Search results for s3

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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

• Big Data: Main Developments in 2017 and Key Trends in 2018">Big Data: Main Developments in 2017 and Key Trends in 2018

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.

  https://www.kdnuggets.com/2017/12/big-data-main-developments-2017-key-trends-2018.html

• Graph Analytics Using Big Data

  An overview and a small tutorial showing how to analyze a dataset using Apache Spark, graphframes, and Java.

  https://www.kdnuggets.com/2017/12/graph-analytics-using-big-data.html

• How (and Why) to Create a Good Validation Set

  The definitions of training, validation, and test sets can be fairly nuanced, and the terms are sometimes inconsistently used. In the deep learning community, “test-time inference” is often used to refer to evaluating on data in production, which is not the technical definition of a test set.

  https://www.kdnuggets.com/2017/11/create-good-validation-set.html

• Taming the Python Visualization Jungle, Nov 29 Webinar

  Python has a ton of plotting libraries—but which ones should you use? And how should you go about choosing them? This webinar shows you key starting points and demonstrates how to solve a range of common problems.

  https://www.kdnuggets.com/2017/11/anaconda-taming-python-visualization-jungle.html

• Overview of GANs (Generative Adversarial Networks) – Part I

  A great introductory and high-level summary of Generative Adversarial Networks.

  https://www.kdnuggets.com/2017/11/overview-gans-generative-adversarial-networks-part1.html

• TensorFlow: What Parameters to Optimize?

  Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.

  https://www.kdnuggets.com/2017/11/tensorflow-parameters-optimize.html

• More than the Hype: Beyond Gartner’s Hype Cycle

  Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.

  https://www.kdnuggets.com/2017/11/beyond-gartners-hype-cycle.html

• 7 Steps to Mastering Deep Learning with Keras">7 Steps to Mastering Deep Learning with Keras

  Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.

  https://www.kdnuggets.com/2017/10/seven-steps-deep-learning-keras.html

• 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

• Python Data Preparation Case Files: Removing Instances & Basic Imputation

  This is the first of 3 posts to cover imputing missing values in Python using Pandas. The slowest-moving of the series (out of necessity), this first installment lays out the task and data at the risk of boring you. The next 2 posts cover group- and regression-based imputation.

  https://www.kdnuggets.com/2017/09/python-data-preparation-case-files-basic-imputation.html

• Videos for Business Analytics using Data Mining course

  Here we present links to very useful videos on Business Analytics using data mining courses.

  https://www.kdnuggets.com/2017/09/shmueli-videos-business-analytics-using-data-mining.html

• Are Data Lakes Fake News?">Are Data Lakes Fake News?

  The quick answer is yes, and the biggest problem is that the term “Data Lakes” has been overloaded by vendors and analysts with different meanings, resulting in an ill-defined and blurry concept.

  https://www.kdnuggets.com/2017/09/data-lakes-fake-news.html

• How To Write Better SQL Queries: The Definitive Guide – Part 2

  Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.

  https://www.kdnuggets.com/2017/08/write-better-sql-queries-definitive-guide-part-2.html

• Understanding overfitting: an inaccurate meme in Machine Learning

  Applying cross-validation prevents overfitting is a popular meme, but is not actually true – it more of an urban legend. We examine what is true and how overfitting is different from overtraining.

  https://www.kdnuggets.com/2017/08/understanding-overfitting-meme-supervised-learning.html

• Machine Learning vs. Statistics: The Texas Death Match of Data Science">Machine Learning vs. Statistics: The Texas Death Match of Data Science

  Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness.

  https://www.kdnuggets.com/2017/08/machine-learning-vs-statistics.html

• Recommendation System Algorithms: An Overview

  This post presents an overview of the main existing recommendation system algorithms, in order for data scientists to choose the best one according a business’s limitations and requirements.

  https://www.kdnuggets.com/2017/08/recommendation-system-algorithms-overview.html

• A Guide to Instagramming with Python for Data Analysis">A Guide to Instagramming with Python for Data Analysis

  I am writing this article to show you the basics of using Instagram in a programmatic way. You can benefit from this if you want to use it in a data analysis, computer vision, or any other cool project you can think of.

  https://www.kdnuggets.com/2017/08/instagram-python-data-analysis.html

• Top KDnuggets tweets, Jun 28-Jul 4: Cheat Sheet of #MachineLearning and #Python Cheat Sheets; Learning #DeepLearning with #Keras

  Also: Train your #deeplearning model faster and sharper — two novel techniques; Lecture Collection - Natural Language Processing with #DeepLearning (Winter 2017) [Stanford]; #ICYMI 10 Free Must-Read Books for #MachineLearning and #DataScience

  https://www.kdnuggets.com/2017/07/top-tweets-jun28-jul4.html

• The world’s first protein database for Machine Learning and AI">The world’s first protein database for Machine Learning and AI

  dSPP is the world first interactive database of proteins for AI and Machine Learning, and is fully integrated with Keras and Tensorflow. You can access the database at peptone.io/dspp

  https://www.kdnuggets.com/2017/06/dspp-protein-database-machine-learning-ai.html

• Stay ahead of cyberattacks and fraud with predictive analytics

  Even as cyber criminals and swindlers step up their game, companies can use predictive analytics to stay ahead. Discover the full scope of IBM SPSS predictive analytics capabilities.

  https://www.kdnuggets.com/2017/06/ibm-spss-fraud-predictive-analytics.html

• Data science platforms are on the rise and IBM is leading the way

  Download the 2017 Gartner Magic Quadrant for Data Science Platforms today to learn why IBM is named a leader in data science and to find out why data science, analytics, and machine learning are the engines of the future.

  https://www.kdnuggets.com/2017/05/ibm-data-science-platforms-gartner.html

• How A Data Scientist Can Improve Productivity

  Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.

  https://www.kdnuggets.com/2017/05/data-scientist-improve-productivity.html

• The Two Phases of Gradient Descent in Deep Learning

  In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!

  https://www.kdnuggets.com/2017/05/two-phases-gradient-descent-deep-learning.html

• Data Version Control: iterative machine learning

  ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.

  https://www.kdnuggets.com/2017/05/data-version-control-iterative-machine-learning.html

• Data Science & Machine Learning Platforms for the Enterprise

  A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.

  https://www.kdnuggets.com/2017/05/data-science-machine-learning-platforms-enterprise.html

• Top 10 Machine Learning Videos on YouTube, updated">Top 10 Machine Learning Videos on YouTube, updated

  The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.

  https://www.kdnuggets.com/2017/05/top-10-machine-learning-videos-on-youtube-updated.html

• Models: From the Lab to the Factory

  In this post, we’ll go over techniques to avoid these scenarios through the process of model management and deployment.

  https://www.kdnuggets.com/2017/04/models-from-lab-factory.html

• Dask and Pandas and XGBoost: Playing nicely between distributed systems

  This blogpost gives a quick example using Dask.dataframe to do distributed Pandas data wrangling, then using a new dask-xgboost package to setup an XGBoost cluster inside the Dask cluster and perform the handoff.

  https://www.kdnuggets.com/2017/04/dask-pandas-xgboost-playing-nicely-distributed-systems.html

• AI & Machine Learning Black Boxes: The Need for Transparency and Accountability

  When something goes wrong, as it inevitably does, it can be a daunting task discovering the behavior that caused an event that is locked away inside a black box where discoverability is virtually impossible.

  https://www.kdnuggets.com/2017/04/ai-machine-learning-black-boxes-transparency-accountability.html

• 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

• 7 Types of Data Scientist Job Profiles

  There is no one profile for the Data Scientist, but I tried to make a few generic job profiles that can somewhat fit job descriptions of different companies. I think there is way too much variety, but I had to narrow down on a set of profiles. Check out the list.

  https://www.kdnuggets.com/2017/03/7-types-data-scientist-job-profiles.html

• 6 Business Concepts you need to become a Data Science Unicorn">6 Business Concepts you need to become a Data Science Unicorn

  Are you a data science professional and want to advance your career as Data Science Unicorn? Here we provide important business concepts and guidelines required for a data science techie to become a Unicorn.

  https://www.kdnuggets.com/2017/03/6-business-concepts-data-science-unicorn.html

• Pandas Cheat Sheet: Data Science and Data Wrangling in Python">Pandas Cheat Sheet: Data Science and Data Wrangling in Python

  The Pandas library can seem very elaborate and it might be hard to find a single point of entry to the material: with other learning materials focusing on different aspects of this library, you can definitely use a reference sheet to help you get the hang of it.

  https://www.kdnuggets.com/2017/01/pandas-cheat-sheet.html

• Why the Data Scientist and Data Engineer Need to Understand Virtualization in the Cloud

  This article covers the value of understanding the virtualization constructs for the data scientist and data engineer as they deploy their analysis onto all kinds of cloud platforms. Virtualization is a key enabling layer of software for these data workers to be aware of and to achieve optimal results from.

  https://www.kdnuggets.com/2017/01/data-scientist-engineer-understand-virtualization-cloud.html

• Deep Learning Can be Applied to Natural Language Processing">Deep Learning Can be Applied to Natural Language Processing

  This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.

  https://www.kdnuggets.com/2017/01/deep-learning-applied-natural-language-processing.html

• Laying the Foundation for a Data Team

  Admittedly, there is a lot more to building a successful data team, and we would be lying if we pretended we have it all figured out. But hopefully focusing on the elements in this post is a good start.

  https://www.kdnuggets.com/2016/12/laying-foundation-data-team.html

• 50+ Data Science, Machine Learning Cheat Sheets, updated">50+ Data Science, Machine Learning Cheat Sheets, updated

  Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.

  https://www.kdnuggets.com/2016/12/data-science-machine-learning-cheat-sheets-updated.html

• Questions To Ask When Moving Machine Learning From Practice to Production

  An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.

  https://www.kdnuggets.com/2016/11/moving-machine-learning-practice-production.html

• A Reference Architecture for Self-Service Analytics

  The keys to self-service analytics success are organizational. In addition to a governed self-service architecture, companies need to establish governance committees and gateways, create federated organizations with co-located BI developers, and provide continuous education, training, and support. Learn how to do this in this report.

  https://www.kdnuggets.com/2016/11/eckerson-reference-architecture-self-service-analytics.html

• How to Rank 10% in Your First Kaggle Competition

  This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.

  https://www.kdnuggets.com/2016/11/rank-ten-precent-first-kaggle-competition.html

• Eight Things an R user Will Find Frustrating When Trying to Learn Python">Eight Things an R user Will Find Frustrating When Trying to Learn Python

  Are you an R user considering learning Python? Here's some insight into what you may be up against, and what, specifically, you may find frustrating. But don't worry, it's not all terrible.

  https://www.kdnuggets.com/2016/11/r-user-frustrating-learning-python.html

• Learn Data Science in 8 (Easy) Steps

  Want to learn data science? Check out these 8 (easy) steps to set out in the right direction!

  https://www.kdnuggets.com/2016/10/learn-data-science-8-steps.html

• Battle of the Data Science Venn Diagrams">Battle of the Data Science Venn Diagrams

  First came Drew Conway's data science Venn diagram. Then came all the rest. Read this comparative overview of data science Venn diagrams for both the insight into the profession and the humor that comes along for free.

  https://www.kdnuggets.com/2016/10/battle-data-science-venn-diagrams.html

• Data Science of Reviews: ReviewMeta tool Automatically Detects Unnatural Reviews on Amazon

  ReviewMeta is a tool that analyzes millions of reviews and helps customers decide which ones to trust. As the dataset grows, so do the insights on unbiased reviews.

  https://www.kdnuggets.com/2016/08/data-science-reviews-reviewmeta-detects-unnatural-amazon-reviews.html

• 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

• Visualizing 1 Billion Points of Data: Doing It Right – Aug 18 Webinar

  Join Continuum Analytics on August 18 for a webinar on Big Data visualization with the datashader library. Save your spot today!

  https://www.kdnuggets.com/2016/08/continuum-visualizing-1-billion-points-data.html

• Big Data Key Terms, Explained

  Just getting started with Big Data, or looking to iron out the wrinkles in your current understanding? Check out these 20 Big Data-related terms and their concise definitions.

  https://www.kdnuggets.com/2016/08/big-data-key-terms-explained.html

• Reinforcement Learning and the Internet of Things

  Gain an understanding of how reinforcement learning can be employed in the Internet of Things world.

  https://www.kdnuggets.com/2016/08/reinforcement-learning-internet-things.html

• How to Start Learning Deep Learning

  Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.

  https://www.kdnuggets.com/2016/07/start-learning-deep-learning.html

• Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey

  This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.

  https://www.kdnuggets.com/2016/07/top-machine-learning-moocs-online-lectures.html

• Text Mining 101: Topic Modeling

  We introduce the concept of topic modelling and explain two methods: Latent Dirichlet Allocation and TextRank. The techniques are ingenious in how they work – try them yourself.

  https://www.kdnuggets.com/2016/07/text-mining-101-topic-modeling.html

• Improving Nudity Detection and NSFW Image Recognition

  This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?

  https://www.kdnuggets.com/2016/06/algorithmia-improving-nudity-detection-nsfw-image-recognition.html

• Apache Spark Key Terms, Explained

  An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.

  https://www.kdnuggets.com/2016/06/spark-key-terms-explained.html

• Cloud Computing Key Terms, Explained

  A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.

  https://www.kdnuggets.com/2016/06/cloud-computing-key-terms-explained.html

• Tips for Data Scientists: Think Like a Business Executive

  Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.

  https://www.kdnuggets.com/2016/05/tips-data-scientist-think-like-executive.html

• Top 15 Frameworks for Machine Learning Experts

  Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.

  https://www.kdnuggets.com/2016/04/top-15-frameworks-machine-learning-experts.html

• Deep Learning for Internet of Things Using H2O

  H2O is feature-rich open source machine learning platform known for its R and Spark integration and it’s ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things.

  https://www.kdnuggets.com/2016/04/deep-learning-iot-h2o.html

• R Learning Path: From beginner to expert in R in 7 steps

  This learning path is mainly for novice R users that are just getting started but it will also cover some of the latest changes in the language that might appeal to more advanced R users.

  https://www.kdnuggets.com/2016/03/datacamp-r-learning-path-7-steps.html

• Top Spark Ecosystem Projects

  Apache Spark has developed a rich ecosystem, including both official and third party tools. We have a look at 5 third party projects which complement Spark in 5 different ways.

  https://www.kdnuggets.com/2016/03/top-spark-ecosystem-projects.html

• Amazon Machine Learning: Nice and Easy or Overly Simple?

  Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service is fast, offers a simple workflow but lacks model selection features and has slow execution times.

  https://www.kdnuggets.com/2016/02/amazon-machine-learning-nice-easy-simple.html

• Python Data Science with Pandas vs Spark DataFrame: Key Differences

  A post describing the key differences between Pandas and Spark's DataFrame format, including specifics on important regular processing features, with code samples.

  https://www.kdnuggets.com/2016/01/python-data-science-pandas-spark-dataframe-differences.html

• Data Science Resume Tips and Guidelines

  A well-built resume is key to get through the first door – in the process of getting hired as a Data Scientist. Learn more, about how to present yourself as a true DS and which pitfalls to avoid.

  https://www.kdnuggets.com/2016/01/data-science-resume-tips-guidelines.html

• Make Beautiful Interactive Data Visualizations Easily, Dec 15 Webinar

  Learn how to use Bokeh interactive visualization framework for open data science to create rich, interactive visualizations in the browser, without writing a line of JavaScript, HTML, or CSS.

  https://www.kdnuggets.com/2015/12/continuum-beautiful-interactive-data-visualizations-webinar.html

• Data Science of IoT: Sensor fusion and Kalman filters, Part 2

  The second part of this tutorial examines use of Kalman filters to determine context for IoT systems, which helps to combine uncertain measurements in a multi-sensor system to accurately and dynamically understand the physical world.

  https://www.kdnuggets.com/2015/11/data-science-iot-sensor-fusion-kalman-filters-part2.html

• Topological Data Analysis – Open Source Implementations

  Topological Data Analysis (TDA) is making waves in the analytics community lately, but are there open source options available?

  https://www.kdnuggets.com/2015/11/topological-data-analysis-open-source-implementations.html

• 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

• 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more

  Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.

  https://www.kdnuggets.com/2015/09/free-data-science-books.html

• Data is Ugly – Tales of Data Cleaning

  Whether you want to do business analytics or build the deep learning models, getting correct data and cleansing it appropriately remains the major task. Find out experts opinions on how you can make efficient data cleansing and collection efforts.

  https://www.kdnuggets.com/2015/08/data-ugly-tales-data-cleaning.html

• 50+ Data Science and Machine Learning Cheat Sheets

  Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.

  https://www.kdnuggets.com/2015/07/good-data-science-machine-learning-cheat-sheets.html

• Top 20 R packages by popularity

  Wondering which are the most popular R packages? Here's an analysis based on most downloaded R packages from Jan to May 2015 to identify the top trending packages in the R world!

  https://www.kdnuggets.com/2015/06/top-20-r-packages.html

• Interview: Joseph Babcock, Netflix on Genie, Lipstick, and Other In-house Developed Tools

  We discuss role of analytics in content acquisition, data architecture at Netflix, organizational structure, and open-source tools from Netflix.

  https://www.kdnuggets.com/2015/06/interview-joseph-babcock-netflix-in-house-developed-tools.html

• Exclusive Interview: Matei Zaharia, creator of Apache Spark, on Spark, Hadoop, Flink, and Big Data in 2020

  Apache Spark is one the hottest Big Data technologies in 2015. KDnuggets talks to Matei Zaharia, creator of Apache Spark, about key things to know about it, why it is not a replacement for Hadoop, how it is better than Flink, and vision for Big Data in 2020.

  https://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.html

• WebDataCommons – the Data and Framework for Web-scale Mining

  The WebDataCommons project extracts the largest publicly available hyperlink graph, large product-, address-, recipe-, and review corpora, as well as millions of HTML tables from the Common Crawl web corpus and provides the extracted data for public download.

  https://www.kdnuggets.com/2015/05/webdatacommons-data-web-scale-mining.html

• Data Science 101: Preventing Overfitting in Neural Networks

  Overfitting is a major problem for Predictive Analytics and especially for Neural Networks. Here is an overview of key methods to avoid overfitting, including regularization (L2 and L1), Max norm constraints and Dropout.

  https://www.kdnuggets.com/2015/04/preventing-overfitting-neural-networks.html

• Interview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started

  We discuss how Deep Learning is different from the other methods of Machine Learning, unique characteristics and benefits of Deep Learning, and the key components of H2O architecture.

  https://www.kdnuggets.com/2015/01/interview-arno-candel-0xdata-deep-learning.html

• 16 NoSQL, NewSQL Databases To Watch

  NoSQL and NewSQL databases have become much more important with the proliferation of big, mobile, and networked data, and these sixteen database solutions are some of the biggest up-and-comers.

  https://www.kdnuggets.com/2014/12/16-nosql-newsql-databases-to-watch.html

• 9 Must-Have Skills You Need to Become a Data Scientist

  Burtch Works details the top 9 data science skills that potential data scientists must have to be competitive in this growing marketplace from the perspective of a recruiter.

  https://www.kdnuggets.com/2014/11/9-must-have-skills-data-scientist.html

• Dataiku Data Science Studio

  Data Science Studio (DSS) from Dataiku is a complete Data Science software tool for developers and analysts, which significantly shortens the time-consuming load-clean-train-test-deploy cycles of building predictive applications. A community edition and a free trial available.

  https://www.kdnuggets.com/2014/08/dataiku-data-science-studio.html

• Guide to Data Science Cheat Sheets

  Selection of the most useful Data Science cheat sheets, covering SQL, Python (including NumPy, SciPy and Pandas), R (including Regression, Time Series, Data Mining), MATLAB, and more.

  https://www.kdnuggets.com/2014/05/guide-to-data-science-cheat-sheets.html

• MADlib: Big Data Machine Learning in SQL for Data Scientists

  MADlib is open source with commercially usable BSD license; supports Postgres and Pivotal Greenplum DBMS, and provides classification, regression, clustering, topic modeling and other analytics for Big Data.

  https://www.kdnuggets.com/2014/01/madlib-big-data-machine-learning-sql-for-data-scientists.html

• Clustering and Segmentation Software

  Commercial Clustering Software BayesiaLab, includes Bayesian classification algorithms for data segmentation and uses Bayesian networks to automatically cluster the variables. ClustanGraphics3, hierarchical cluster analysis from Read more »

  https://www.kdnuggets.com/software/clustering.html

• LinkedIn InMaps – Visualize your network

  InMaps provides a visual representation of your professional Linkedin universe, and allows you to better understand your professional ties and the relationship patterns.

  https://www.kdnuggets.com/2013/10/linkedin-inmaps-visualize-your-network.html