What are the best data science questions currently available to purchase? This article attempts to give you some answers to guide you in the process of purchasing the best data science questions to suit your needs. In our buying guide, we outline certain features to consider when buying data science questions. It is important to take time and research before you commit to purchasing.
Best data science questions
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Best data science questions reviews
1. Heard In Data Science Interviews: Over 650 Most Commonly Asked Interview Questions & Answers
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A collection of over 650 actual Data Scientist/Machine Learning Engineer job interview questions along with their full answers, references, and useful tips2. Pop Science: Serious Answers to Deep Questions Posed in Songs
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A Pulitzer Prizewinning journalist uses data, facts, and science to deliver hilarious, fascinating answers to some of the most famous questions in pop music history.Is there life on Mars? Where have all the flowers gone? Pop songs can pose excellent questions and James Ball has given them the answers they deserve.The Times(UK)
Some of the most famous questions of our time have come to us in pop songs. What is love? How soon is now? How do you solve a problem like Maria? But do you know the answers?
Breaking down lyrics from Bob Dylan, Queen, Rihanna, the Ting Tings, Billy Joel, and a variety of other genre- and decade-spanning artists with colorful graphs and Venn diagrams,Pop Sciencereveals the exact points where lowbrow pop culture and the highest science and philosophy meet. By revealing the economic status of doggies in windows, what war is good for, and what becomes of the brokenhearted, James Ball uncovers what we have always knownthat pop music is the key to life itself.
3. Cracking The Machine Learning Interview
Description
"A breakthrough in machine learning would be worth ten Microsofts." -Bill GatesDespite being one of the hottest disciplines in the Tech industry right now, Artificial Intelligence and Machine Learning remain a little elusive to most.The erratic availability of resources online makes it extremely challenging for us to delve deeper into these fields. Especially when gearing up for job interviews, most of us are at a loss due to the unavailability of a complete and uncondensed source of learning.
Cracking the Machine Learning Interview
- Equips you with 225 of the best Machine Learning problems along with their solutions.
- Requires only a basic knowledge of fundamental mathematical and statistical concepts.
- Assists in learning the intricacies underlying Machine Learning concepts and algorithms suited to specific problems.
- Uniquely provides a manifold understanding of both statistical foundations and applied programming models for solving problems.
- Discusses key points and concrete tips for approaching real life system design problems and imparts the ability to apply them to your day to day work.
This book covers all the major topics within Machine Learning which are frequently asked in the Interviews. These include:
- Supervised and Unsupervised Learning
- Classification and Regression
- Decision Trees
- Ensembles
- K-Nearest Neighbors
- Logistic Regression
- Support Vector Machines
- Neural Networks
- Regularization
- Clustering
- Dimensionality Reduction
- Feature Extraction
- Feature Engineering
- Model Evaluation
- Natural Language Processing
- Real life system design problems
- Mathematics and Statistics behind the Machine Learning Algorithms
- Various distributions and statistical tests
This book can be used by students and professionals alike. It has been drafted in a way to benefit both, novices as well as individuals with substantial experience in Machine Learning.
Following Cracking The Machine Learning Interview diligently would equip you to face any Machine Learning Interview.
4. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists
Description
The Data Science Handbook contains interviews with 25 of the world s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice. In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.5. A collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (II): Hands-on Big Data and Machine ... Programming Interview Questions) (Volume 7)
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A Collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark II Hands On Big Data and Machine LearningDescription
A collection of Machine Learning interview questions in Python and Spark6. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
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R for Data Science Import Tidy Transform Visualize and Model DataDescription
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.
Youll learn how to:
- Wrangletransform your datasets into a form convenient for analysis
- Programlearn powerful R tools for solving data problems with greater clarity and ease
- Exploreexamine your data, generate hypotheses, and quickly test them
- Modelprovide a low-dimensional summary that captures true "signals" in your dataset
- Communicatelearn R Markdown for integrating prose, code, and results
7. Data Science: Create Teams That Ask the Right Questions and Deliver Real Value
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Data Science Create Teams That Ask the Right Questions and Deliver Real ValueDescription
Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business.
Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story.
Insight!: How to Build Data Science Teams that Deliver Real Business Valueshows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts:
- You should mine your own company for talent. You cant change your organization by hiring a few data science superheroes.
- You should form small, agile-like data teams that focus on delivering valuable insights early and often.
- You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry.
- Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organizations data
- Understand key data science terms and concepts
- Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member
- Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value
- Use sprints and storytelling to help your team stay on track and adapt to new knowledge
