2019 Apr
All (109) | Courses, Education (11) | Meetings (15) | News (13) | Opinions (30) | Top Stories, Tweets (10) | Tutorials, Overviews (27) | Webcasts & Webinars (3)
- Naive Bayes: A Baseline Model for Machine Learning Classification Performance - May 7, 2019.
We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.
- Powerful like your local notebook. Sharable like a Google Doc. - Apr 30, 2019.
Mode is the only analytics platform with native Python and R Notebooks. Get everyone up and running in minutes by delivering Notebook-powered results right in your browser. Now anyone on your team can re-run R- and Python-powered reports themselves—without ever touching code.
- Top Stories, Apr 22-28: The most desired skill in data science; How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists - Apr 30, 2019.
Artificial Intelligence 101 Cheatsheet; AI Supporting The Earth; Data Visualization in Python: Matplotlib vs Seaborn; 2019 Best Masters in Data Science and Analytics Online; Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own
- Learn About Data Science & the Future of Investing from Hedge Fund Leaders at Rev 2 - Apr 30, 2019.
Rev 2 features interactive sessions, Q&A with industry luminaries, poster sessions for interesting modeling techniques and accomplishments, and stimulating conversations about how to make data science an enterprise-grade capability.
- Interview Questions for Data Science – Three Case Interview Examples - Apr 30, 2019.
Part two in this series of useful posts for aspiring data scientists focuses on case interviews and how you can best go about answering them.
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Normalization vs Standardization — Quantitative analysis - Apr 30, 2019.
Stop using StandardScaler from Sklearn as a default feature scaling method can get you a boost of 7% in accuracy, even when you hyperparameters are tuned! - MS in Health Analytics Online - Apr 29, 2019.
Students in the Northwestern master's program in Health Analytics build in-depth data science expertise specifically for healthcare that can provide solutions and improve patient outcomes. Drive impactful healthcare insights from data!
- On Stage at PAW Industry 4.0: Bayer, Continental, HP, Vodafone & Many More - Apr 29, 2019.
Only a few weeks left until you have the opportunity to listen and learn from top class industry experts from all over the world at PAW Industry 4.0 in Munich on 6-7 May. Use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.
- Strata SF day 1 Highlights: from Edge to AI, scoring AI projects, cyberconflict, cryptography - Apr 29, 2019.
Journey from “Edge to AI”, scoring your AI projects, cyberconflict, role of cryptography in AI and more insights from a leading conference.
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Top Data Science and Machine Learning Methods Used in 2018, 2019 - Apr 29, 2019.
Once again, the most used methods are Regression, Clustering, Visualization, Decision Trees/Rules, and Random Forests. The greatest relative increases this year are overwhelmingly Deep Learning techniques, while SVD, SVMs and Association Rules show the greatest decline. - Pandas DataFrame Indexing - Apr 29, 2019.
The goal of this post is identify a single strategy for pulling data from a DataFrame using the Pandas Python library that is straightforward to interpret and produces reliable results.
- Delivering Trusted AI with DataRobot and Microsoft - Apr 26, 2019.
In this webinar, Apr 30 @ 1 PM ET, attendees will learn more about how their organizations can add AI to BI, making more predictive decisions along the way.
- AI and the data production landscape - Apr 26, 2019.
Data Science Salon NY returns to Viacom HQ in Times Square on June 13. Here are insights from DSS NY top speakers on the future of AI in the media production landscape.
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The most desired skill in data science - Apr 26, 2019.
What is the biggest skill gap in data science according to hiring managers looking for hire recent graduates? Hint: it’s not coding. - Projects to Include in a Data Science Portfolio - Apr 26, 2019.
“Don’t pick just random projects to work on and add it to your resume or portfolio. Solve a problem that relates to the companies that you’re interested in.”
- Meet the World’s Leading AI & Deep Learning Experts - Apr 25, 2019.
RE-WORK returns to San Francisco Jun 20-21 with the Deep Reinforcement Learning Summit, the Applied AI Summit and the AI for Good Summit. KDnuggets subscribers get 20% off Early Bird discounted passes when you register before May 3 with code KDNUGGETS.
- The problem with data science job postings - Apr 25, 2019.
We provide a useful set of rules you can follow to make sure you’re applying to the right roles and explain why confusing job descriptions with impossible requirements are the new normal.
- Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own - Apr 25, 2019.
Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.
- Join the new generation of AI technologists - Apr 24, 2019.
Galvanize the power of data science at the leading AI and data festival on the west coast, DATAx San Francisco this May 14-15. Use code 'KD200' before Friday, May 3.
- Top KDnuggets tweets, Apr 17–23: The History of Artificial #NeuralNetworks; Artificial Intelligence 101 Cheatsheet - Apr 24, 2019.
Also: 3 Big Problems with Big Data and How to Solve Them; Data Visualization in Python: Matplotlib vs Seaborn; The Deep Learning Toolset — An Overview; Another 10 Free Must-See Courses for Machine Learning and Data Science
- Top 10 Python Use Cases - Apr 24, 2019.
This paper covers 10 of the most common use cases by industry for Python that ActiveState has witnessed implemented by its customers.
- Generative Adversarial Networks – Key Milestones and State of the Art - Apr 24, 2019.
We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.
- Attention Craving RNNS: Building Up To Transformer Networks - Apr 24, 2019.
RNNs let us model sequences in neural networks. While there are other ways of modeling sequences, RNNs are particularly useful. RNNs come in two flavors, LSTMs (Hochreiter et al, 1997) and GRUs (Cho et al, 2014)
- Lower Rates End Friday for Mega-PAW Vegas – the Largest Predictive Analytics World to Date - Apr 23, 2019.
Five Predictive Analytics World Events in Las Vegas, Jun 16-20: Business, Financial, Healthcare, Industry 4.0, Deep Learning. Regular Pricing Ends This Friday. Register now!
- Top Stories, Apr 15-21: Data Visualization in Python: Matplotlib vs Seaborn; Data Science with Optimus Part 2: Setting your DataOps Environment - Apr 23, 2019.
Also: Best Data Visualization Techniques for small and large data; K-Means Clustering: Unsupervised Learning for Recommender Systems; An Introduction on Time Series Forecasting with Simple Neural Networks & LSTM; The Rise of Generative Adversarial Networks
- Wharton Customer Analytics Initiative Annual Conference in Philadelphia, May 15-16 – Register Now - Apr 23, 2019.
The Wharton Customer Analytics Initiative (WCAI) annual conference, “Successful Applications of Analytics: How Analytics Drives Disruption,” returns to Philadelphia May 15-16, and includes analytic professionals from a wide variety of industries for a day and a half of knowledge sharing and networking.
- Machine Learning and Deep Link Graph Analytics: A Powerful Combination - Apr 23, 2019.
We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.
- 2019 Best Masters in Data Science and Analytics – Online - Apr 23, 2019.
We provide an updated comprehensive and objective survey of online Masters in Analytics and Data Science, including rankings, tuition, and duration of the education program.
- Was it Worth Studying a Data Science Masters? - Apr 23, 2019.
As I started to apply for Data Science roles it quickly became apparent that I was lacking two key skills: applying Machine Learning and coding
- Approach pre-trained deep learning models with caution - Apr 23, 2019.
Pre-trained models are easy to use, but are you glossing over details that could impact your model performance?
- Earn a Deep Learning Certificate - Apr 22, 2019.
Now is your chance to break into AI, even if you don’t have a PhD. If you want a job in AI and Deep Learning, Andrew Ng’s Specialization will help you get there.
- Easy Way to Scrape Data from Website By Yourself - Apr 22, 2019.
Introducing Octoparse, a simple cloud-based website data scrapper that will let you extract any web data in real-time and coding is not needed.
- AI Supporting The Earth - Apr 22, 2019.
To celebrate Earth Day 2019, we explain how Intel is committed to advancing uses of AI that positively impact our world by providing social good organizations with technologies and expertise to accelerate their work.
- The Mueller Report Word Cloud: A brief tutorial in R - Apr 22, 2019.
Word clouds are simple visual summaries of the mostly frequently used words in a text, presenting essentially the same information as a histogram but are somewhat less precise and vastly more eye-catching. Get a quick sense of the themes in the recently released Mueller Report and its 448 pages of legal content.
- How To Go Into Data Science: Ultimate Q&A for Aspiring Data Scientists with Serious Guides - Apr 22, 2019.
To learn ALL the skills sets in data science is next to impossible as the scope is way too wide. There’ll always be some skills (technical/non-technical) that data scientists don’t know or haven’t learned as different businesses require different skill sets.
- Data Driven Government Workshops Announced! - Apr 19, 2019.
The workshops have been announced for Data Driven Government (formerly known as Predictive Analytics World for Government), Sep 25 in Washington, DC. Use the code KDNUGGETS for a 15% discount on your Deep Learning World ticket.
- The Rise of Generative Adversarial Networks - Apr 19, 2019.
A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.
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Data Visualization in Python: Matplotlib vs Seaborn - Apr 19, 2019.
Seaborn and Matplotlib are two of Python's most powerful visualization libraries. Seaborn uses fewer syntax and has stunning default themes and Matplotlib is more easily customizable through accessing the classes. - Unleash a faster Python on your data - Apr 18, 2019.
Intel’s optimized Python packages deliver quick repeatable results compared to standard Python packages. Intel offers optimized Scikit-learn, Numpy, and SciPy to help data scientists get rapid results on their Intel® hardware. Download now.
- Sisense BloX – Go Beyond Dashboards - Apr 18, 2019.
Introducing Sisense BloX, the tool that allows you to integrate your business platforms inside your dashboards using prebuilt templates. Users stay within the dashboard environment and go from understanding insights to taking action—in one click.
- 3 Big Problems with Big Data and How to Solve Them - Apr 18, 2019.
We discuss some of the negatives of using big data, including false equivalences and bias, vulnerability to security breaches, protecting against unauthorized access and the lack of international standards for data privacy regulations.
- Distributed Artificial Intelligence: A primer on Multi-Agent Systems, Agent-Based Modeling, and Swarm Intelligence - Apr 18, 2019.
Distributed Artificial Intelligence (DAI) is a class of technologies and methods that span from swarm intelligence to multi-agent technologies. It is one of the subsets of AI where simulation has greater importance that point-prediction.
- How Optimization Works - Apr 18, 2019.
Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time.
- Top KDnuggets tweets, Apr 10–16: Math for Programmers teaches you the #math you need to know; The Third Wave Data Scientist – what skills are required? - Apr 17, 2019.
Also: 4 Reasons Why Your Machine Learning Code is Probably Bad; Google launches an end-to-end #AI platform Also #AutoML Tables; How to Recognize a Good Data Scientist Job From a Bad One; Another 10 Free Must-Read Books for Machine Learning and Data Science
- DATAx San Francisco | 14-15 May | Over 500 Data Professionals - Apr 17, 2019.
Just a few weeks left until DATAx San Francisco, May 14 & 15. Find out about a number of our stand out sessions taking place at the biggest data festival on the West Coast. Secure your ticket to DATAx San Francisco on May 14 & 15 now!
- Best Data Visualization Techniques for small and large data - Apr 17, 2019.
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.
- Building a Flask API to Automatically Extract Named Entities Using SpaCy - Apr 17, 2019.
This article discusses how to use the Named Entity Recognition module in spaCy to identify people, organizations, or locations in text, then deploy a Python API with Flask.
- Northwestern’s MS in Data Science - Apr 16, 2019.
Advance your data-driven career with an online MS in Data Science at Northwestern. You’ll learn from an accomplished faculty of leading industry experts. You can choose from a wide range of specializations and electives to suit your goals.
- Penn State Online MS in Data Analytics. - Apr 16, 2019.
Teaches students to examine the entire life cycle of analytics problem solving and play a major role in key business decisions.
- How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms - Apr 16, 2019.
We outline three different clustering algorithms - k-means clustering, hierarchical clustering and Graph Community Detection - providing an explanation on when to use each, how they work and a worked example.
- 2019 Best Masters in Data Science and Analytics – Europe Edition - Apr 16, 2019.
We provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across Europe.
- Data Science with Optimus Part 2: Setting your DataOps Environment - Apr 16, 2019.
Breaking down data science with Python, Spark and Optimus. Today: Data Operations for Data Science. Here we’ll learn to set-up Git, Travis CI and DVC for our project.
- Top Stories, Apr 8-14: How to Recognize a Good Data Scientist Job From a Bad One; An Introduction on Time Series Forecasting with Simple Neural Networks & LSTM - Apr 16, 2019.
Also: Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application?; Why Data Scientists Need To Work In Groups; Advice for New Data Scientists
- Hot Deep Learning Applications at Deep Learning World – Las Vegas, June 16-20 - Apr 15, 2019.
Deep Learning World 2019, Jun 16-20 in Las Vegas, will cover a good portion of the wide range of deep learning application areas. Regular prices available until Apr 26. Register now!
- Top March Stories: Another 10 Free Must-Read Books for Machine Learning and Data Science - Apr 15, 2019.
Also: Artificial Neural Networks Optimization using Genetic Algorithm with Python; Who is a typical Data Scientist in 2019?
- An introduction to explainable AI, and why we need it - Apr 15, 2019.
We introduce explainable AI, why it is needed, and present the Reversed Time Attention Model, Local Interpretable Model-Agnostic Explanation and Layer-wise Relevance Propagation.
- Become the new generation of marketing technologists - Apr 15, 2019.
The MSc in Digital Marketing & Data Science at EM Lyon is a 18-month program designed to grow a new generation of leading marketing specialists – apply to current session by June 3, 2019.
- Data Science with Optimus Part 1: Intro - Apr 15, 2019.
With Optimus you can clean your data, prepare it, analyze it, create profilers and plots, and perform machine learning and deep learning, all in a distributed fashion, because on the back-end we have Spark, TensorFlow, Sparkling Water and Keras. It’s super easy to use.
- Custom Data & Analytics Workshops at Your Location - Apr 12, 2019.
TDWI Onsite Education allows you to train at your office so each member of your team learns the same best practices, methodologies, and strategies directly from industry experts. Each workshop includes customized activities that work with your people, projects, processes, and data.
- Avoiding Obvious Insights Using Analyze With Insight Miner - Apr 12, 2019.
Analyze with Insight Miner delivers value for every business user with machine learning. Learn how it was created from Sisense Data Scientist, Ayelet Arditi.
- How can quantum computing be useful for Machine Learning - Apr 12, 2019.
We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.
- Make Your Own Job in Data Science: A High-Risk, High-Reward Approach - Apr 12, 2019.
This article discusses an alternative approach to finding data science jobs that’s also worth considering, although it has some inherent risks: make your own.
- Why Data Scientists Need To Work In Groups - Apr 12, 2019.
If you read this article you will see that the job of data scientist is NOT listed. The rest of this article will explore why it is true that data scientists need to work in groups.
- Win KDnuggets Pass to Strata Data, 29 Apr – 2 May, London - Apr 12, 2019.
Your chance to win a Bronze Pass to Strata Data London 2019, which offers an unmatched breadth and depth of data knowledge, providing a clear view of the future of data.
- Because analysis is more than just dashboards - Apr 11, 2019.
Where traditional BI tools often make it easy to build dashboards, Mode makes it easy for you to answer any follow-up questions when you see changes in those dashboards. Choose the level of abstraction you want for a given dataset and quickly get to the story behind the change.
- Math for Programmers - Apr 11, 2019.
Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.
- How to build a technology narrative for early career data and analytics talent acquisition - Apr 11, 2019.
We provide advice for companies in industries still going through a digital transformation on how they can start to understand the problem that Data and Analytics professionals can help solve.
- AI For Ordinary Folks - Apr 11, 2019.
There are many excellent books, articles, YouTube lectures and blogs on AI and topics related to it aimed at data scientists and AI researchers. You may want to, instead, check out this list of AI resources crafted for ordinary folks.
- Top KDnuggets tweets, Apr 03–09: Top 8 Sources For #MachineLearning and Analytics Datasets - Apr 10, 2019.
Also: Preprocessing data for data science (Part 1); The Deep Learning Toolset — An Overview; How to Choose the Right Chart Type; Top KDnuggets tweets: Here is a great explanation of what is a scalar, vector, matrix, #tensor; Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019?
- [Upcoming Webinar] 5 Steps to Building Responsible AI Systems - Apr 10, 2019.
What does responsible AI mean? This webinar, Apr 18 @ 11 AM ET, will cover the essential steps to building AI systems that are responsible.
- Build Python for Data Science in Just a Few Clicks - Apr 10, 2019.
There is only one Python distro that lets you add new versions of packages, remove unused packages, and rebuild in minutes. Yes, for free. Download ActiveState Python 3.6 build now.
- Beyond Siri, Google Assistant, and Alexa – what you need to know about AI Conversational Applications - Apr 10, 2019.
We discuss industry trends in Artificial Intelligence with Vijay Ramakrishnan, a machine learning engineer and expert in conversational applications.
- Compilation of Advice for New and Aspiring Data Scientists - Apr 10, 2019.
Check out this compilation of advice for the new and upcoming data scientist, condensing 30+ pieces of advice into 6 minutes.
- Find Your Algorithm for Success with Drexel’s Online MS in Data Science - Apr 9, 2019.
Success is waiting with Drexel’s new online MS in Data Science. Graduate workplace-ready by having experience with some of the industry’s leading technology. Learn more today.
- S2DS, a 5-week data science bootcamp helping analytical PhDs transition from academia to industry - Apr 9, 2019.
Introducing Europe’s largest data science training programme. Five weeks of intensive, project-based training turning exceptional analytical PhDs and MScs into Data Scientists.
- All you need to know about text preprocessing for NLP and Machine Learning - Apr 9, 2019.
We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them.
- Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? - Apr 9, 2019.
Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? Take part in the latest KDnuggets survey and have your say.
- How to Recognize a Good Data Scientist Job From a Bad One - Apr 9, 2019.
Here are six characteristics which set good data scientist jobs apart form the bad ones.
- Advance Your Data and Analytics Skills, Your Way - Apr 8, 2019.
Find the topics and learning style that resonate with you and your team! Join us for essential training in analytics, data management, business intelligence, machine learning, and more. Save 20% on TDWI seminars with code KD20.
- Top Stories, Apr 1-7: Top 10 Coding Mistakes Made by Data Scientists; Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 8, 2019.
Also: 7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them; Getting started with NLP using the PyTorch framework; Predict Age and Gender Using Convolutional Neural Network and OpenCV; Which Face is Real?
- Register for the Wharton Customer Analytics Initiative Annual Conference in Philadelphia: May 15-16 - Apr 8, 2019.
The Wharton Customer Analytics Initiative (WCAI) annual conference, “Successful Applications of Analytics: How Analytics Drives Disruption,” returns to Philadelphia May 15-16, and includes analytic professionals from a wide variety of industries for a day and a half of knowledge sharing and networking.
- Advice for New Data Scientists - Apr 8, 2019.
We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.
- Advanced Keras — Constructing Complex Custom Losses and Metrics - Apr 8, 2019.
In this tutorial I cover a simple trick that will allow you to construct custom loss functions in Keras which can receive arguments other than
y_trueandy_pred. - Statistical Thinking for Industrial Problem Solving (STIPS) – a free online course - Apr 5, 2019.
This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better.
- From Business Intelligence to Machine Intelligence - Apr 5, 2019.
This webinar, Apr 18 @ 1 PM ET, will help listeners understand both the opportunities and limits of AI for decision making. It will underscore the importance of applying appropriate governance and controls to analytic models and use cases.
- What is missing when AI makes a decision? - Apr 5, 2019.
We explain the need for caution when it comes to using AI in real-life situations and outline the importance of asking the right question to deliver the right impact.
- Spatio-Temporal Statistics: A Primer - Apr 5, 2019.
Marketing scientist Kevin Gray asks University of Missouri Professor Chris Wikle about Spatio-Temporal Statistics and how it can be used in science and business.
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Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas. - Download your DATAx guide to AI in Marketing - Apr 4, 2019.
Find out how marketers can utilise AI, data segmentation, digital natives, influencers, apps and the internet to help build better, more personalized customer experience: download our ebook 'DATAx: Guide to AI in Marketing'.
- KDnuggets Offer: Save 20% on Strata in London - Apr 4, 2019.
Strata Data Conference is coming to London Apr 29-May 2. Discover what's coming in data and AI. Save 20% on Gold, Silver, and Bronze passes with code KDNU (up to £231 on a Gold pass).
- Training a Champion: Building Deep Neural Nets for Big Data Analytics - Apr 4, 2019.
Introducing Sisense Hunch, the new way of handling Big Data sets that uses AQP technology to construct Deep Neural Networks (DNNs) which are trained to learn the relationships between queries and their results in these huge datasets.
- Building a Recommender System - Apr 4, 2019.
A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch.
- Predict Age and Gender Using Convolutional Neural Network and OpenCV - Apr 4, 2019.
Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.
- Top KDnuggets tweets, Mar 27 – Apr 02: Here is a great explanation of what is a scalar, vector, matrix, tensor - Apr 3, 2019.
Here is a great explanation of what is a scalar, vector, matrix, tensor; Machine Learning and Data Science Cheat Sheets; Papers with Code: A Fantastic GitHub Resource for Machine Learning.
- ODSC East is selling out; ODSC India announced - Apr 3, 2019.
ODSC East is in Boston Apr 30-May 3, and it's selling out fast! A limited amount of tickets still remain, and 20% off ends Friday! ODSC India 2019 will take place in Bengaluru, Aug 7-10. Tickets are on sale now!
- Grow your data career at DataScienceGO, San Diego, Sep 27-29 - Apr 3, 2019.
DataScienceGO is the only conference dedicated to career advancement for data science managers, practitioners and beginners. Early Bird tickets are on sale until June 27 - get them now.
- Getting started with NLP using the PyTorch framework - Apr 3, 2019.
We discuss the classes that PyTorch provides for helping with Natural Language Processing (NLP) and how they can be used for related tasks using recurrent layers.
- How to DIY Your Data Science Education - Apr 3, 2019.
Some people find the path of formal education works well for them, but this may not work for everyone, in every situation. Here are eight ways that you can take a DIY approach to your data science education.
- Top 8 Data Science Use Cases in Gaming - Apr 3, 2019.
The understanding of the data value for optimization and improvement of gaming makes specialists search for new ways to apply data science and its benefits in the gaming business. Therefore, various specific data science use cases appear. Here is our list of the most efficient and widely applied data science use cases in gaming.
- Make better data-driven business decisions - Apr 2, 2019.
Prepare yourself, wherever you are, with a Master of Professional Studies in Data Analytics – Business Analytics option, offered online through Penn State World Campus. You still have time to apply for fall 2019. Our next application deadline for the master's program is Monday, July 15.
- Top Stories, Mar 25-31: R vs Python for Data Visualization; The Deep Learning Toolset — An Overview - Apr 2, 2019.
Also: Pedestrian Detection in Aerial Images Using RetinaNet; How to Choose the Right Chart Type; Explaining Random Forest (with Python Implementation); A Beginner's Guide to Linear Regression in Python with Scikit-Learn; The Four Levels of Analytics Maturity
- Two Predictive Analytics World Events in Europe This Fall - Apr 2, 2019.
Two Predictive Analytics World events are coming to Europe this fall. Join PAW in London, Oct 16-17, and Berlin, Nov 18-19. Use the code KDNUGGETS for a 15% discount.
- 7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them - Apr 2, 2019.
Without the right visualization tools, raw data is of little use. Data visualization helps present the data in an interactive visual format. Here are the qualities to look for in a data visualization tool.
- Which Face is Real? - Apr 2, 2019.
Which Face Is Real? was developed based on Generative Adversarial Networks as a web application in which users can select which image they believe is a true person and which was synthetically generated. The person in the synthetically generated photo does not exist.
- Top 10 Coding Mistakes Made by Data Scientists - Apr 2, 2019.
Here is a list of 10 common mistakes that a senior data scientist — who is ranked in the top 1% on Stackoverflow for python coding and who works with a lot of (junior) data scientists — frequently sees.
- Uber’s Case Study at PAW Industry 4.0: Machine Learning to Enforce Mobile Performance - Apr 1, 2019.
Data scientists, industrial planners, and other machine learning experts will meet at PAW in Las Vegas on June 16-20, 2019 to explore the latest trends and technologies in machine & deep learning for the IoT era.
- XAI – A Data Scientist’s Mouthpiece - Apr 1, 2019.
We outline the usefulness of Explainable AI, which allows you to explain the results of a multidimensional model - including having a multimodal decision boundary - to a business user.
- What Does GPT-2 Think About the AI Arms Race? - Apr 1, 2019.
It may be April first, but that doesn't mean you will necessarily be fooled by GPT-2's views on the AI arms race. Why not have a read for fun and to see what the language generation model is capable of.
- Calling All Data Geeks to Come Home and Ignite Their Powers - Apr 1, 2019.
Introducing GOJEK, the superapp that is looking for people to help with its mission to make an impact through data.
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