.

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You will learn about Most frequently asked questions in data science interviews Quickly learn the basics required to understand and work on data science projects Bottom up approach to start data science learning Cracking the data science interview Cracking the Machine Learning Interview Machine Learning Pipelines This course is ideal for individuals who are Anyone who wants to prepare for a Data Science Interview or Anyone who has limited time and wants to quickly get ready for Data Science Interview or Machine Learning Interview preparation or Deep Learning or Statistics It is particularly useful for Anyone who wants to prepare for a Data Science Interview or Anyone who has limited time and wants to quickly get ready for Data Science Interview or Machine Learning Interview preparation or Deep Learning or Statistics.

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Summary

Title: Sprint the Data Science Interview

Price: $19.99

Average Rating: 4.05

Number of Lectures: 66

Number of Published Lectures: 66

Number of Curriculum Items: 66

Number of Published Curriculum Objects: 66

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

  • Most frequently asked questions in data science interviews
  • Quickly learn the basics required to understand and work on data science projects
  • Bottom up approach to start data science learning
  • Cracking the data science interview
  • Cracking the Machine Learning Interview
  • Machine Learning Pipelines

Who Should Attend

  • Anyone who wants to prepare for a Data Science Interview
  • Anyone who has limited time and wants to quickly get ready for Data Science Interview
  • Machine Learning Interview preparation
  • Deep Learning
  • Statistics

Target Audiences

  • Anyone who wants to prepare for a Data Science Interview
  • Anyone who has limited time and wants to quickly get ready for Data Science Interview
  • Machine Learning Interview preparation
  • Deep Learning
  • Statistics

Data Science is a multidisciplinary which involves Computer Science, Mathematics, Statistics and Domain Knowledge. Preparation for an interview in data science would require knowledge and expertise in a subset of the above disciplines. To summarize one would require skills on Algorithms, SQL, Probability, Statistics, Econometrics, Problem Solving, Machine Learning, Optimization, storytelling and presentation skills. In this course, I would cover few questions on Probability, Statistics and different aspects of Machine Learning. This course can act as a quick preparatory guide to kick start your preparation for a data science interview.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Probability Theory

Lecture 1: Bangalore Rain Prediction

Lecture 2: Intuition

Lecture 3: Birthday Chance

Lecture 4: Payout

Lecture 5: Unfair Coin

Chapter 3: Statistics

Lecture 1: Probability Distributions

Lecture 2: Sampling

Lecture 3: Central tendency

Lecture 4: Theorem

Lecture 5: Median

Chapter 4: Machine Learning – Data Preprocessing

Lecture 1: Why to do?

Lecture 2: First things first

Lecture 3: Categorical variables

Lecture 4: Missing Values

Lecture 5: Box-Cox

Chapter 5: Machine Learning – Know Your Algorithms

Lecture 1: Know the differences

Lecture 2: Know how they work

Lecture 3: Know the best one

Lecture 4: Know the role of functions

Lecture 5: XOR

Lecture 6: Kernels

Lecture 7: Reinforcement Learning

Lecture 8: Activation functions

Lecture 9: Know the Mechanism

Lecture 10: Forests

Lecture 11: Ensemble

Lecture 12: Support Vectors

Lecture 13: Can you Differentiate

Lecture 14: Optimization

Lecture 15: Auto Encoder

Lecture 16: Ensemble learning

Lecture 17: Parametric Models

Lecture 18: SVM

Lecture 19: Compare the algorithms

Lecture 20: Probability of class

Lecture 21: Cross Entropy

Lecture 22: Soft Max

Chapter 6: Machine Learning – Know your problems

Lecture 1: Ugly model

Lecture 2: Bad Model

Lecture 3: Unbalanced class

Lecture 4: Data Wrangling

Lecture 5: Trade off

Lecture 6: Data Corruption

Lecture 7: Outliers

Lecture 8: Datasets

Lecture 9: Flexible model

Lecture 10: Cross verify

Lecture 11: Null gradient

Lecture 12: Bagging

Lecture 13: Batch Normalization

Lecture 14: Exploding gradient

Lecture 15: Regularization

Chapter 7: Machine Learning – Rapid Fire

Lecture 1: 1×1 convolution

Lecture 2: Dropout

Lecture 3: Document representation

Lecture 4: Plagiarism

Lecture 5: Define

Lecture 6: Ensemble

Lecture 7: Clustering types

Lecture 8: Discriminative model

Lecture 9: Distance

Lecture 10: Combine Predictions

Lecture 11: Probability Distributions

Chapter 8: Data Science – Your Contribution

Lecture 1: Share your experience

Chapter 9: Test your coding skills

Lecture 1: Test your coding skills

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 1 votes
  • 3 stars: 3 votes
  • 4 stars: 4 votes
  • 5 stars: 7 votes

Frequently Asked Questions


How long do I have access to the course materials?


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


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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