Search results for Probability Statistics
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How to Become a (Good) Data Scientist – Beginner Guide">How to Become a (Good) Data Scientist – Beginner Guide
A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.https://www.kdnuggets.com/2019/10/good-data-scientist-beginner-guide.html
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An Overview of Density Estimation
Density estimation is estimating the probability density function of the population from the sample. This post examines and compares a number of approaches to density estimation.https://www.kdnuggets.com/2019/10/overview-density-estimation.html
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How AI will transform healthcare (and can it fix the US healthcare system?)">How AI will transform healthcare (and can it fix the US healthcare system?)
This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.https://www.kdnuggets.com/2019/09/ai-transform-healthcare.html
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6 bits of advice for Data Scientists">6 bits of advice for Data Scientists
As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.https://www.kdnuggets.com/2019/09/advice-data-scientists.html
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Beta Distribution: What, When & How
This article covers the beta distribution, and explains it using baseball batting averages.https://www.kdnuggets.com/2019/09/beta-distribution-what-when-how.html
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5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python
“I want to learn machine learning and artificial intelligence, where do I start?” Here.https://www.kdnuggets.com/2019/09/5-beginner-friendly-steps-learn-machine-learning-data-science-python.html
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My journey path from a Software Engineer to BI Specialist to a Data Scientist">My journey path from a Software Engineer to BI Specialist to a Data Scientist
The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science.https://www.kdnuggets.com/2019/09/journey-software-engineer-bi-data-scientist.html
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Many Heads Are Better Than One: The Case For Ensemble Learning
While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.https://www.kdnuggets.com/2019/09/ensemble-learning.html
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There is No Free Lunch in Data Science">There is No Free Lunch in Data Science
There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.https://www.kdnuggets.com/2019/09/no-free-lunch-data-science.html
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Ensemble Methods for Machine Learning: AdaBoost
It turned out that, if we ask the weak algorithm to create a whole bunch of classifiers (all weak for definition), and then combine them all, what may figure out is a stronger classifier.https://www.kdnuggets.com/2019/09/ensemble-methods-machine-learning-adaboost.html
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Scikit-Learn vs mlr for Machine Learning
How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.https://www.kdnuggets.com/2019/09/scikit-learn-mlr-machine-learning.html
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I wasn’t getting hired as a Data Scientist. So I sought data on who is.">I wasn’t getting hired as a Data Scientist. So I sought data on who is.
Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before.https://www.kdnuggets.com/2019/09/getting-hired-data-scientist-sought-data.html
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How to count Big Data: Probabilistic data structures and algorithms
Learn how probabilistic data structures and algorithms can be used for cardinality estimation in Big Data streams.https://www.kdnuggets.com/2019/08/count-big-data-probabilistic-data-structures-algorithms.html
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Artificial Intelligence vs. Machine Learning vs. Deep Learning: What is the Difference?
Over the past few years, artificial intelligence continues to be one of the hottest topics. And in order to work effectively with it, you need to understand its constituent parts.https://www.kdnuggets.com/2019/08/artificial-intelligence-vs-machine-learning-vs-deep-learning-difference.html
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Order Matters: Alibaba’s Transformer-based Recommender System
Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems.https://www.kdnuggets.com/2019/08/order-matters-alibabas-transformer-based-recommender-system.html
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Detecting stationarity in time series data
Explore how to determine if your time series data is generated by a stationary process and how to handle the necessary assumptions and potential interpretations of your result.https://www.kdnuggets.com/2019/08/stationarity-time-series-data.html
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Statistical Modelling vs Machine Learning">Statistical Modelling vs Machine Learning
At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.https://www.kdnuggets.com/2019/08/statistical-modelling-vs-machine-learning.html
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Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree
This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon.https://www.kdnuggets.com/2019/08/pytorch-cheat-sheet-beginners.html
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P-values Explained By Data Scientist
This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.https://www.kdnuggets.com/2019/07/p-values-explained-data-scientist.html
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A Gentle Introduction to Noise Contrastive Estimation
Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.https://www.kdnuggets.com/2019/07/introduction-noise-contrastive-estimation.html
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12 Things I Learned During My First Year as a Machine Learning Engineer
Learn about the day-in-the-life of one machine learning engineer and the important lessons learned for being successful in that role.https://www.kdnuggets.com/2019/07/12-things-learned-machine-learning-engineer.html
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Secrets to a Successful Data Science Interview
Are you puzzled as to what to prepare for data science interviews? That you are reading this document is a reflection of your seriousness in being a successful data scientist.https://www.kdnuggets.com/2019/07/secrets-data-science-interview.html
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Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference">Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference
Researchers from MIT recently unveiled a new probabilistic programming language named Gen, a language which allow researchers to write models and algorithms from multiple fields where AI techniques are applied without having to deal with equations or manually write high-performance code.https://www.kdnuggets.com/2019/07/introducing-gen-language-progammable-inference.html
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What’s wrong with the approach to Data Science?">What’s wrong with the approach to Data Science?
The job ‘Data Scientist’ has been around for decades, it was just not called “Data Scientist”. Statisticians have used their knowledge and skills using machine learning techniques such as Logistic Regression and Random Forest for prediction and insights for longer than people actually realize.https://www.kdnuggets.com/2019/07/whats-wrong-with-data-science.html
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Why you’re not a job-ready data scientist (yet)">Why you’re not a job-ready data scientist (yet)
Trying to snag a dream Data Science job, but can't seem to land one? Check out these four skills that companies really want and be prepared for your next interview.https://www.kdnuggets.com/2019/07/not-job-ready-data-scientist-yet.html
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Optimization with Python: How to make the most amount of money with the least amount of risk?
Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobel-prize winning economic theory for maximizing investment profits while minimizing risk.https://www.kdnuggets.com/2019/06/optimization-python-money-risk.html
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How to Learn Python for Data Science the Right Way">How to Learn Python for Data Science the Right Way
The biggest mistake you can make while learning Python for data science is to learn Python programming from courses meant for programmers. Avoid this mistake, and learn Python the right way by following this approach.https://www.kdnuggets.com/2019/06/python-data-science-right-way.html
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All Models Are Wrong – What Does It Mean?
During your adventures in data science, you may have heard “all models are wrong.” Let’s unpack this famous quote to understand how we can still make models that are useful.https://www.kdnuggets.com/2019/06/all-models-are-wrong.html
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Overview of Different Approaches to Deploying Machine Learning Models in Production
Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.https://www.kdnuggets.com/2019/06/approaches-deploying-machine-learning-production.html
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If you’re a developer transitioning into data science, here are your best resources"> If you’re a developer transitioning into data science, here are your best resources
This article will provide a background on the data scientist role and why your background might be a good fit for data science, plus tangible stepwise actions that you, as a developer, can take to ramp up on data science.https://www.kdnuggets.com/2019/06/developer-transitioning-data-science-best-resources.html
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What Does a Lady Tasting Tea Have to Do with Science?
Design of Experiments (DOE) is a statistical concept used to find the cause-and-effect relationships. Surprisingly, an experiment arising from a casual conversation about tea-drinking is one of the first examples of an experiment designed using statistical ideas.https://www.kdnuggets.com/2019/05/lady-tasting-tea-science.html
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6 Industries Warming up to Predictive Analytics and Forecasting
Here are six sectors that are realizing how beneficial predictive analytics could be, embracing the possibilities of valuable insights extracted from such technology.https://www.kdnuggets.com/2019/05/6-industries-warming-up-predictive-analytics-forecasting.html
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Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own
Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.https://www.kdnuggets.com/2019/04/graduating-gans-understanding-generative-adversarial-networks.html
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Best Data Visualization Techniques for small and large data">Best Data Visualization Techniques for small and large data
Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques.https://www.kdnuggets.com/2019/04/best-data-visualization-techniques.html
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2019 Best Masters in Data Science and Analytics – Europe Edition">2019 Best Masters in Data Science and Analytics – Europe Edition
We provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across Europe.https://www.kdnuggets.com/2019/04/best-masters-data-science-analytics-europe.html
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Checklist for Debugging Neural Networks
Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.https://www.kdnuggets.com/2019/03/checklist-debugging-neural-networks.html
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Beating the Bookies with Machine Learning
We investigate how to use a custom loss function to identify fair odds, including a detailed example using machine learning to bet on the results of a darts match and how this can assist you in beating the bookmaker.https://www.kdnuggets.com/2019/03/beating-bookies-machine-learning.html
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Logistic Regression: A Concise Technical Overview
Logistic Regression is a Regression technique that is used when we have a categorical outcome (2 or more categories). Logistic Regression is one of the most easily interpretable classification techniques in a Data Scientist’s portfolio.https://www.kdnuggets.com/2019/01/logistic-regression-concise-technical-overview.html
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2018’s Top 7 R Packages for Data Science and AI
This is a list of the best packages that changed our lives this year, compiled from my weekly digests.https://www.kdnuggets.com/2019/01/vazquez-2018-top-7-r-packages.html
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Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.https://www.kdnuggets.com/2019/01/active-blogs-ai-analytics-data-science.html
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10 More Must-See Free Courses for Machine Learning and Data Science">10 More Must-See Free Courses for Machine Learning and Data Science
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.https://www.kdnuggets.com/2018/12/10-more-free-must-see-courses-machine-learning-data-science.html
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Should you become a data scientist?">Should you become a data scientist?
An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.https://www.kdnuggets.com/2018/12/should-i-become-a-data-scientist.html
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How Different are Conventional Programming and Machine Learning?
When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.https://www.kdnuggets.com/2018/12/different-conventional-programming-machine-learning.html
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A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.https://www.kdnuggets.com/2018/12/finlayson-machine-learning-resources.html
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Common mistakes when carrying out machine learning and data science">Common mistakes when carrying out machine learning and data science
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!https://www.kdnuggets.com/2018/12/common-mistakes-data-science.html
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Kick Start Your Data Career! Tips From the Frontline
I am going to provide very interesting and useful tips through this blog series which will help students to kick start their career in Data.https://www.kdnuggets.com/2018/12/kick-start-your-data-career.html
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Intro to Data Science for Managers">Intro to Data Science for Managers
This mindmap contains a condensed introduction to the key data science concepts and techniques that have revolutionized the business landscape and became essential for making beneficial data-driven decisionshttps://www.kdnuggets.com/2018/11/intro-data-science-managers.html
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Evaluating the Business Value of Predictive Models in Python and R
In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.https://www.kdnuggets.com/2018/10/evaluating-business-value-predictive-models-modelplotpy.html
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10 Best Mobile Apps for Data Scientist / Data Analysts">10 Best Mobile Apps for Data Scientist / Data Analysts
A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more.https://www.kdnuggets.com/2018/10/10-best-mobile-apps-data-scientist.html
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When Bayes, Ockham, and Shannon come together to define machine learning
A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.https://www.kdnuggets.com/2018/09/when-bayes-ockham-shannon-come-together-define-machine-learning.html
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Machine Learning Cheat Sheets">Machine Learning Cheat Sheets
Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.https://www.kdnuggets.com/2018/09/machine-learning-cheat-sheets.html
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Object Detection and Image Classification with YOLO
We explain object detection, how YOLO algorithm can help with image classification, and introduce the open source neural network framework Darknet.https://www.kdnuggets.com/2018/09/object-detection-image-classification-yolo.html
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Word Vectors in Natural Language Processing: Global Vectors (GloVe)
A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.https://www.kdnuggets.com/2018/08/word-vectors-nlp-glove.html
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Data Scientist Interviews Demystified">Data Scientist Interviews Demystified
We look at typical questions in a data science interview, examine the rationale for such questions, and hope to demystify the interview process for recent graduates and aspiring data scientists.https://www.kdnuggets.com/2018/08/data-scientist-interviews-demystified.html
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The Industries That Can Benefit Most From Predictive Analytics
Predictive analytics are useful for doing all those things and more, and could increase the overall competitiveness of individual companies or entire sectors.https://www.kdnuggets.com/2018/07/industries-benefit-most-predictive-analytics.html
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Explaining the 68-95-99.7 rule for a Normal Distribution">Explaining the 68-95-99.7 rule for a Normal Distribution
This post explains how those numbers were derived in the hope that they can be more interpretable for your future endeavors.https://www.kdnuggets.com/2018/07/explaining-68-95-99-7-rule-normal-distribution.html
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Deep Quantile Regression
Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. This article will purely focus on inferring quantiles.https://www.kdnuggets.com/2018/07/deep-quantile-regression.html
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Top 20 Python Libraries for Data Science in 2018">Top 20 Python Libraries for Data Science in 2018
Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.https://www.kdnuggets.com/2018/06/top-20-python-libraries-data-science-2018.html
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7 Simple Data Visualizations You Should Know in R">7 Simple Data Visualizations You Should Know in R
This post presents a selection of 7 essential data visualizations, and how to recreate them using a mix of base R functions and a few common packages.https://www.kdnuggets.com/2018/06/7-simple-data-visualizations-should-know-r.html
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How (dis)similar are my train and test data?
This articles examines a scenario where your machine learning model can fail.https://www.kdnuggets.com/2018/06/how-dissimilar-train-test-data.html
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The Book of Why
Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.https://www.kdnuggets.com/2018/06/gray-pearl-book-of-why.html
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Using Linear Regression for Predictive Modeling in R
In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.https://www.kdnuggets.com/2018/06/linear-regression-predictive-modeling-r.html
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10 More Free Must-Read Books for Machine Learning and Data Science">10 More Free Must-Read Books for Machine Learning and Data Science
Summer, summer, summertime. Time to sit back and unwind. Or get your hands on some free machine learning and data science books and get your learn on. Check out this selection to get you started.https://www.kdnuggets.com/2018/05/10-more-free-must-read-books-for-machine-learning-and-data-science.html
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Neural Network based Startup Name Generator
How to build a recurrent neural network to generate suggestions for your new company’s name.https://www.kdnuggets.com/2018/04/neural-network-startup-name-generator.html
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7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning">7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.https://www.kdnuggets.com/2018/04/7-books-mathematical-foundations-data-science.html
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Scalable Select of Random Rows in SQL
Performance boosts are achieved by selecting random rows or the sampling technique. Let’s learn how to select random rows in SQL.https://www.kdnuggets.com/2018/04/scalable-select-random-rows-sql.html
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Introduction to Markov Chains">Introduction to Markov Chains
What are Markov chains, when to use them, and how they workhttps://www.kdnuggets.com/2018/03/introduction-markov-chains.html
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How to Survive Your Data Science Interview
There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every companyhttps://www.kdnuggets.com/2018/03/survive-data-science-interview.html
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A Guide to Hiring Data Scientists
This article provides a short overview of emerging data scientist types and their unique skillsets, as well as a guide for HR professionals and analytics managers who are looking to hire their first data scientists or build a data science team. Included are an overview of skills for each type and specific questions that can be asked to assess candidates.https://www.kdnuggets.com/2018/02/guide-hiring-data-scientists.html
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Top 15 Scala Libraries for Data Science in 2018
For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.https://www.kdnuggets.com/2018/02/top-15-scala-libraries-data-science-2018.html
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Want to Become a Data Scientist? Try Feynman Technique">Want to Become a Data Scientist? Try Feynman Technique
Get over the impostor syndrome by developing a strong understanding about the various Data Science topics using the Feynman Techniquehttps://www.kdnuggets.com/2018/01/data-scientist-feynman-technique.html
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Propensity Score Matching in R
Propensity scores are an alternative method to estimate the effect of receiving treatment when random assignment of treatments to subjects is not feasible.https://www.kdnuggets.com/2018/01/propensity-score-matching-r.html
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The Art of Learning Data Science">The Art of Learning Data Science
A beginner’s account of getting into comfort zone of learning Data Science.https://www.kdnuggets.com/2018/01/art-learning-data-science.html
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How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?">How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?
When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.https://www.kdnuggets.com/2017/12/mathematics-needed-learn-data-science-machine-learning.html
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Best Masters in Data Science and Analytics – Europe Edition
The third part of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics, examining the programs from Europe.https://www.kdnuggets.com/2017/12/best-masters-data-science-analytics-europe.html
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You have created your first Linear Regression Model. Have you validated the assumptions?
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.https://www.kdnuggets.com/2017/11/first-linear-regression-assumptions.html
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The 10 Statistical Techniques Data Scientists Need to Master">The 10 Statistical Techniques Data Scientists Need to Master
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.https://www.kdnuggets.com/2017/11/10-statistical-techniques-data-scientists-need-master.html
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How Bayesian Networks Are Superior in Understanding Effects of Variables
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.https://www.kdnuggets.com/2017/11/bayesian-networks-understanding-effects-variables.html
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Process Mining with R: Introduction
In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.https://www.kdnuggets.com/2017/11/process-mining-r-introduction.html
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Getting Started with Machine Learning in One Hour!
Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.https://www.kdnuggets.com/2017/11/getting-started-machine-learning-one-hour.html
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Statistical Mistakes Even Scientists Make
Scientists are all experts in statistics, right? Wrong.https://www.kdnuggets.com/2017/10/statistical-mistakes-even-scientists-make.html
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30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets">30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets
This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.https://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html
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Big Data Architecture: A Complete and Detailed Overview
Data scientists may not be as educated or experienced in computer science, programming concepts, devops, site reliability engineering, non-functional requirements, software solution infrastructure, or general software architecture as compared to well-trained or experienced software architects and engineers.https://www.kdnuggets.com/2017/09/big-data-architecture-overview.html
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Evaluating Data Science Projects: A Case Study Critique
It’s not necessary to understand the inner workings of a machine learning project, but you should understand whether the right things have been measured and whether the results are suited to the business problem. You need to know whether to believe what data scientists are telling you.https://www.kdnuggets.com/2017/09/evaluating-data-science-projects-case-study-critique.html
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37 Reasons why your Neural Network is not working">37 Reasons why your Neural Network is not working
Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.https://www.kdnuggets.com/2017/08/37-reasons-neural-network-not-working.html
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Sampling: A Primer
Though it doesn’t get a lot of buzz, sampling is fundamental to any field of science. Marketing scientist Kevin Gray asks Dr. Stas Kolenikov, Senior Scientist at Abt Associates, what marketing researchers and data scientists most need to know about it.https://www.kdnuggets.com/2017/08/sampling-primer.html
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AI and Deep Learning, Explained Simply">AI and Deep Learning, Explained Simply
AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
https://www.kdnuggets.com/2017/07/ai-deep-learning-explained-simply.html
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What Makes a Good Analyst?
Without doubt, critical thinking is necessary in order to be a good analyst but particular skills and experience are also required. What are some of these skills?https://www.kdnuggets.com/2017/04/gray-makes-good-analyst.html
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Must-Know: How to evaluate a binary classifier
Binary classification is a basic concept which involves classifying the data into two groups. Read on for some additional insight and approaches.https://www.kdnuggets.com/2017/04/must-know-evaluate-binary-classifier.html
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10 Free Must-Read Books for Machine Learning and Data Science">10 Free Must-Read Books for Machine Learning and Data Science
Spring. Rejuvenation. Rebirth. Everything’s blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html
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Stuff Happens: A Statistical Guide to the “Impossible”
Why are some people struck by lightning multiple times or, more encouragingly, how could anyone possibly win the lottery more than once? The odds against these sorts of things are enormous.https://www.kdnuggets.com/2017/04/stuff-happens-statistical-guide-impossible.html
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How to think like a data scientist to become one
The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.https://www.kdnuggets.com/2017/03/think-like-data-scientist-become-one.html
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What Is Data Science, and What Does a Data Scientist Do?">What Is Data Science, and What Does a Data Scientist Do?
This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.https://www.kdnuggets.com/2017/03/data-science-data-scientist-do.html
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What Top Firms Ask: 100+ Data Science Interview Questions
Check this out: A topic wise collection of 100+ data science interview questions from top companies.https://www.kdnuggets.com/2017/03/top-firms-100-data-science-interview-questions.html
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Applying Machine Learning To March Madness
March Madness is upon us. But before you get your brackets set, check out this overview of using machine learning to do the heavy lifting for you. A great discussion, and a timely topic.https://www.kdnuggets.com/2017/03/machine-learning-march-madness.html
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Every Intro to Data Science Course on the Internet, Ranked">Every Intro to Data Science Course on the Internet, Ranked
For this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings.https://www.kdnuggets.com/2017/03/every-intro-data-science-course-ranked.html
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17 More Must-Know Data Science Interview Questions and Answers">17 More Must-Know Data Science Interview Questions and Answers
17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers.
https://www.kdnuggets.com/2017/02/17-data-science-interview-questions-answers.html
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Bad Data + Good Models = Bad Results
No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data.https://www.kdnuggets.com/2017/01/bad-data-good-models-bad-results.html
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Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall
Data science and predictive analytics can provide huge value, but they can mislead and backfire if not used with fail-safe measures. The author gives examples of such problems and provides guidelines to avoid them.https://www.kdnuggets.com/2017/01/siegel-data-science-avoiding-prediction-pitfall.html
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Introduction to Bayesian Inference
Bayesian inference is a powerful toolbox for modeling uncertainty, combining researcher understanding of a problem with data, and providing a quantitative measure of how plausible various facts are. This overview from Datascience.com introduces Bayesian probability and inference in an intuitive way, and provides examples in Python to help get you started.https://www.kdnuggets.com/2016/12/datascience-introduction-bayesian-inference.html
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The Best Metric to Measure Accuracy of Classification Models
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.https://www.kdnuggets.com/2016/12/best-metric-measure-accuracy-classification-models.html
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Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.https://www.kdnuggets.com/2016/11/top-10-amazon-books-ai-machine-learning.html
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10 Tips to Improve your Data Science Interview
Interviewing is a skill. Here are 10 tips and resources to improve your Data Science interviews.https://www.kdnuggets.com/2016/11/tips-improve-your-data-science-interview.html
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How Bayesian Inference Works">How Bayesian Inference Works
Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.https://www.kdnuggets.com/2016/11/how-bayesian-inference-works.html
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Top 10 Amazon Books in Data Mining, 2016 Edition">Top 10 Amazon Books in Data Mining, 2016 Edition
Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.https://www.kdnuggets.com/2016/11/top-10-amazon-books-data-mining.html
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Data Science Basics: An Introduction to Ensemble Learners
New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.https://www.kdnuggets.com/2016/11/data-science-basics-intro-ensemble-learners.html
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Artificial Intelligence, Deep Learning, and Neural Networks, Explained">Artificial Intelligence, Deep Learning, and Neural Networks, Explained
This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html
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Random Forest®: A Criminal Tutorial
Get an overview of Random Forest here, one of the most used algorithms by KDnuggets readers according to a recent poll.https://www.kdnuggets.com/2016/09/reandom-forest-criminal-tutorial.html
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The 10 Algorithms Machine Learning Engineers Need to Know">The 10 Algorithms Machine Learning Engineers Need to Know
Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand.https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html
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Central Limit Theorem for Data Science – Part 2
This post continues an explanation of Central Limit Theorem started in a previous post, with additional details... and beer.https://www.kdnuggets.com/2016/08/central-limit-theorem-data-science-part-2.html
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Understanding the Empirical Law of Large Numbers and the Gambler’s Fallacy
Law of large numbers is a important concept for practising data scientists. In this post, The empirical law of large numbers is demonstrated via simple simulation approach using the Bernoulli process.https://www.kdnuggets.com/2016/08/understanding-empirical-law-large-numbers.html
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7 Steps to Understanding Computer Vision
A starting point for Computer Vision and how to get going deeper. Dive into this post for some overview of the right resources and a little bit of advice.https://www.kdnuggets.com/2016/08/seven-steps-understanding-computer-vision.html
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The Core of Data Science
This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.https://www.kdnuggets.com/2016/08/core-data-science.html
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Statistical Data Analysis in Python
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.https://www.kdnuggets.com/2016/07/statistical-data-analysis-python.html
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Big Data, Bible Codes, and Bonferroni
This discussion will focus on 2 particular statistical issues to be on the look out for in your own work and in the work of others mining and learning from Big Data, with real world examples emphasizing the importance of statistical processes in practice.https://www.kdnuggets.com/2016/07/big-data-bible-codes-bonferroni.html
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History of Data Mining
Data mining is a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning. Here are the major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data.https://www.kdnuggets.com/2016/06/rayli-history-data-mining.html
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New Deep Learning Book Finished, Finalized Online Version Available
What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.https://www.kdnuggets.com/2016/04/deep-learning-book-finished.html
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100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning
Stay on top of your data science skills game! Here’s a list of about 100 most active and interesting blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.https://www.kdnuggets.com/2016/03/100-active-blogs-analytics-big-data-science-machine-learning.html
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Lift Analysis – A Data Scientist’s Secret Weapon
Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.https://www.kdnuggets.com/2016/03/lift-analysis-data-scientist-secret-weapon.html
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What is the influence of Big Data in Medicine?
The 360-degree customer view is the idea, that companies can get a complete view of customers by aggregating data from the various touch points that a user. And, big data is helping to materialize this idea, which will revolutionize the healthcare.https://www.kdnuggets.com/2016/03/influence-big-data-medicine.html
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21 Must-Know Data Science Interview Questions and Answers">21 Must-Know Data Science Interview Questions and Answers
KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.https://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html
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How to Tackle a Lottery with Mathematics
With mathematical rigor and narrative flair, Adam Kucharski reveals the tangled history of betting and science. The house can seem unbeatable. In this book, Kucharski shows us just why it isn't. Even better, he shows us how the search for the perfect bet has been crucial for the scientific pursuit of a better world.https://www.kdnuggets.com/2016/01/how-tackle-lottery-mathematics.html
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Understanding Rare Events and Anomalies: Why streaks patterns change
We often look back at the past year and an overall history of rare events, and try to then extrapolate future odds of the same rare event, based on that. We illustrate here, that rare past events have no usefulness in understanding the rarity of the same events in the future!https://www.kdnuggets.com/2016/01/understanding-rare-events-anomalies-patterns-change.html
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Software development skills for data scientists
Data science is not only about building the models and sharing insights, many times they have to collaborate in deploying models and sharing them with software developers, learn which things to keep in mind while doing so.https://www.kdnuggets.com/2015/12/software-development-skills-data-scientists.html
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We need a statistically rigorous and scientifically meaningful definition of replication
Replication and confirmation are indispensable concepts that help define scientific facts. It seems that before continuing the debate over replication, we need a statistically meaningful definition of replication.https://www.kdnuggets.com/2015/10/statistically-rigorous-scientifically-meaningful-definition-replication.html
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Data Science of IoT: Sensor fusion and Kalman filters, Part 1
The Kalman filter has numerous applications, including IoT and Sensor fusion, which helps to determine the State of an IoT based computing system based on sensor input.https://www.kdnuggets.com/2015/10/data-science-iot-sensor-fusion-kalman-filters-part1.html
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The Best Advice From Quora on ‘How to Learn Machine Learning’
Top machine learning writers on Quora give their advice on learning machine learning, including specific resources, quotes, and personal insights, along with some extra nuggets of information.https://www.kdnuggets.com/2015/10/learning-machine-learning-quora.html
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Crushed it! Landing a data science job
Data scientist interviews depend on the company and the team, it might look like a software developer’s interview, or statistician’s interview. Here we collected some hot tips to pass along if you’re thinking about a move soon.https://www.kdnuggets.com/2015/10/erin-shellman-landing-data-science-job.html
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15 Mathematics MOOCs for Data Science
The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.https://www.kdnuggets.com/2015/09/15-math-mooc-data-science.html
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Top 10 Quora Machine Learning Writers and Their Best Advice
Top Quora machine learning writers give their advice on pursuing a career in the field, academic research, and selecting and using appropriate technologies.https://www.kdnuggets.com/2015/09/top-machine-learning-writers-quora.html