The ultimate guide to data science interviews. Get interview-ready in just a few hours with proven strategies developed by FAANG engineers. Practice with interview questions from top companies.

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This course will answer all your queries about starting your career in data science. There is only one prerequisite to taking this course—you are learning to program or working in a job where programming is used.

In this course, your queries regarding career in data science get answered. These include career transitioning or starting, efficient job search, common mistakes that people make, educational requirements, list of topics to study, resume creation, do's and don'ts, job search sites, interview process and the types of interview questions and answers. You will learn about how to navigate through various hurdles to reach your career goals.

After completing this course, you will know how to prepare for data science interviews. Specifically, what you should do and what you should not do. This course will guide you to becoming a self-taught data scientist and speed up your learning path toward careers in data science.

Deep Dive Into Data Science Interview

\space \space \space \space \space \space \space \space \space \space \space \space \space P(Last\space person\space sit\space on \space his\space seat)=\frac{Total\space number\space of\space seats\space assigned\space to\space him}{Total\space number\space of\space seats\space he\space can\space sit\space on}\\=\frac{1}{2}

P(A\space wins\space any\space of\space the\space first\space 3\space matches)={}_n \mathrm{ C }_xp^x(1-p)^{n-x}\\\space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space ={}_6 \mathrm{ C }_3p^3(1-p)^{6-3}

P(A\space wins\space the\space last\space  match)=p

P(A\space wins\space the\space game\space at\space the\space 7th\space match)={}_6 \mathrm{ C }_3p^3(1-p)^{6-3}\times p\\ \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space ={}_6 \mathrm{ C }_3p^4(1-p)^{6-3}

P(Student\space will\space graduate\space if\space  female)=\frac{P(Student\space will\space graduate\space and\space student\space is\space female)}{P(Student\space is\space female)}

P(Student\space will\space graduate\space if\space  female)=0.6\\ \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space P(Student\space is\space female)=0.89

P(Student\space will\space graduate\space and\space student\space is\space female)=[P(Student\space will\space graduate\space if \space  female)\times\space P(Student\space is\space female)]\\ \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space   =0.60\times\space0.89\\\space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space \space =0.534

The probability questions and answers in this lesson will help you understand the types of probability questions you can expect in data science interviews.

Getting Started

Data Science Job Outline

What to Study

Portfolio Making

Expertise and Readiness

Efficient Data Science Job Search

The Interview

Types of Questions and Answers

Epilogue

Probability