Search results for Probability Statistics

Found 406 documents, 5949 searched:

• Must-haves on Your Data Science Resume

  Recruiters look at a resume for 7.4 seconds before making a decision on the candidate. So that means you have basically less than 10 seconds to make a good impression. 10 seconds is not a lot of time; especially when you really want this job.

  https://www.kdnuggets.com/2022/06/musthaves-data-science-resume.html

• How to Become a Machine Learning Engineer

  A machine learning engineer is a programmer proficient in building and designing software to automate predictive models. They have a deeper focus on computer science, compared to data scientists.

  https://www.kdnuggets.com/2022/05/become-machine-learning-engineer.html

• The Definitive Guide To Switching Your Career Into Data Science

  Colossal amounts of data need to be dealt with by specialists. It’s no wonder then that the job prospects in this industry are expected to rise much faster than in other occupations.

  https://www.kdnuggets.com/2022/05/definitive-guide-switching-career-data-science.html

• The “Hello World” of Tensorflow

  In this article, we will build a beginner-friendly machine learning model using TensorFlow.

  https://www.kdnuggets.com/2022/05/hello-world-tensorflow.html

• Machine Learning Key Terms, Explained

  Read this overview of 12 important machine learning concepts, presented in a no frills, straightforward definition style.

  https://www.kdnuggets.com/2016/05/machine-learning-key-terms-explained.html

• Best Data Science Career Tracks of 2022

  Top-rated data science tracks consist of multiple project-based courses covering all aspects of data. It includes an introduction to Python/R, data ingestion & manipulation, data visualization, machine learning, and reporting.

  https://www.kdnuggets.com/2022/04/best-data-science-career-tracks-2022.html

• Data Science Interview Guide – Part 2: Interview Resources

  Check out these resources to help you prepare for your data science Interview, or for those who are brushing up on their technical skills or who want to start learning data science.

  https://www.kdnuggets.com/2022/04/data-science-interview-guide-part-2-interview-resources.html

• How to Ace Data Science Assessment Test by Using Automatic EDA Tools

  By using a few lines of code, you can understand key aspects of a given dataset. These tools have helped me answer business-related questions during the data assessment test by Alooba.

  https://www.kdnuggets.com/2022/04/ace-data-science-assessment-test-automatic-eda-tools.html

• Top 5 Reasons Why You Should Avoid a Data Science Career

  The intent of this article is to give you a reality check of what are the personality traits of a typical data scientist before you dip your feet in the ocean of the big shiny world of data science.

  https://www.kdnuggets.com/2022/04/top-5-reasons-avoid-data-science-career.html

• Data Science Interview Guide – Part 1: The Structure

  According to one source, the types of questions that will generally be asked in data scientist interviews can be broken down into five categories. Let's take a closer look.

  https://www.kdnuggets.com/2022/04/data-science-interview-guide-part-1-structure.html

• Uncertainty Quantification in Artificial Intelligence-based Systems

  The article summarizes the plethora of UQ methods using Bayesian techniques, shows issues and gaps in the literature, suggests further directions, and epitomizes AI-based systems within the Financial Crime domain.

  https://www.kdnuggets.com/2022/04/uncertainty-quantification-artificial-intelligencebased-systems.html

• 8 Free MIT Courses to Learn Data Science Online

  Create a data science learning path with courses from the world’s most prestigious university.

  https://www.kdnuggets.com/2022/03/8-free-mit-courses-learn-data-science-online.html

• Top 13 Skills That Every Data Scientist Should Have

  Let me walk you through the top 13 data science skills that you should have to become a successful data scientist. Following this outline, you’ll have a great path of digestible steps to educate yourself and be prepared to apply for data scientist positions.

  https://www.kdnuggets.com/2022/03/top-13-skills-every-data-scientist.html

• A Guide On How To Become A Data Scientist (Step By Step Approach)

  Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to become a data scientist.

  https://www.kdnuggets.com/2021/05/guide-become-data-scientist.html

• Top 5 Free Machine Learning Courses

  Give a boost to your career and learn job-ready machine learning skills by taking the best free online courses.

  https://www.kdnuggets.com/2022/02/top-5-free-machine-learning-courses.html

• How to Learn Math for Machine Learning

  So how much math do you need to know in order to work in the data science industry? The answer: Not as much as you think.

  https://www.kdnuggets.com/2022/02/learn-math-machine-learning.html

• The Complete Collection of Data Science Cheat Sheets – Part 1

  A collection of cheat sheets that will help you prepare for a technical interview, assessment tests, class presentation, and help you revise core data science concepts.

  https://www.kdnuggets.com/2022/02/complete-collection-data-science-cheat-sheets-part-1.html

• 8 Best Data Science Courses to Enroll in 2022 For Steep Career Advancement

  Here is the list of the top 8 data science courses and programs that you can consider for upskilling yourself and get the best data scientist job in 2022.

  https://www.kdnuggets.com/2022/02/scaler-8-best-data-science-courses-enroll-2022-steep-career-advancement.html

• Demystifying Bad Science

  Rigorous science is challenging and any study can be questioned. Deception is part of human nature and scientists are human, as are journalists and policymakers. We are too and must be careful not to trust a study just because we find it exciting, or because it comforts us or conforms to our beliefs.

  https://www.kdnuggets.com/2022/01/demystifying-bad-science.html

• How to Get Into Data Analytics If You Don’t Have the Right Degree

  So, is a career in data analytics a good fit for you?

  https://www.kdnuggets.com/2021/12/how-to-get-into-data-analytics.html

• 5 Key Skills Needed To Become a Great Data Scientist">5 Key Skills Needed To Become a Great Data Scientist

  Based on 10 years of my experience (learn to build those skills).

  https://www.kdnuggets.com/2021/12/5-key-skills-needed-become-great-data-scientist.html

• What Does a Data Scientist Do?

  This guide provides you with the best possible, most direct, and clear answers to "What is data science?" and "What does a data scientist do?".

  https://www.kdnuggets.com/2021/12/what-does-a-data-scientist-do.html

• Visual Scoring Techniques for Classification Models

  Read this article assessing a model performance in a broader context.

  https://www.kdnuggets.com/2021/11/visual-scoring-techniques-classification-models.html

• Neural Networks from a Bayesian Perspective

  This article looks at neural networks from a Bayesian perspective.

  https://www.kdnuggets.com/2021/11/neural-networks-bayesian-perspective.html

• How to calculate confidence intervals for performance metrics in Machine Learning using an automatic bootstrap method

  Are your model performance measurements very precise due to a “large” test set, or very uncertain due to a “small” or imbalanced test set?

  https://www.kdnuggets.com/2021/10/calculate-confidence-intervals-performance-metrics-machine-learning.html

• 38 Free Courses on Coursera for Data Science">38 Free Courses on Coursera for Data Science

  There are so many online resources for learning data science, and a great deal of it can be used at no cost. This collection of free courses hosted by Coursera will help you enhance your data science and machine learning skills, no matter your current level of expertise.

  https://www.kdnuggets.com/2021/10/38-free-courses-coursera-datascience.html

• Advanced Statistical Concepts in Data Science

  The article contains some of the most commonly used advanced statistical concepts along with their Python implementation.

  https://www.kdnuggets.com/2021/09/advanced-statistical-concepts-data-science.html

• Path to Full Stack Data Science">Path to Full Stack Data Science

  Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.

  https://www.kdnuggets.com/2021/09/path-full-stack-data-science.html

• How to be a Data Scientist without a STEM degree">How to be a Data Scientist without a STEM degree

  Breaking into data science as a professional does require technical skills, a well-honed knack for problem-solving, and a willingness to swim in oceans of data. Maybe you are coming in as a career change or ready to take a new learning path in life--without having previously earned an advanced degree in a STEM field. Follow these tips to find your way into this high-demand and interesting field.

  https://www.kdnuggets.com/2021/09/data-scientist-without-stem-degree.html

• What 2 years of self-teaching data science taught me

  Many of us self-learn data science from the very beginning. While continuing to self-learn on demand is crucial, especially after you become a professional, there can be many pitfalls early on for learning the wrong way or missing out on key ideas that are important for the real-world application of data science.

  https://www.kdnuggets.com/2021/09/2-years-self-teaching-data-science.html

• ebook: Learn Data Science with R – free download

  Check out this new book for data science beginners with many practical examples that covers statistics, R, graphing, and machine learning. As a source to learn the full breadth of data science foundations, "Learn Data Science with R" starts at the beginner level and gradually progresses into expert content.

  https://www.kdnuggets.com/2021/09/ebook-learn-data-science-r.html

• Learning Data Science and Machine Learning: First Steps After The Roadmap">Learning Data Science and Machine Learning: First Steps After The Roadmap

  Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.

  https://www.kdnuggets.com/2021/08/learn-data-science-machine-learning.html

• Most Common Data Science Interview Questions and Answers">Most Common Data Science Interview Questions and Answers

  After analyzing 900+ data science interview questions from companies over the past few years, the most common data science interview question categories are reviewed in this guide, each explained with an example.

  https://www.kdnuggets.com/2021/08/common-data-science-interview-questions-answers.html

• Mastering Clustering with a Segmentation Problem

  The one stop shop for implementing the most widely used models in Python for unsupervised clustering.

  https://www.kdnuggets.com/2021/08/mastering-clustering-segmentation-problem.html

• Machine Learning Skills – Update Yours This Summer

  The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.

  https://www.kdnuggets.com/2021/07/update-your-machine-learning-skills.html

• Advice for Learning Data Science from Google’s Director of Research">Advice for Learning Data Science from Google’s Director of Research

  Surfing the professional career wave in data science is a hot prospect for many looking to get their start in the world. The digital revolution continues to create many exciting new opportunities. But, jumping in too fast without fully establishing your foundational skills can be detrimental to your success, as is suggested by this advice for data science newbies from Peter Norvig, the Director of Research at Google.

  https://www.kdnuggets.com/2021/07/google-advice-learning-data-science.html

• How to Create Unbiased Machine Learning Models

  In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.

  https://www.kdnuggets.com/2021/07/create-unbiased-machine-learning-models.html

• ROC Curve Explained

  Learn to visualise a ROC curve in Python.

  https://www.kdnuggets.com/2021/07/roc-curve-explained.html

• A Learning Path To Becoming a Data Scientist">A Learning Path To Becoming a Data Scientist

  Becoming a professional data scientist may not be as easy as "1... 2... 3...", but these 10 steps can be your self-learning roadmap to kickstarting your future in the exciting and ever-expanding field of data science.

  https://www.kdnuggets.com/2021/07/learning-path-data-scientist.html

• Data Scientists and ML Engineers Are Luxury Employees">Data Scientists and ML Engineers Are Luxury Employees

  Maybe it seems that everyone wants to become a data scientist and every organization wants to hire one as quickly as possible. However, a mismatch often exists between what companies tend to need and what ML practitioners want to do. So, it's time for the field to take another step toward maturity through an enhanced appreciation of the broad range of technical foundations for an organization to become data-driven.

  https://www.kdnuggets.com/2021/07/data-scientists-machine-learning-engineers-luxury-employees.html

• Prepare Behavioral Questions for Data Science Interviews

  This is part 5 of a series by the author which helps readers nail the data science interviews with confidence.

  https://www.kdnuggets.com/2021/07/prepare-behavioral-questions-data-science-interviews.html

• Learning Data Science Through Social Media

  Want your social media algorithms to show you actual algorithms? Spare a moment during your social media scrolling to learn a bit of data science. Here are suggestions for at-a-glance access to good ideas and tips on your favorite platforms.

  https://www.kdnuggets.com/2021/07/learning-data-science-through-social-media.html

• From Scratch: Permutation Feature Importance for ML Interpretability

  Use permutation feature importance to discover which features in your dataset are useful for prediction — implemented from scratch in Python.

  https://www.kdnuggets.com/2021/06/from-scratch-permutation-feature-importance-ml-interpretability.html

• 10 Mistakes You Should Avoid as a Data Science Beginner

  Read this article on how to gain a competitive advantage in the data science job market.

  https://www.kdnuggets.com/2021/06/10-mistakes-avoid-data-science-beginner.html

• 10 Must-Know Statistical Concepts for Data Scientists

  Statistics is a building block of data science. If you are working or plan to work in this field, then you will encounter the fundamental concepts reviewed for you here. Certainly, there is much more to learn in statistics, but once you understand these basics, then you can steadily build your way up to advanced topics.

  https://www.kdnuggets.com/2021/04/10-statistical-concepts-data-scientists.html

• Data Science 101: Normalization, Standardization, and Regularization

  Normalization, standardization, and regularization all sound similar. However, each plays a unique role in your data preparation and model building process, so you must know when and how to use these important procedures.

  https://www.kdnuggets.com/2021/04/data-science-101-normalization-standardization-regularization.html

• Must Know for Data Scientists and Data Analysts: Causal Design Patterns">Must Know for Data Scientists and Data Analysts: Causal Design Patterns

  Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for measuring causal effects from observational data.

  https://www.kdnuggets.com/2021/03/causal-design-patterns.html

• 3 Mathematical Laws Data Scientists Need To Know">3 Mathematical Laws Data Scientists Need To Know

  Machine learning and data science are founded on important mathematics in statistics and probability. A few interesting mathematical laws you should understand will especially help you perform better as a Data Scientist, including Benford's Law, the Law of Large Numbers, and Zipf's Law.

  https://www.kdnuggets.com/2021/03/3-mathematical-laws.html

• 10 Statistical Concepts You Should Know For Data Science Interviews

  Data Science is founded on time-honored concepts from statistics and probability theory. Having a strong understanding of the ten ideas and techniques highlighted here is key to your career in the field, and also a favorite topic for concept checks during interviews.

  https://www.kdnuggets.com/2021/02/10-statistical-concepts-data-science-interviews.html

• 10 resources for data science self-study

  Many resources exist for the self-study of data science. In our modern age of information technology, an enormous amount of free learning resources are available to anyone, and with effort and dedication, you can master the fundamentals of data science.

  https://www.kdnuggets.com/2021/02/10-resources-data-science-self-study.html

• Want to Be a Data Scientist? Don’t Start With Machine Learning">Want to Be a Data Scientist? Don’t Start With Machine Learning

  Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data.

  https://www.kdnuggets.com/2021/01/data-scientist-dont-start-machine-learning.html

• Null Hypothesis Significance Testing is Still Useful

  Even in the aftermath of the replication crisis, statistical significance lingers as an important concept for Data Scientists to understand.

  https://www.kdnuggets.com/2021/01/null-hypothesis-significance-testing-useful.html

• Comprehensive Guide to the Normal Distribution

  Drop in for some tips on how this fundamental statistics concept can improve your data science.

  https://www.kdnuggets.com/2021/01/comprehensive-guide-normal-distribution.html

• The Four Jobs of the Data Scientist">The Four Jobs of the Data Scientist

  So, what do you do for a living? Sometimes, the answer to that question can feel like, "everything!" Well, for the Data Scientist, an extreme sense of being a "jack of all trades" is common. In fact, four such trades can be defined that a top-quality Data Scientist will iterate through during any one project.

  https://www.kdnuggets.com/2021/01/four-jobs-data-scientist.html

• 10 Underappreciated Python Packages for Machine Learning Practitioners">10 Underappreciated Python Packages for Machine Learning Practitioners

  Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.

  https://www.kdnuggets.com/2021/01/10-underappreciated-python-packages-machine-learning-practitioners.html

• Learn Data Science for free in 2021">Learn Data Science for free in 2021

  If you are considering starting a career path in machine learning and data science, then there is a great deal to learn theoretically, along with gaining practical skills in applying a broad range of techniques. This comprehensive learning plan will guide you to start on this path, and it is all available for free.

  https://www.kdnuggets.com/2021/01/learn-data-science-free-2021.html

• MLOps: Model Monitoring 101

  Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.

  https://www.kdnuggets.com/2021/01/mlops-model-monitoring-101.html

• Monte Carlo integration in Python">Monte Carlo integration in Python

  A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python?

  https://www.kdnuggets.com/2020/12/monte-carlo-integration-python.html

• XGBoost: What it is, and when to use it

  XGBoost is a tree based ensemble machine learning algorithm which is a scalable machine learning system for tree boosting. Read more for an overview of the parameters that make it work, and when you would use the algorithm.

  https://www.kdnuggets.com/2020/12/xgboost-what-when.html

• Data Science and Machine Learning: The Free eBook

  Check out the newest addition to our free eBook collection, Data Science and Machine Learning: Mathematical and Statistical Methods, and start building your statistical learning foundation today.

  https://www.kdnuggets.com/2020/12/data-science-machine-learning-free-ebook.html

• 20 Core Data Science Concepts for Beginners">20 Core Data Science Concepts for Beginners

  With so much to learn and so many advancements to follow in the field of data science, there are a core set of foundational concepts that remain essential. Twenty of these ideas are highlighted here that are key to review when preparing for a job interview or just to refresh your appreciation of the basics.

  https://www.kdnuggets.com/2020/12/20-core-data-science-concepts-beginners.html

• 15 Exciting AI Project Ideas for Beginners">15 Exciting AI Project Ideas for Beginners

  There are many branches to AI to learn, but a project-based approach can keep things interesting. Here is a list of 15 such projects you can get started on implementing today.

  https://www.kdnuggets.com/2020/11/greatlearning-ai-project-ideas-beginners.html

• Top 6 Data Science Programs for Beginners

  Udacity has the best industry-leading programs in data science. Here are the top six data science courses for beginners to help you get started.

  https://www.kdnuggets.com/2020/11/udacity-data-science-programs-beginners.html

• Algorithms for Advanced Hyper-Parameter Optimization/Tuning

  In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.

  https://www.kdnuggets.com/2020/11/algorithms-for-advanced-hyper-parameter-optimization-tuning.html

• How to Get Into Data Science Without a Degree">How to Get Into Data Science Without a Degree

  Breaking into any new field or slogging through a career change is always a challenge, and requires focus and even a little grit. While transitioning to becoming a Data Scientist is no different, aspiring to this role is possible, even without a formal post-secondary degree, largely due to the vast amount of quality learning resources available today.

  https://www.kdnuggets.com/2020/11/data-science-without-degree.html

• Free From MIT: Intro to Computational Thinking with Julia

  Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic is another freely-available offering from MIT's Open Courseware.

  https://www.kdnuggets.com/2020/11/free-mit-intro-computational-thinking-julia.html

• My Data Science Online Learning Journey on Coursera

  Check out the author's informative list of courses and specializations on Coursera taken to get started on their data science and machine learning journey.

  https://www.kdnuggets.com/2020/11/data-science-online-learning-journey-coursera.html

• Doing the impossible? Machine learning with less than one example

  Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.

  https://www.kdnuggets.com/2020/11/machine-learning-less-than-one-example.html

• Data scientist or machine learning engineer? Which is a better career option?

  In order to build automated data processing systems, we require professionals like Machine Learning Engineers and Data Scientists. But which of these is a better career option right now? Read on to find out.

  https://www.kdnuggets.com/2020/11/greatlearning-data-scientist-machine-learning-engineer.html

• How to Make Sense of the Reinforcement Learning Agents?

  In this blog post, you’ll learn what to keep track of to inspect/debug your agent learning trajectory. I’ll assume you are already familiar with the Reinforcement Learning (RL) agent-environment setting and you’ve heard about at least some of the most common RL algorithms and environments.

  https://www.kdnuggets.com/2020/10/make-sense-reinforcement-learning-agents.html

• Advice for Aspiring Data Scientists

  Are you a student of some type asking how to get into Data Science? You've come to the right place. Read on for both common and less basic advice on entering the field and excelling in the profession.

  https://www.kdnuggets.com/2020/10/advice-aspiring-data-scientists.html

• How to become a Data Scientist: a step-by-step guide">How to become a Data Scientist: a step-by-step guide

  Data science is everywhere. But what are the best ways to learn the field well enough to enter the profession? Read on for some tips and steps on doing so, and some great courses to help you get there.

  https://www.kdnuggets.com/2020/10/greatlearning-become-data-scientist-guide.html

• Your Guide to Linear Regression Models

  This article explains linear regression and how to program linear regression models in Python.

  https://www.kdnuggets.com/2020/10/guide-linear-regression-models.html

• The Best Free Data Science eBooks: 2020 Update">The Best Free Data Science eBooks: 2020 Update

  The author has updated their list of best free data science books for 2020. Read on to see what books you should grab.

  https://www.kdnuggets.com/2020/09/best-free-data-science-ebooks-2020-update.html

• Missing Value Imputation – A Review

  Detecting and handling missing values in the correct way is important, as they can impact the results of the analysis, and there are algorithms that can’t handle them. So what is the correct way?

  https://www.kdnuggets.com/2020/09/missing-value-imputation-review.html

• The Insiders’ Guide to Generative and Discriminative Machine Learning Models

  In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

  https://www.kdnuggets.com/2020/09/insiders-guide-generative-discriminative-machine-learning-models.html

• Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities">Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities

  We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.

  https://www.kdnuggets.com/2020/09/online-certificates-ai-data-science-machine-learning-top.html

• Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills">Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills

  We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.

  https://www.kdnuggets.com/2020/09/modern-data-science-skills.html

• 4 ways to improve your TensorFlow model – key regularization techniques you need to know">4 ways to improve your TensorFlow model – key regularization techniques you need to know

  Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow.

  https://www.kdnuggets.com/2020/08/tensorflow-model-regularization-techniques.html

• The List of Top 10 Lists in Data Science">The List of Top 10 Lists in Data Science

  The list of Top 10 lists that Data Scientists -- from enthusiasts to those who want to jump start a career -- must know to smoothly navigate a path through this field.

  https://www.kdnuggets.com/2020/08/top-10-lists-data-science.html

• Hypothesis Test for Real Problems

  Hypothesis tests are significant for evaluating answers to questions concerning samples of data.

  https://www.kdnuggets.com/2020/08/hypothesis-test-real-problems.html

• Going Beyond Superficial: Data Science MOOCs with Substance">Going Beyond Superficial: Data Science MOOCs with Substance

  Data science MOOCs are superficial. At least, a lot of them are. What are your options when looking for something more substantive?

  https://www.kdnuggets.com/2020/08/beyond-superficial-data-science-moocs-substance.html

• Data Science Internship Interview Questions

  Data science is an attractive field because not only is it lucrative, but you can have opportunities to work on interesting projects, and you’re always learning new things. If you're trying to get started from the ground up, then review this guide to prepare for the interview essentials.

  https://www.kdnuggets.com/2020/08/data-science-internship-interview-questions.html

• Know What Employers are Expecting for a Data Scientist Role in 2020">Know What Employers are Expecting for a Data Scientist Role in 2020

  The analysis is done from 1000+ recent Data scientist jobs, extracted from job portals using web scraping.

  https://www.kdnuggets.com/2020/08/employers-expecting-data-scientist-role-2020.html

• R squared Does Not Measure Predictive Capacity or Statistical Adequacy

  The fact that R-squared shouldn't be used for deciding if you have an adequate model is counter-intuitive and is rarely explained clearly. This demonstration overviews how R-squared goodness-of-fit works in regression analysis and correlations, while showing why it is not a measure of statistical adequacy, so should not suggest anything about future predictive performance.

  https://www.kdnuggets.com/2020/07/r-squared-predictive-capacity-statistical-adequacy.html

• Deep Learning for Signal Processing: What You Need to Know

  Signal Processing is a branch of electrical engineering that models and analyzes data representations of physical events. It is at the core of the digital world. And now, signal processing is starting to make some waves in deep learning.

  https://www.kdnuggets.com/2020/07/deep-learning-signal-processing.html

• Demystifying Statistical Significance

  With more professionals from a wide range of less technical fields diving into statistical analysis and data modeling, these experimental techniques can seem daunting. To help with these hurdles, this article clarifies some misconceptions around p-values, hypothesis testing, and statistical significance.

  https://www.kdnuggets.com/2020/07/demystifying-statistical-significance.html

• Foundations of Data Science: The Free eBook

  As has become tradition on KDnuggets, let's start a new week with a new eBook. This time we check out a survey style text with a variety of topics, Foundations of Data Science.

  https://www.kdnuggets.com/2020/07/foundations-data-science-free-ebook.html

• A Complete Guide To Survival Analysis In Python, part 1">A Complete Guide To Survival Analysis In Python, part 1

  This three-part series covers a review with step-by-step explanations and code for how to perform statistical survival analysis used to investigate the time some event takes to occur, such as patient survival during the COVID-19 pandemic, the time to failure of engineering products, or even the time to closing a sale after an initial customer contact.

  https://www.kdnuggets.com/2020/07/complete-guide-survival-analysis-python-part1.html

• Software engineering fundamentals for Data Scientists

  As a data scientist writing code for your models, it's quite possible that your work will make its way into a production environment to be used by the masses. But, writing code that is deployed as software is much different than writing code for exploratory data analysis. Learn about the key approaches for making your code production-ready that will save you time and future headaches.

  https://www.kdnuggets.com/2020/06/software-engineering-fundamentals-data-scientists.html

• How to Prepare Your Data

  This is an overview of structuring, cleaning, and enriching raw data.

  https://www.kdnuggets.com/2020/06/how-prepare-your-data.html

• Practical Markov Chain Monte Carlo

  This is a slightly more intricate example of MCMC, compared to many with a fairly simple model, a single predictor (maybe two), and not much else, which highlights a couple of issues and tricks worth noting for a handwritten implementation.

  https://www.kdnuggets.com/2020/06/practical-markov-chain-monte-carlo.html

• 4 Free Math Courses to do and Level up your Data Science Skills">4 Free Math Courses to do and Level up your Data Science Skills

  Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.

  https://www.kdnuggets.com/2020/06/4-free-maths-courses.html

• Overview of data distributions">Overview of data distributions

  With so many types of data distributions to consider in data science, how do you choose the right one to model your data? This guide will overview the most important distributions you should be familiar with in your work.

  https://www.kdnuggets.com/2020/06/overview-data-distributions.html

• Model Evaluation Metrics in Machine Learning">Model Evaluation Metrics in Machine Learning

  A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.

  https://www.kdnuggets.com/2020/05/model-evaluation-metrics-machine-learning.html

• The Best NLP with Deep Learning Course is Free">The Best NLP with Deep Learning Course is Free

  Stanford's Natural Language Processing with Deep Learning is one of the most respected courses on the topic that you will find anywhere, and the course materials are freely available online.

  https://www.kdnuggets.com/2020/05/best-nlp-deep-learning-course-free.html

• Appropriately Handling Missing Values for Statistical Modelling and Prediction

  Many statisticians in industry agree that blindly imputing the missing values in your dataset is a dangerous move and should be avoided without first understanding why the data is missing in the first place.

  https://www.kdnuggets.com/2020/05/handnling-missing-values-statistical-modelling-prediction.html

• Looking Normal(ly Distributed)

  This article investigates when some probability distributions look normal "enough" for a statistical test.

  https://www.kdnuggets.com/2020/05/looking-normally-distributed.html

• I Designed My Own Machine Learning and AI Degree

  With so many pioneering online resources for open education, check out this organized collection of courses you can follow to become a well-rounded machine learning and AI engineer.

  https://www.kdnuggets.com/2020/05/designed-machine-learning-ai-degree.html

• Start Your Machine Learning Career in Quarantine">Start Your Machine Learning Career in Quarantine

  While this quarantine can last two months, make the most of it by starting your career in Machine Learning with this 60-day learning plan.

  https://www.kdnuggets.com/2020/05/machine-learning-career-quarantine.html

• Beginners Learning Path for Machine Learning">Beginners Learning Path for Machine Learning

  So, you are interested in machine learning? Here is your complete learning path to start your career in the field.

  https://www.kdnuggets.com/2020/05/beginners-learning-path-machine-learning.html

• Deep Learning: The Free eBook">Deep Learning: The Free eBook

  "Deep Learning" is the quintessential book for understanding deep learning theory, and you can still read it freely online.

  https://www.kdnuggets.com/2020/05/deep-learning-free-ebook.html

• Outbreak Analytics: Data Science Strategies for a Novel Problem

  You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?

  https://www.kdnuggets.com/2020/04/outbreak-analytics-data-science-novel-problem.html

• A Concise Course in Statistical Inference: The Free eBook">A Concise Course in Statistical Inference: The Free eBook

  Check out this freely available book, All of Statistics: A Concise Course in Statistical Inference, and learn the probability and statistics needed for success in data science.

  https://www.kdnuggets.com/2020/04/statistics-concise-course-statistical-inference-free-ebook.html

• Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition">Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition

  If you find yourself quarantined and looking for free learning materials in the way of books and courses to sharpen your data science and machine learning skills, this collection of articles I have previously written curating such things is for you.

  https://www.kdnuggets.com/2020/04/machine-learning-data-science-books-courses-quarantine.html

• Better notebooks through CI: automatically testing documentation for graph machine learning

  In this article, we’ll walk through the detailed and helpful continuous integration (CI) that supports us in keeping StellarGraph’s demos current and informative.

  https://www.kdnuggets.com/2020/04/better-notebooks-through-ci-automatically-testing-documentation-graph-machine-learning.html

• Time Series Classification Synthetic vs Real Financial Time Series">Time Series Classification Synthetic vs Real Financial Time Series

  This article discusses distinguishing between real financial time series and synthetic time series using XGBoost.

  https://www.kdnuggets.com/2020/03/time-series-classification-synthetic-real-financial-time-series.html

• 50 Must-Read Free Books For Every Data Scientist in 2020">50 Must-Read Free Books For Every Data Scientist in 2020

  In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science.

  https://www.kdnuggets.com/2020/03/50-must-read-free-books-every-data-scientist-2020.html

• Data Science Curriculum for self-study

  Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.

  https://www.kdnuggets.com/2020/02/data-science-curriculum-self-study.html

• Free Mathematics Courses for Data Science & Machine Learning">Free Mathematics Courses for Data Science & Machine Learning

  It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond.

  https://www.kdnuggets.com/2020/02/free-mathematics-courses-data-science-machine-learning.html

• Getting Started with R Programming

  An end to end Data Analysis using R, the second most requested programming language in Data Science.

  https://www.kdnuggets.com/2020/02/getting-started-r-programming.html

• How to land a Data Scientist job at your dream company">How to land a Data Scientist job at your dream company

  Job hunting for anyone just starting out as a data scientist can require grit, passion, and perseverance before finding the best opportunity. Follow this career search journey to learn what it took -- and the learning resources used -- to land the dream job.

  https://www.kdnuggets.com/2020/01/data-scientist-job-dream-company.html

• Exoplanet Hunting Using Machine Learning

  Search for exoplanets — those planets beyond our own solar system — using machine learning, and implement these searches in Python.

  https://www.kdnuggets.com/2020/01/exoplanet-hunting-machine-learning.html

• I wanna be a data scientist, but… how?">I wanna be a data scientist, but… how?

  It’s easy to say "I wanna be a data scientist," but... where do you start? How much time is needed to be desired by companies? Do you need a Master’s degree? Do you need to know every mathematical concept ever derived? The journey might be long, but follow this plan to help you keep moving forward toward your career goal.

  https://www.kdnuggets.com/2020/01/wanna-be-data-scientist.html

• Top 9 Mobile Apps for Learning and Practicing Data Science">Top 9 Mobile Apps for Learning and Practicing Data Science

  This article will tell you about the top 9 mobile apps that help the user in learning and practicing data science and hence is improving their productivity.

  https://www.kdnuggets.com/2020/01/top-9-mobile-apps-learning-practicing-data-science.html

• Classify A Rare Event Using 5 Machine Learning Algorithms

  Which algorithm works best for unbalanced data? Are there any tradeoffs?

  https://www.kdnuggets.com/2020/01/classify-rare-event-machine-learning-algorithms.html

• An Introductory Guide to NLP for Data Scientists with 7 Common Techniques">An Introductory Guide to NLP for Data Scientists with 7 Common Techniques

  Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.

  https://www.kdnuggets.com/2020/01/intro-guide-nlp-data-scientists.html

• Stock Market Forecasting Using Time Series Analysis

  Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.

  https://www.kdnuggets.com/2020/01/stock-market-forecasting-time-series-analysis.html

• 10 Best and Free Machine Learning Courses, Online

  Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.

  https://www.kdnuggets.com/2019/12/best-free-machine-learning-courses-online.html

• How To “Ultralearn” Data Science: removing distractions and finding focus, Part 2

  This second part in a series about how to "ultralearn" data science will guide you through several techniques to remove those distractions -- because your focus needs more focus.

  https://www.kdnuggets.com/2019/12/ultralearn-data-science-distractions-focus-part2.html

• How To “Ultralearn” Data Science, Part 1

  What is "ultralearning" and how can you follow the strategy to become an expert of data science? Start with this first part in a series that will guide you through this self-motivated methodology to help you efficiently master difficult skills.

  https://www.kdnuggets.com/2019/12/ultralearn-data-science-part1.html

• NeurIPS 2019 Outstanding Paper Awards

  NeurIPS 2019 is underway in Vancouver, and the committee has just recently announced this year's Outstanding Paper Awards. Find out what the selections were, along with some additional info on NeurIPS papers, here.

  https://www.kdnuggets.com/2019/12/neurips-2019-paper-awards.html

• 10 Free Top Notch Machine Learning Courses">10 Free Top Notch Machine Learning Courses

  Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.

  https://www.kdnuggets.com/2019/12/10-free-top-notch-courses-machine-learning.html

• Data Science Curriculum Roadmap">Data Science Curriculum Roadmap

  What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.

  https://www.kdnuggets.com/2019/12/data-science-curriculum-roadmap.html

• A Non-Technical Reading List for Data Science">A Non-Technical Reading List for Data Science

  The world still cannot be reduced to numbers on a page because human beings are still the ones making all the decisions. So, the best data scientists understand the numbers and the people. Check out these great data science books that will make you a better data scientist without delving into the technical details.

  https://www.kdnuggets.com/2019/12/non-technical-reading-list-data-science.html

• The Future of Careers in Data Science & Analysis">The Future of Careers in Data Science & Analysis

  As the fields of data science and analysis continue to expand, the next crop of bright minds is always needed. Learn more about the nuances of these jobs and find where you can fit in for a rewarding and interesting career.

  https://www.kdnuggets.com/2019/11/future-careers-data-science-analysis.html

• Pro Tips: How to deal with Class Imbalance and Missing Labels

  Your spectacularly-performing machine learning model could be subject to the common culprits of class imbalance and missing labels. Learn how to handle these challenges with techniques that remain open areas of new research for addressing real-world machine learning problems.

  https://www.kdnuggets.com/2019/11/tips-class-imbalance-missing-labels.html

• Understanding Boxplots">Understanding Boxplots

  A boxplot. It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.

  https://www.kdnuggets.com/2019/11/understanding-boxplots.html

• How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow

  In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.

  https://www.kdnuggets.com/2019/10/extend-scikit-learn-bring-sanity-machine-learning-workflow.html

• How Bayes’ Theorem is Applied in Machine Learning

  Learn how Bayes Theorem is in Machine Learning for classification and regression!

  https://www.kdnuggets.com/2019/10/bayes-theorem-applied-machine-learning.html