Data Science Interview Prep Book

Why Is Statistics Considered Harder Subjects To Learn For Students

The foremost reason students consider statistics as one of the toughest subjects is that statistics have complex formulas. If you look at the statistics formula, you find that they are arithmetically a little bit complex.

Moreover, each formula is utilized in a specific situation. That is why students struggle to understand which formula they should go with.

Apart from this, it is also blamed that teachers make statistics more complicated. The reason could be that the teachers are unable to teach statistics in an easier way to the students.

It is also noticeable that students cannot learn statistics until they do not apply it in real life. But to apply statistics in real life, students must know how to analyze the data.

That is why we can say that it is just as if you want to learn cooking, then start cooking. In the same manner, if you want to learn statistics, then start analyzing the data.

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Tip #: Seek Out Data Science Mentorship

One of the best ways to prepare for data science interviews is to seek out data science mentorship.

A mentor can provide you with guidance and support as you navigate the data science job market.

They can help you polish your resume, hone your interview skills, and give you insights into the data science industry.

Moreover, they might also point you in the right direction on skills to learn within the industry.

If you dont have a data science mentor, consider joining a data science mentorship program like Springboards.

Alternatively, you can ask a senior colleague at your organization or reach out to someone on LinkedIn to source mentors.

With the right mentorship, Im certain your data science job search will be accelerated to the next level.

With the help of a data science mentor, youll be well on your way to impressing in data science interviews and landing your dream job!

If You Want One Of The Best Data Science Interview Books For All Abilities Then Look No Further

Build a Career in Data Science by Emily Robinson and Jaqueline Nolis takes more of a soft skills approach to data science.

Instead of teaching you recipes and programming languages, youll explore things like:

• how to land a job in data science
• the lifecycle of a data science project
• how to become a manager

And much more.

What youll do in this book:

• Discover data science & data science companies
• Explore how to acquire data science skills
• Learn how to build a portfolio
• Get advice for finding a data science job

This includes searching for the right job and creating resumes and cover letters.

Importantly, it also covers what to expect at your data science interview.

Build a Career in Data Science

After that, Build a Career in Data Science covers what to expect the first few months on the job.

Looking for a data science interview course? Check out The Data Science Interview Handbook on Educative.io.

Finally, youll discover ways to grow in your role as a data scientist. From what to do when you experience failure, to finding your perfect data science community, we loved the insights in this chapter.

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Introduction To Machine Learning With Python: A Guide For Data Scientists

Andreas C. Müller, Sarah GuidoLatest edition: illustrated, reprint, revisedPublisher: OReilly Media, Incorporated, 2016

This book will show you practical techniques to develop your own machine learning solutions if you use Python, even if you are a newbie. Machine learning applications are only restricted by your creativity now that there is so much data available. Youll learn how to use Python and the sci-kit-learn library to develop a successful machine-learning application. The tone is pleasant and straightforward. Although machine learning is a hard topic, you should be able to design your own ML models after practicing with the book. You will gain a solid understanding of machine learning concepts.

The Art Of Data Science A Guide For Anyone Who Works With Data

About the book This data science book describes the process of analyzing data. Applicable to both practitioners and managers in data science, it provides an amazing overview of the data analysis workflow. It also gives an effective overview of how data analysis is primarily an art that involves iterative processes, with information learned at every step.

• Roger D. Peng and Elizabeth Matsui
• Price 20.28 USD

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Situational Question Based On The Resume

If you have a gap on your resume, recruiters will often ask about it. Theres no need to panic. Just be honest about why you took a professional break, and explain how youve gotten reacquainted with the industry.

Candidates without an academic background in computer science or math might get asked why they didnt pursue those fields. Answer this question by explaining why you chose an unconventional route.

Further reading: here is a guide with data science interview preparation tips to know what to expect from your data science interview

Best Python Books For Data Science And Machine Learning In 2022

Hello guys, if you want to learn Data Science and Machine learning with Python and looking for the best Python books for Data Science and ML then you have come to the right place.

In the past, I have shared the best Python courses for Data Science and ML, and today, I m going to share the best books to learn Data Science and Machine learning with Python.

Python is a universal language that is used by both data engineers and data scientists and probably the most popular programming language, as well.

All the Data Scientists I have spoken to, and many in my friend circle just love Python, mainly because it can automate all the tedious operational work that data engineers need to do.

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Bayesian Methods For Hackers

Rating: 4.3/5

Heres another free read on Bayesian statistics and programming. The cool thing about this one is that the chapters are in Jupyter Notebook form, so its easy to run, edit, and tinker with all of the code you come across.

A book on statistics specifically for data scientists! This 2nd edition includes valuable Python examples.

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Data Structures & Algorithms

Interview Cake: My subscription to this service was the best money I ever spent. Thank goodness I did because it prepared me for every data structure and algorithm question that came my way. They take you through the theory and how to code it like no other resource I have seen. Be prepared though, its pricey but worth it.

Cracking the Coding Interview: This book is the best place to start learning and reviewing software engineering aspects of your data interviews. The reality is that software engineering fundamentals are generally expected of those of us in this field, this book really helps cover any gaps you may have.

Introduction to Algorithms: Be prepared to invest time in this one, but it is worth it. This textbook covers algorithms in depth. I re-coded the pseudo-code in Python and it was the perfect supplement to Interview Cake.

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What Are The Best Books For Your Data Science Career

The 3 best books for Data Scientists who are trying to succeed in their career and land data science jobs are Ace the Data Science Interview for interview prep, the Data Science Handbook for career and life insights from top Data Scientists, and So Good They Can’t Ignore You to help you more broadly design a successful career.

Ace the Data Science Interview

Ace the Data Science Interview is the best book to prepare for a Data Science Interview. It covers the most frequently-tested topics in data interviews like Probability, Statistics, Machine Learning, SQL query questions, Coding , and Product Analytics. With 201 data science interview questions to practice with, this book is a must-read for those trying to land data jobs at FAANG, tech startups, or on Wall Street. Itâs also a great book to prepare for Data Analyst and Machine Learning interviews too.

Of course, we wrote this Amazon Best-Seller, so weâre a tiny bit prejudiced!

If you’re looking for the eBook of Ace the Data Science Interview, we’re sorry to announce that there aren’t any online available. However, you’ll find many of the SQL interview tips from the book on DataLemur’s 6000-word guide to SQL interview prep. On DataLemur, you’ll also find 100+ SQL Interview Questions from FAANG and plenty more Machine Learning Interview questions too!

You can also find 9 other Data Science Interview books which we recommend, which complement the material from Ace the Data Science Interview very nicely!

Be The Outlier Is One Of The Best Data Science Interview Books If Youre In The Final Stages Of Preparing For Your Data Science Interview

I would highly recommend this book to any student with dreams of working in a data science role and acing their interview!

Ideal for: data science beginners Major topics: data science fundamentals, machine learning, statistics

The Data Science Design Manual by Steven Skiena isnt specifically written for data science interview preparation. Rather, its a manual that has elements of interview prep.

So while we dont think it should be the only data science interview book you look into, we think you should definitely check it out.

The Data Science Design Manual

Treated as a textbook and reference, youll focus on the fundamentals of data science so you can collect, analyze and interpret data.

Instead of learning about data-analysis tools or programming languages, youll focus on higher-level design principles.

In addition, youll learn how data science crosses paths with machine learning, computer science and statistics.

Who is Steven Skiena?Steven Skiena is the author behind the acclaimed book The Algorithm Design Manual.The Data Science Design Manual is another banger that doesnt skimp on the action-packed, punch of information we crave.

There are real-world stories throughout the book. Youll also find plenty of problems to work on with detailed solutions and explanations.

Theres also an online portion that contains slides and video lectures.

Practical Statistics

Fun fact: there are over 6,700 crab species in existence.

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Crack Your Data Science Interview

If you need help with your prep, join Interview Kickstartâs Data Science Interview Course â the first-of-its-kind, domain-specific tech interview prep program designed and taught by FAANG+ instructors. to learn more about the program.

IK is the gold standard in tech interview prep. Our programs include a comprehensive curriculum, unmatched teaching methods, FAANG+ instructors, and career coaching to help you nail your next tech interview.

Ace The Data Science Interview

Paid: $30

by Emily Robinson, Jacqueline Nolis

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager.

Paid: Lots of free preview available $20

This book collects many of their discussions from the podcast Not SoStandard Deviations and distillsthem into a readable format.

Paid: Pay what you want for the ebook, minimum $0

by Roger Peng

This book draws a complete picture of the data analysis process, fillingout many details that are missing from previous presentations. Itpresents a new perspective on what makes for a successful data analysisand how the quality of data analyses can be judged.

Paid: Pay what you want for the ebook, minimum $0

A Guide to Training and Managing the Best Data Scientists. Learn whatyou need to know to begin assembling and leading a data scienceenterprise.

Paid: Pay what you want for the PDF, minimum $0

by Ayodele Odubela

This book is for anyone intersted in Data Science, but is unsure whereto start. Cut through the noise and learn my best tips for understandingMachine Learning with insight from my 4 years of industry experience.Learn the math as it applies to real-life data projects and get anunderstanding of fairness, ethics, and accounability in AI.

Paid: $20

by Roy Keyes

Paid: varies $25

by Chip Huyen

by Oscar Baruffa

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What Are The Best Books For Data Scientists To Improve Their Business And Product Management Skills

The 4 books we recommend Data Scientists to read to improve their business intuition and product-sense are the Personal MBA, BCGs on Strategy, Lean Analytics, and the Product Management classic Inspired.

Personal MBA

Letâs face it: as a Data Scientist, often your projectâs success isnât based on the cleverness of your technical solution, but on your ability to work effectively with business stakeholders. So, how do you work better with business people? Speak their language! This book is essentially a crash-course on the most important terms, concepts, and mental models in business, at 0.01% of the price of going to business school.

The Boston Consulting Group on Strategy

Want to be a better âbig-pictureâ thinker . This book, written by many partners at BCG, talks about concepts like organization design, change management, and developing business strategies. The frameworks and terminology in this book have permeated boardrooms everywhere itâs much bigger than BCG! If youre frequently presenting data-driven recommendations to the C-Suite, or doing analysis that informs the companyâs larger strategic vision, you need to read this book.

Lean Analytics: Use Data to Build a Better Startup Faster

Inspired: How to Create Tech Products Customers Love

About The Authors: Nick Singh & Kevin Huo

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What Do You Understand About The True

The true-positive rate is the ratio between the number of true positives and the sum of the number of true positives and false negatives . The false positive rate is the ratio between the number of false positives and the sum of the number of false positives and true negatives.

TPR = TP/TP+FN

FPR = FP/FP+TN

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Cracking The Pm Interview: How To Land A Product Manager Job In Technology

I recommend this book not just to aspiring PMs, but to anyone who works closely with PMs. Software Engineers and Designers who read this book will be able to better communicate and empathize with their PM teammates a valuable skill for the workplace. The majority of the advice in this book is easily generalizable to other technical roles. For example, many of the tips in my â36 Resume Rules For Software Engineersâ come from Cracking the PM Interview. The section on behavioral interviews is also excellent for all kinds of job seekers.

What Is A Linear Regression Model List Its Drawbacks

A linear regression model is a model in which there is a linear relationship between the dependent and independent variables.

Here are the drawbacks of linear regression:

• Only the mean of the dependent variable is taken into consideration.
• It assumes that the data is independent.
• The method is sensitive to outlier data values.

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Case In Point: A Consulting Interview Prep Book For More Structured Thinking

If you are an aspiring Consultant or Product Manager, I highly recommend this consulting interview classic. Reading this book helped me better structure my thinking, which helped me get into Deloitteâs summer leadership program my 1st year of college. Re-working through the case studies many years later helped me transition from my SWE job at Facebook to my business role at SafeGraph.

Nervous For Your Data Science / Data Engineering Interview Start Here

Data Science, Data Engineering, Business Intelligence, Data Analysis, and other related positions fall at an intersection of coding, databases, statistics, and business/product. This blend of subjects results in an engaging and challenging career. The interviews are similarly engaging and challenging. 🙂

When I was studying for my data interviews, I noticed there was not one holy grail. I was googling, review SQL fast, data science interview questions, statistics questions, data model interview questions, and more endlessly. I found a lot of resources and also a lot of gaps which is why I wrote this list.

Im putting together this list to help others who are taking their next step in their data career. While the title says, ultimate, Im hoping to keep collecting resources from others who have also been through the process. I want to get feedback on what worked & what didnt. I want to keep growing this list.

How can you help? Share this with your friends! And also reach out to me with resources that worked for you!

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Mining Of Massive Datasets

Jure Leskovec, Anand Rajaraman, Jeffrey David UllmanLatest edition: 2nd editionPublisher: Dreamtech Press

The ubiquity of the Internet and Internet commerce has resulted in numerous enormously big datasets from which data mining can extract information. It concentrates on the analysis of extremely huge datasets. With the help of this book, one can learn how to construct large-scale production-level models. Mining data streams, MapReduce, creating recommendation systems, link analysis, dimensionality reduction, and other subjects are addressed in depth in this book. The authors demonstrate how to mine data that arrive too quickly for exhaustive processing using locality-sensitive hashing and stream-processing methods.

Brushing Up The Right Skillset From The Right Resources For The Right Job

These are unprecedented times where many of us are looking to switch or land a job. Interview preparation has come to the limelight. And interviews are a big deal for everyone.

Uncertainty, randomness, and human errors make an interview damn scary. Adrenaline rushing through your veins, you are on the verge of messing it all up.

Preparedness is the only solution to minimize your losses during an interview. As Benjamin Franklin said:

One of my last weeks post was on building an effective Data Science portfolio where I shared a comprehensive and actionable guide to building a portfolio.

A good portfolio most of the time helps you get the first call and if you really know your thing, you are almost 90% there. The rest 10% is accounted for by the three traits mentioned in the first line of this post.

So, this post intends to provide you actionable tips and resources to prepare well for your next data science interview.

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