- Cartoon: Thanksgiving, Big Data, and Turkey Data Science. - Nov 22, 2018.
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.
Cartoon, Data Science, Thanksgiving
The Big Data Game Board™ - Nov 19, 2018.
Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!
Big Data, Data Science, Games
What is the Best Python IDE for Data Science? - Nov 14, 2018.
Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.
Data Science, IDE, Jupyter, Programming, Python
- The 5 Basic Statistics Concepts Data Scientists Need to Know - Nov 13, 2018.
Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!
Data Science, Data Scientist, Statistics
- Self-Service Analytics and Operationalization – Why You Need Both - Nov 12, 2018.
Get the guidebook / whitepaper for a look at how today's top data-driven companies scale their advanced analytics & machine learning efforts.
Analytics, Data Science, Dataiku, Deployment, Self-service
- Best Practices for Using Notebooks for Data Science - Nov 8, 2018.
Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.
Best Practices, Data Science, Jupyter
10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
Data Science, Deep Learning, fast.ai, Google, Linear Algebra, Machine Learning, MIT, NLP, Reinforcement Learning, Stanford, Yandex
- The Most in Demand Skills for Data Scientists - Nov 2, 2018.
Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget?
Career, Data Science, Data Science Skills, LinkedIn, Python vs R
- Data Science “Paint by the Numbers” with the Hypothesis Development Canvas - Nov 2, 2018.
Now you are ready to take the next step from a Big Data MBA perspective by building off of the Business Model Canvas to flesh out the business use cases – or hypothesis – which is where we can become more effective at leveraging data and analytics to optimize our the business.
Big Data, Business, Data Science
- How Data Science Is Improving Higher Education - Nov 1, 2018.
Increasingly, colleges and universities, as well as governments, are using data science to improve the ways educational institutions do everything from recruiting to engaging with students to budgeting.
Data Science, Education
Graphs Are The Next Frontier In Data Science - Oct 18, 2018.
GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science.
Conference, Data Science, Graph Analytics, Neo4j
- Applied Data Science: Solving a Predictive Maintenance Business Problem Part 3 - Oct 16, 2018.
In this post we will expand our analysis to multiple variables and then see how intuitions we develop during the exploration phase, can lead to generating new features for modelling.
Business Context, Data Science, Predictive Maintenance
- Using Confusion Matrices to Quantify the Cost of Being Wrong - Oct 11, 2018.
The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.
Confusion Matrix, Data Science, Machine Learning, Metrics, Predictive Modeling
- How To Learn Data Science If You’re Broke - Oct 9, 2018.
A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more.
Beginners, Career, Data Science, Data Science Education
- Understand Why ODSC is the Most Recommended Conference for Applied Data Science - Oct 4, 2018.
Running 4 days, 40 training sessions, 50 workshops, and over 200 speakers, an ODSC conference offers unparalleled depth and breadth in deep learning, machine learning, and other data science topics. Save 20% offer ends tomorrow. Register now!
CA, Data Science, ODSC, San Francisco
- 5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects - Oct 2, 2018.
In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.
Cross-validation, Data Science, Machine Learning
- Raspberry Pi IoT Projects for Fun and Profit - Sep 27, 2018.
In this post, I will explain how to run an IoT project from the command line, without graphical interface, using Ubuntu Core in a Raspberry Pi 3.
Pages: 1 2
Data Science, IoT, Python, Raspberry Pi
- Diversity in Data Science: Overview and Strategy - Sep 24, 2018.
We take a hard look at diversity within the tech industry, root causes, and potential solutions and highlight resources/initiatives that can connect readers with programs aiding their professional development.
Career, Data Science, Diversity, Hiring, Trends, Women
6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study - Sep 20, 2018.
Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.
Data Science, Machine Learning, Neural Networks
- A Winning Game Plan For Building Your Data Science Team - Sep 18, 2018.
We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder.
Data Engineering, Data Science, Data Science Team
- Ethics + Data Science: opinion by DJ Patil, former US Chief Data Scientist - Sep 14, 2018.
How much has data changed our lives over the past decade? Former US Chief Data Scientist DJ Patil investigates.
Data Science, DJ Patil, Ethics, Social Good
- The Growing Participation of Women in the Data Science Community - Sep 14, 2018.
We still have a long way to go before the gender representation becomes more equalized, but the field at large indicates hopeful trends about women working in the role or desiring to do so in the future.
Data Science, STEM, Women
- Data Science Cheat Sheet - Sep 6, 2018.
Check out this new data science cheat sheet, a relatively broad undertaking at a novice depth of understanding, which concisely packs a wide array of diverse data science goodness into a 9 page treatment.
Cheat Sheet, Data Science
- What on earth is data science? - Sep 4, 2018.
An overview and discussion around data science, covering the history behind the term, data mining, statistical inference, machine learning, data engineering and more.
Data Mining, Data Science, Decision Making, Statistics
- 5 Resources to Inspire Your Next Data Science Project - Sep 4, 2018.
In this post, my intention is provide some useful tips and resources to springboard you into your next data science project.
Data Science, Resources
- UX Design Guide for Data Scientists and AI Products - Aug 21, 2018.
Realizing that there is a legitimate knowledge gap between UX Designers and Data Scientists, I have decided to attempt addressing the needs from the Data Scientist’s perspective.
AI, Data Science, Data Scientist, UI/UX
- Interpreting a data set, beginning to end - Aug 20, 2018.
Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.
Analytics, Big Data, Data Science, Data Visualization, Machine Learning, SAS, Statistics, t-SNE
- Project Hydrogen, new initiative based on Apache Spark to support AI and Data Science - Aug 16, 2018.
An introduction to Project Hydrogen: how it can assist machine learning and AI frameworks on Apache Spark and what distinguishes it from other open source projects.
AI, Apache Spark, Data Science, Databricks, Distributed Computing, Production
Data Scientist guide for getting started with Docker - Aug 14, 2018.
Docker is an increasingly popular way to create and deploy applications through virtualization, but can it be useful for data scientists? This guide should help you quickly get started.
Data Science, Data Scientist, Docker, Jupyter
Programming Best Practices For Data Science - Aug 7, 2018.
In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.
Best Practices, Data Science, Pandas, Programming, Python
- DevOps for Data Scientists: Taming the Unicorn - Jul 27, 2018.
How do we version control the model and add it to an app? How will people interact with our website based on the outcome? How will it scale!?
Data Science, Data Scientist, DevOps, Unicorn, Version Control
- Data Science For Business: 3 Reasons You Need To Learn The Expected Value Framework - Jul 26, 2018.
This article highlights the importance of learning the expected value framework in data science, covering classification, maximization and testing.
Business, Business Value, Data Science, H2O
How to Build a Data Science Portfolio - Jul 25, 2018.
This post will include links to where various data science professionals (data science managers, data scientists, social media icons, or some combination thereof) and others talk about what to have in a portfolio and how to get noticed.
Advice, Career, Data Science, Portfolio, Resume, Social Media
Cookiecutter Data Science: How to Organize Your Data Science Project - Jul 24, 2018.
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Data Science, Programming, Project, Python
- Building A Data Science Product in 10 Days - Jul 23, 2018.
At startups, you often have the chance to create products from scratch. In this article, the author will share how to quickly build valuable data science products, using his first project at Instacart as an example.
Data Science, Instacart, Product
- Explaining the 68-95-99.7 rule for a Normal Distribution - Jul 19, 2018.
This post explains how those numbers were derived in the hope that they can be more interpretable for your future endeavors.
Data Analysis, Data Science, Normal Distribution, Python, Statistics
- The 4 Levels of Data Usage in Data Science - Jul 9, 2018.
This is an overview of the 4 levels, or "buckets," of data usage in business, starting at monitoring and progressing to automation.
Automation, Ben Lorica, Business, Data Science, O'Reilly
- Cartoon: How is Data Science Different From Religion? - Jul 8, 2018.
This difference between Data Science and Religion is not what you expect ...
Cartoon, Data Science, Religion
- 5 of Our Favorite Free Visualization Tools - Jul 5, 2018.
5 key free data visualization tools that can provide flexible and effective data presentation.
Analytics, D3.js, Data Science, Data Visualization, Free Software, R, Tableau
- Why a Professional Association for Data Scientists is a Bad Idea - Jul 2, 2018.
This post presents the argument against having a professional association for data scientists.
Certification, Data Science, Data Science Education, SIGKDD, Trends
- Automated Machine Learning vs Automated Data Science - Jul 2, 2018.
Just by adding the term "automated" in front of these 2 separate, distinct concepts does not somehow make them equivalent. Machine learning and data science are not the same thing.
Automated Data Science, Automated Machine Learning, Data Science, Machine Learning
Top 20 Python Libraries for Data Science in 2018 - Jun 27, 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.
Pages: 1 2
Bokeh, Data Science, Keras, Matplotlib, NLTK, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn, SciPy, Seaborn, TensorFlow, XGBoost
5 Data Science Projects That Will Get You Hired in 2018 - Jun 26, 2018.
A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.
Data Preparation, Data Science, Data Visualization, Hiring, Jupyter, Machine Learning
- Data Science Predicting The Future - Jun 19, 2018.
In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.
Data Science, Forecasting, Machine Learning, Programming Languages, Regression
- Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray - Jun 15, 2018.
While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.
AI, Computer Science, Data Science, Judea Pearl, Statistics
- Advice For Applying To Data Science Jobs - Jun 13, 2018.
A comprehensive guide to applying for a job in data science, covering the application, interview and offer stage.
Advice, Career, Data Science, Jobs
- The What, Where and How of Data for Data Science - Jun 12, 2018.
Here we will take data science apart and build it back up to a coherent and manageable concept. Bear with us!
Big Data, Data Science
- DIY Deep Learning Projects - Jun 8, 2018.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
Computer Vision, Data Science, Deep Learning, LinkedIn, Neural Networks, OpenCV, Python
- How (dis)similar are my train and test data? - Jun 7, 2018.
This articles examines a scenario where your machine learning model can fail.
Data Science, Datasets, Feature Selection, Machine Learning, Training Data
- Command Line Tricks For Data Scientists - Jun 7, 2018.
Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive.
Data Science, Data Science Tools, Data Scientist
The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R? - Jun 6, 2018.
We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.
Anaconda, Apache Spark, Data Science, Keras, Machine Learning, Open Source, Poll, Python, R, RapidMiner, Scala, scikit-learn, TensorFlow
- Resources For Women In Data Science and Machine Learning - Jun 4, 2018.
A comprehensive list of resources for Women in Data Science and Machine Learning, including a list of useful tech groups and published lists for finding Women speakers.
Data Science, Diversity, Meetings, Resources, Women
- The Book of Why - Jun 1, 2018.
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.
Bayesian Networks, Causality, Data Science, Judea Pearl, Simpson's Paradox, Statistics
A Beginner’s Guide to the Data Science Pipeline - May 29, 2018.
On one end was a pipe with an entrance and at the other end an exit. The pipe was also labeled with five distinct letters: "O.S.E.M.N."
Beginners, Data Science, Pipeline
- 10 More Free Must-Read Books for Machine Learning and Data Science - May 28, 2018.
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.
Books, Data Science, ebook, Free ebook, Machine Learning
- Top 20 R Libraries for Data Science in 2018 - May 25, 2018.
We have prepared an infographic of Top 20 R packages for data science, which covers the libraries main features and GitHub activities, as all of the libraries are open-source.
Data Science, Infographic, R
- Data Science: 4 Reasons Why Most Are Failing to Deliver - May 24, 2018.
Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.
Data Science, Deployment, Domino, Failure, Production
- Scientific debt – what does it mean for Data Science? - May 23, 2018.
This article analyses scientific debt - what it is and what it means for data science.
Business, Data Engineering, Data Science, DataCamp, Technical Debt
- The Executive Guide to Data Science and Machine Learning - May 10, 2018.
This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.
Big Data, Business, Data Science, Machine Learning
- Torus for Docker-First Data Science - May 8, 2018.
To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.
Data Science, DevOps, Docker, Machine Learning Engineer, Open Source, Python
- Top Data Science, Machine Learning Courses from Udemy – May 2018 - May 8, 2018.
Learn Machine Learning, Data Science, Python, Azure Machine Learning, and more with Udemy Mother's Day $9.99 sale - get top courses from leading instructors.
Azure ML, Data Science, Machine Learning, Python, Udemy
2018 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2018.
Vote in KDnuggets 19th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
Data Mining Software, Data Science, Machine Learning, Poll
- Skewness vs Kurtosis – The Robust Duo - May 4, 2018.
Kurtosis and Skewness are very close relatives of the “data normalized statistical moment” family – Kurtosis being the fourth and Skewness the third moment, and yet they are often used to detect very different phenomena in data. At the same time, it is typically recommendable to analyse the outputs of both together to gather more insight and understand the nature of the data better.
Data Science, Descriptive Analytics, Statistics
- Boost your data science skills. Learn linear algebra. - May 3, 2018.
The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.
Data Science, Linear Algebra, Mathematics, numpy, Python
- To Kaggle Or Not - May 2, 2018.
Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.
Advice, Competition, Data Science, Kaggle
- KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric - May 2, 2018.
Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial
Blockchain, Convolutional Neural Networks, Data Science, Machine Learning, Metrics, numpy, Python
Data Science vs Machine Learning vs Data Analytics vs Business Analytics - May 1, 2018.
This article gives a broad overview of data science and the various fields within it, including business analytics, data analytics, business intelligence, advanced analytics, machine learning, and AI.
AI, Business, Business Analytics, Data Analytics, Data Science, Machine Learning
- The Dirty Little Secret Every Data Scientist Knows (but won’t admit) - Apr 26, 2018.
Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!
Data Cleaning, Data Preparation, Data Science, Machine Learning
- Data Science Interview Guide - Apr 25, 2018.
Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).
Pages: 1 2
Data Science, Interview
7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
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.
Book, Data Science, Ian Goodfellow, Machine Learning, Mathematics, Robert Tibshirani, Vladimir Vapnik
Key Algorithms and Statistical Models for Aspiring Data Scientists - Apr 16, 2018.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
Algorithms, Data Science, Machine Learning, Online Education, Statistics
- Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor - Apr 6, 2018.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
Data Science, Descriptive Analytics, Statistics
- A Day in the Life of a Data Scientist: Part 4 - Apr 2, 2018.
Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
Advice, Career, Data Science, Data Scientist
- 8 Common Pitfalls That Can Ruin Your Prediction - Mar 21, 2018.
A good prediction can help your work and make it easier. But how can you be sure that your prediction is good? Here are some common pitfalls that you should avoid.
Advice, Data Science, Outliers, Overfitting, Predictive Analytics
Top 12 Essential Command Line Tools for Data Scientists - Mar 21, 2018.
This post is a short introductory overview of 12 Unix-like operating system command line tools of value to data science tasks, and the data scientists who perform them.
Data Exploration, Data Science, Data Science Tools
- Ranking Popular Distributed Computing Packages for Data Science - Mar 20, 2018.
We examined 140 frameworks and distributed programing packages and came up with a list of top 20 distributed computing packages useful for Data Science, based on a combination of Github, Stack Overflow, and Google results.
Apache Spark, Data Science, Distributed Systems, GitHub, Hadoop
- Descriptive Statistics: The Mighty Dwarf of Data Science - Mar 20, 2018.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
Data Science, Descriptive Analytics, Statistics
- Multiscale Methods and Machine Learning - Mar 19, 2018.
We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.
Algorithms, Data Science, Deep Learning, Machine Learning, Statistics
- Your free 70-page guide to a career in data science - Mar 16, 2018.
To help you become a Data Scientist, we put together a guide with answers to: how do you break into the profession? What skills do you need to become a data scientist? Where are best data science jobs?
Advice, Career, Data Science, Springboard
- A Beginner’s Guide to Data Engineering – Part II - Mar 15, 2018.
In this post, I share more technical details on how to build good data pipelines and highlight ETL best practices. Primarily, I will use Python, Airflow, and SQL for our discussion.
Pages: 1 2
AirBnB, Data Engineering, Data Science, ETL, Pipeline, Python, SQL
- 5 Things to Know Before Rushing to Start in Data Science - Mar 13, 2018.
Strong math understanding, computing skills, critical thinking and presentations skills provide a strong foundation for a career in Data Science.
Advice, Business Analytics, Career, Data Science, Data Science Education
18 Inspiring Women In AI, Big Data, Data Science, Machine Learning - Mar 8, 2018.
For the 2018 international women's day, we profile 18 inspiring women who lead the field in AI, Analytics, Big Data , Data science, and Machine Learning areas.
AI, Big Data, Carla Gentry, Data Science, Fei-Fei Li, Hilary Mason, Jill Dyche, Meta Brown, Monica Rogati, Women
- Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box - Mar 8, 2018.
The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.
Andrew Ng, Data Science, Data Scientist, Deep Learning, Machine Learning
- Should You Ever Volunteer Your Data Skills for Free? - Mar 6, 2018.
The question has probably come up of whether it’s ever okay to offer your data-related knowledge to people or organizations for free. Does taking that approach ever benefit you?
Career, Data Science, Social Good
- Time Series for Dummies – The 3 Step Process - Mar 5, 2018.
Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.
Data Science, Deep Learning, Machine Learning, Predictive Modeling, Stationarity, Time Series
- Data Science in Fashion - Mar 2, 2018.
Fashion industry is an extremely competitive and dynamic market. Trends and styles change with the blink of an eye. Data Science can be used here on historical data to predict the trends which will be “Hot” hence potentially saving a lot of time and money.
Brands, Data Science, Fashion, Retail, Supply Chain
- How to Survive Your Data Science Interview - Mar 1, 2018.
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 company
Advice, Career, Data Science, Interview
- Applied Data Science: Solving a Predictive Maintenance Business Problem Part 2 - Feb 20, 2018.
In this post we will discuss further on how exploratory analysis can be used for getting insights for feature engineering.
Data Analysis, Data Exploration, Data Science, Feature Engineering
- 5 Things You Need To Know About Data Science - Feb 19, 2018.
Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists
Algorithms, BI, Data Analytics, Data Mining, Data Science, Data Science Education, Data Scientist, Google Trends, Jobs, Machine Learning
- Histogram 202: Tips and Tricks for Better Data Science - Feb 15, 2018.
We show how to make an ideal histogram, share some tips, and give examples. Let's dive into the world of binning.
Data Science, Histogram, Statistics
Data Science at the Command Line: Exploring Data - Feb 14, 2018.
See what's available in the freely-available book "Data Science at the Command Line" by digging into data exploration in the terminal.
Data Exploration, Data Science, Data Science Tools
- 7 Steps of a Data Science PoC – Get The Guidebook - Feb 12, 2018.
Download a free copy of the white paper The 7 Steps to Driving a Successful Data Science POC for a detailed walk-through of the seven steps to running a successful POC.
Data Science, Dataiku, Proof-of-concept, White Paper
- Top 15 Scala Libraries for Data Science in 2018 - Feb 9, 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.
Apache Spark, Data Analysis, Data Science, Data Visualization, Machine Learning, NLP, Scala
- Why Data Scientists Must Know About Change Management - Feb 8, 2018.
Change management may be seen as an opposite to data science, but in reality both are related. Without proper implementation, a data science project fails.
Change Management, Data Science, Implementation
- Deep Feature Synthesis: How Automated Feature Engineering Works - Feb 7, 2018.
Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.
Automated Machine Learning, Automation, Data Science, Feature Engineering, Machine Learning
- Generalists Dominate Data Science - Feb 2, 2018.
An interesting insight into why small teams generalists outperform large teams of specialists.
Data Science, Data Science Team
- Governance in Data Science - Jan 16, 2018.
Governance roles for data science and analytics teams are becoming more common... One of the key functions of this role is to perform analysis and validation of data sets in order to build confidence in the underlying data sets.
Data Governance, Data Preparation, Data Science
The Art of Learning Data Science - Jan 9, 2018.
A beginner’s account of getting into comfort zone of learning Data Science.
Coursera, Data Science, Data Science Education, Kaggle, LinkedIn, MOOC
- How Nonprofits Can Benefit from the Power of Data Science - Jan 3, 2018.
Nonprofits can use analytics to boost their fundraising efforts, measure and monitor the impact of their activities, build predictive models, optimize allocation of funds, and more
Big Data, Data Science, Social Good
- Docker for Data Science - Jan 2, 2018.
Coming from a statistics background I used to care very little about how to install software and would occasionally spend a few days trying to resolve system configuration issues. Enter the god-send Docker almighty.
Data Science, Docker
How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science? - Dec 20, 2017.
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.
Data Science, Engineer, Machine Learning, Mathematics
Transitioning to Data Science: How to become a data scientist, and how to create a data science team - Dec 15, 2017.
"A good data scientist in my mind is the person that takes the science part in data science very seriously; a person who is able to find problems and solve them using statistics, machine learning, and distributed computing."
Career, Data Science, Data Science Team, Data Scientist
- Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018 - Dec 12, 2017.
The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.
2018 Predictions, Carla Gentry, Data Science, Eric Siegel, Jeff Ullman, Jen Underwood, Jill Dyche, Kirk D. Borne, Machine Learning, Predictions, Rexer Analytics, Tom Davenport, Trends
- Web Scraping for Data Science with Python - Dec 6, 2017.
We take a quick look at how web scraping can be useful in the context of data science projects, eg to construct a social graph based of S&P 500 companies, using Python and Gephi.
Bart Baesens, Data Science, Python, S&P 500, Web Mining, Web Scraping
- 8 Ways to Improve Your Data Science Skills in 2 Years - Nov 17, 2017.
Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. That’s exactly why this guide is focused on honing the most beneficial skills in two years.
Data Science, Data Science Skills, Skills, Training
- You have created your first Linear Regression Model. Have you validated the assumptions? - Nov 15, 2017.
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.
Data Science, Linear Regression, Machine Learning, Multicollinearity, Statistics
The 10 Statistical Techniques Data Scientists Need to Master - Nov 15, 2017.
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.
Pages: 1 2
Algorithms, Data Science, Data Scientist, Machine Learning, Statistical Learning, Statistics
A Day in the Life of a Data Scientist - Nov 13, 2017.
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
Advice, Career, Data Science, Data Scientist
- Process Mining with R: Introduction - Nov 2, 2017.
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.
Pages: 1 2
Data Mining, Data Science, Process Mining, R
6 Books Every Data Scientist Should Keep Nearby - Oct 31, 2017.
The best way to stay in touch is to continue brushing up on your knowledge while also maintaining experience. It’s the perfect storm or combination of skills to help you succeed in the industry.
Books, Data Science, Data Scientist, Machine Learning
- Business intuition in data science - Oct 24, 2017.
Data Science projects are not just use of algorithms & building models; there are other steps of the project which are equally important. Here we explain them in detail.
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Business, Business Analytics, Data Science
- Want to Become a Data Scientist? Read This Interview First - Oct 13, 2017.
There’s been a lot of hype about Data Science... and probably just as much confusion about it.
Data Science, Data Science Education, Data Scientist
- Data Science Bootcamp in Zurich, Switzerland, January 15 – April 6, 2018 - Oct 12, 2017.
Come to the land of chocolate and Data Science where the local tech scene is booming and the jobs are a plenty. Learn the most important concepts from top instructors by doing and through projects. Use code KDNUGGETS to save.
Bootcamp, Data Science, Data Visualization, Machine Learning, NLP, Python, R, Switzerland, Zurich
- An opinionated Data Science Toolbox in R from Hadley Wickham, tidyverse - Oct 10, 2017.
Get your productivity boosted with Hadley Wickham's powerful R package, tidyverse. It has all you need to start developing your own data science workflows.
Data Analysis, Data Science, Data Science Platform, Data Science Tools, Hadley Wickham, R, Tidyverse
- Data Science –The need for a Systems Engineering approach - Oct 5, 2017.
We need a greater emphasis on the Systems Engineering aspects of Data Science. I am exploring these ideas as part of my course "Data Science for Internet of Things" at the University of Oxford.
Data Science, Oxford, Systems Engineering, TensorFlow
- Applied Data Science: Solving a Predictive Maintenance Business Problem - Oct 5, 2017.
The use case involved is to predict the end life of large industrial batteries, which falls under the genre of use cases called preventive maintenance use cases.
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Business, Data Science, Predictive Maintenance
XGBoost, a Top Machine Learning Method on Kaggle, Explained - Oct 3, 2017.
Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.
Algorithms, Data Science, Explained, Kaggle, Machine Learning
- The 5 Best Industries to Find a Job in Data Science - Oct 3, 2017.
There’s never been a better time to pursue a career in this field. With that in mind, here are five extremely practical and exciting fields you could leave a mark on with an education in data science.
Career, Data Science, Industries
Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated - Sep 26, 2017.
Looking for advice? Guidance? Stories? We’ve put a list of the top ten LinkedIn influencers of the last three months, follow them and stay up-to-date with the latest news in Big Data, Data Science, Analytics, Machine Learning and AI.
About Gregory Piatetsky, Bernard Marr, Big Data, Carla Gentry, Data Science, DJ Patil, Influencers, Kirk D. Borne, LinkedIn, Machine Learning, Tom Davenport, Trends
- Visualizing High Dimensional Data In Augmented Reality - Sep 25, 2017.
When Data Scientists first get a data set, they oftne use a matrix of 2D scatter plots to quickly see the contents and relationships between pairs of attributes. But for data with lots of attributes, such analysis does not scale.
Data Science, Data Visualization, IBM, Instacart, Machine Learning, R
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
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.
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Cheat Sheet, Data Science, Deep Learning, Machine Learning, Neural Networks, Probability, Python, R, SQL, Statistics
- Evaluating Data Science Projects: A Case Study Critique - Sep 19, 2017.
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.
Data Science, SVDS, Tom Fawcett
Data Science and the Imposter Syndrome - Sep 15, 2017.
You are not the only one who wonders how much longer they can get away with pretending to be a data scientist. You are not the only one who has nightmares about being laughed out of your next interview.
Bias, Data Science, Data Scientist
- Python vs R – Who Is Really Ahead in Data Science, Machine Learning? - Sep 12, 2017.
We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.
Data Science, Google Trends, Jobs, Kaggle, Machine Learning, Python, Python vs R, R
- Putting the “Science” Back in Data Science - Sep 6, 2017.
The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.
Business, Data Science, Machine Learning, Process, Rubens Zimbres
- Search Millions of Documents for Thousands of Keywords in a Flash - Sep 1, 2017.
We present a python library called FlashText that can search or replace keywords / synonyms in documents in O(n) – linear time.
Algorithms, Data Science, GitHub, NLP, Python, Search, Search Engine, Text Mining
- 277 Data Science Key Terms, Explained - Sep 1, 2017.
This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.
Data Science, Explained, Key Terms
- Vital Statistics You Never Learned… Because They’re Never Taught - Aug 29, 2017.
Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.
Bayesian, Data Science, Machine Learning, Statistics
42 Steps to Mastering Data Science - Aug 25, 2017.
This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.
Data Preparation, Data Science, Deep Learning, Machine Learning, NoSQL, Python, SQL
- Data Science Primer: Basic Concepts for Beginners - Aug 11, 2017.
This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types of patterns we can mine from data.
Bias, Data Mining, Data Science, Distribution, Ensemble Methods, Statistics
