Data Science (1355)

• The Rise of User-Generated Data Labeling - Dec 4, 2019.
  
  Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in!

  Data Labeling, Data Preparation, Data Science, User Generated Content

• Data Science Curriculum Roadmap - Dec 3, 2019.
  
  What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.

  Data Science, Data Science Education

• A Non-Technical Reading List for Data Science - Dec 2, 2019.
  
  The world still cannot be reduced to numbers on a page because human beings are still the ones making all the decisions. So, the best data scientists understand the numbers and the people. Check out these great data science books that will make you a better data scientist without delving into the technical details.

  Books, Data Science, Future, Review, Society

• Top 7 Data Science Use Cases in Trust and Security - Dec 2, 2019.
  
  What are trust and safety? What is the role of trust and security in the modern world? Read this overview of 7 data science application use cases in the realm of trust and security.

  AI, Data Science, Security, Trust, Use Cases

• Open Source Projects by Google, Uber and Facebook for Data Science and AI - Nov 28, 2019.
  
  Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.

  Advice, AI, Data Science, Data Scientist, Data Visualization, Deep Learning, Facebook, Google, Open Source, Python, Uber

• The Future of Careers in Data Science & Analysis - Nov 27, 2019.
  
  As the fields of data science and analysis continue to expand, the next crop of bright minds is always needed. Learn more about the nuances of these jobs and find where you can fit in for a rewarding and interesting career.

  Careers, Data Analyst, Data Science, Data Scientist

• Top 8 Data Science Use Cases in Marketing - Nov 25, 2019.
  
  In this article, we want to highlight some key data science use cases in marketing. Let us concentrate on several instances that present particular interest and managed to prove their efficiency in the course of time.

  Data Science, Marketing, Use Cases

• Reproducibility, Replicability, and Data Science - Nov 19, 2019.
  
  As cornerstones of scientific processes, reproducibility and replicability ensure results can be verified and trusted. These two concepts are also crucial in data science, and as a data scientist, you must follow the same rigor and standards in your projects.

  Best Practices, Data Science, Overfitting, Reproducibility, Trust, Validation

• What is Data Science? - Nov 8, 2019.
  
  Data Science is pitched as a modern and exciting job offering high satisfaction. Does its reality really live up to the hype? Here, we show what it's really like to work as a Data Scientist.

  Career, Data Science, Data Science Skills, Explained

• Set Operations Applied to Pandas DataFrames - Nov 7, 2019.
  
  In this tutorial, we show how to apply mathematical set operations (union, intersection, and difference) to Pandas DataFrames with the goal of easing the task of comparing the rows of two datasets.

  Data Preparation, Data Science, Pandas, Python

• Data Sources 101 - Oct 28, 2019.
  
  Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from?

  Big Data, Data Science, Datasets, Unstructured data

• How to Write Web Apps Using Simple Python for Data Scientists - Oct 22, 2019.
  
  Convert your Data Science Projects into cool apps easily without knowing any web frameworks.

  Apps, Data Science, Data Scientist, Python

• Top 7 Things I Learned in my Data Science Masters - Oct 15, 2019.
  
  Even though I’m still in my studies, here’s a list of the most important things I’ve learned (as of yet).

  Data Science, Data Science Education, Tips

• The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization - Oct 7, 2019.
  
  As a data scientist, your most important skill is creating meaningful visualizations to disseminate knowledge and impact your organization or client. These seven principals will guide you toward developing charts with clarity, as exemplified with data from a recent KDnuggets poll.

  Data Science, Data Science Skills, Data Visualization, Excel, Java, Python, Skills, TensorFlow

• The Last SQL Guide for Data Analysis You’ll Ever Need - Oct 4, 2019.
  
  This is it: the last SQL guide for data analysis you'll ever need! OK, maybe it’s actually the first. But it’ll give you a solid head start.

  Cheat Sheet, Data Analysis, Data Science, SQL

• Data Preparation for Machine learning 101: Why it’s important and how to do it - Oct 2, 2019.
  
  As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.

  Data Preparation, Data Science, Machine Learning

• Why data analysts should choose stories over statistics - Sep 26, 2019.
  
  Join the Crunch Data Conference in Budapest, Oct 16-18, with stellar speakers from companies like Facebook, Netflix and LinkedIn. Use the discount code ‘KDNuggets’ to save $100 off your conference ticket.

  Budapest, Career Advice, Crunch Conference, Data Analytics, Data Science, Hungary, Storytelling

• The thin line between data science and data engineering - Sep 25, 2019.
  
  Today, as companies have finally come to understand the value that data science can bring, more and more emphasis is being placed on the implementation of data science in production systems. And as these implementations have required models that can perform on larger and larger datasets in real-time, an awful lot of data science problems have become engineering problems.

  Data Engineering, Data Science, Podcast

• Applying Data Science to Cybersecurity Network Attacks & Events - Sep 19, 2019.
  
  Check out this detailed tutorial on applying data science to the cybersecurity domain, written by an individual with backgrounds in both fields.

  Cybersecurity, Data Science, Machine Learning, Python, Security

• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python - Sep 19, 2019.
  
  “I want to learn machine learning and artificial intelligence, where do I start?” Here.

  Beginners, Data Science, Machine Learning, Python

• Version Control for Data Science: Tracking Machine Learning Models and Datasets - Sep 13, 2019.
  
  I am a Git god, why do I need another version control system for Machine Learning Projects?

  Data Science, Datasets, Machine Learning, Modeling, Version Control

• There is No Free Lunch in Data Science - Sep 12, 2019.
  
  There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.

  Algorithms, Data Science, Machine Learning, Optimization

• The 5 Graph Algorithms That Data Scientists Should Know - Sep 10, 2019.
  
  In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.

  Algorithms, Data Science, Data Scientist, Graph, Python

• 10 Great Python Resources for Aspiring Data Scientists - Sep 9, 2019.
  
  This is a collection of 10 interesting resources in the form of articles and tutorials for the aspiring data scientist new to Python, meant to provide both insight and practical instruction when starting on your journey.

  Data Science, Data Scientist, Programming, Python

• I wasn’t getting hired as a Data Scientist. So I sought data on who is. - Sep 6, 2019.
  
  Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before.

  Career, Career Advice, Data Science, Data Science Skills, Data Scientist

• Automate your Python Scripts with Task Scheduler: Windows Task Scheduler to Scrape Alternative Data - Sep 3, 2019.
  
  In this tutorial, you will learn how to run task scheduler to web scrape data from Lazada (eCommerce) website and dump it into SQLite RDBMS Database.

  Data Science, Python, Web Scraping

• Top 10 Data Science Use Cases in Energy and Utilities - Sep 2, 2019.
  
  In this article, we will consider the most vivid data science use cases in the industry of energy and utilities.

  Data Science, Energy, Use Cases, Utilities

• Types of Bias in Machine Learning - Aug 29, 2019.
  
  The sample data used for training has to be as close a representation of the real scenario as possible. There are many factors that can bias a sample from the beginning and those reasons differ from each domain (i.e. business, security, medical, education etc.)

  Bias, Data Science, Data Scientist, Machine Learning

• Top Handy SQL Features for Data Scientists - Aug 23, 2019.
  
  Whenever we hear "data," the first thing that comes to mind is SQL! SQL comes with easy and quick to learn features to organize and retrieve data, as well as perform actions on it in order to gain useful insights.

  Data Science, Data Scientist, SQL

• Automate Stacking In Python: How to Boost Your Performance While Saving Time - Aug 21, 2019.
  
  Utilizing stacking (stacked generalizations) is a very hot topic when it comes to pushing your machine learning algorithm to new heights. For instance, most if not all winning Kaggle submissions nowadays make use of some form of stacking or a variation of it.

  Algorithms, Big Data, Data Science, Python

• Is Kaggle Learn a “Faster Data Science Education?” - Aug 20, 2019.
  
  Kaggle Learn is "Faster Data Science Education," featuring micro-courses covering an array of data skills for immediate application. Courses may be made with newcomers in mind, but the platform and its content is proving useful as a review for more seasoned practitioners as well.

  Data Science, Data Science Education, Kaggle, Online Education

• An Overview of Python’s Datatable package - Aug 20, 2019.
  
  Modern machine learning applications need to process a humongous amount of data and generate multiple features. Python’s datatable module was created to address this issue. It is a toolkit for performing big data (up to 100GB) operations on a single-node machine, at the maximum possible speed.

  Big Data, Data Science, Python

• Crafting an Elevator Pitch for your Data Science Startup - Aug 19, 2019.
  
  If you are launching a data science startup, these tips will give you a head start as you seek capital for seed funding or your next level of growth.

  Data Science, Startup, Startups, VC

• Manual Coding or Automated Data Integration – What’s the Best Way to Integrate Your Enterprise Data? - Aug 19, 2019.
  
  What’s the best way to execute your data integration tasks: writing manual code or using ETL tool? Find out the approach that best fits your organization’s needs and the factors that influence it.

  Advice, Data Integration, Data Management, Data Science, Data Science Platform, ETL

• Command Line Basics Every Data Scientist Should Know - Aug 15, 2019.
  
  Check out this introductory guide to completing simple tasks with the command line.

  Data Science, Data Science Tools

• Statistical Modelling vs Machine Learning - Aug 14, 2019.
  
  At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

  Advice, Data Science, Machine Learning, Statistics

• Learn how to use PySpark in under 5 minutes (Installation + Tutorial) - Aug 13, 2019.
  
  Apache Spark is one of the hottest and largest open source project in data processing framework with rich high-level APIs for the programming languages like Scala, Python, Java and R. It realizes the potential of bringing together both Big Data and machine learning.

  Apache Spark, Big Data, Data Science, Python

• Five Command Line Tools for Data Science - Jul 31, 2019.
  
  You can do more data science than you think from the terminal.

  Data Exploration, Data Science, Data Science Tools

• Ten more random useful things in R you may not know about - Jul 31, 2019.
  
  I had a feeling that R has developed as a language to such a degree that many of us are using it now in completely different ways. This means that there are likely to be numerous tricks, packages, functions, etc that each of us use, but that others are completely unaware of, and would find useful if they knew about them.

  Advice, Analytics, Data Science, R

• P-values Explained By Data Scientist - Jul 30, 2019.
  
  This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.

  Data Science, Data Scientist, Hypothesis Testing, P-value, Statistics

• Here’s how you can accelerate your Data Science on GPU - Jul 30, 2019.
  
  Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time.

  Big Data, Data Science, DBSCAN, Deep Learning, GPU, NVIDIA, Python

• Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning - Jul 29, 2019.
  
  Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations.

  AI, Analytics, Data Science, Machine Learning, Podcast

• Top 13 Skills To Become a Rockstar Data Scientist - Jul 26, 2019.
  
  Education, coding, SQL, big data platforms, storytelling and more. These are the 13 skills you need to master to become a rockstar data scientist.

  Career Advice, Data Science, Data Science Skills, Data Scientist, Skills

• Fantastic Four of Data Science Project Preparation - Jul 26, 2019.
  
  This article takes a closer look at the four fantastic things we should keep in mind when approaching every new data science project.

  Comic, Data Exploration, Data Preparation, Data Science, Domain Knowledge

• Is SQL needed to be a data scientist? - Jul 25, 2019.
  
  As long as there is ‘data’ in data scientist, Structured Query Language (or see-quel as we call it) will remain an important part of it. In this blog, let us explore data science and its relationship with SQL.

  Data Science, Relational Databases, SQL

• Easy, One-Click Jupyter Notebooks - Jul 24, 2019.
  
  All of the setup for software, networking, security, and libraries is automatically taken care of by the Saturn Cloud system. Data Scientists can then focus on the actual Data Science and not the tedious infrastructure work that falls around it

  Big Data, Cloud, Data Science, Data Scientist, DevOps, Jupyter, Python, Saturn Cloud

• Is Bias in Machine Learning all Bad? - Jul 23, 2019.
  
  We have been taught over our years of predictive model building that bias will harm our model. Bias control needs to be in the hands of someone who can differentiate between the right kind and wrong kind of bias.

  Bias, Data Science, Machine Learning

• Things I Have Learned About Data Science - Jul 16, 2019.
  
  Read this collection of 38 things the author has learned along his travels, and has opted to share for the benefit of the reader.

  Data Science, Tips

• Secrets to a Successful Data Science Interview - Jul 15, 2019.
  
  Are you puzzled as to what to prepare for data science interviews? That you are reading this document is a reflection of your seriousness in being a successful data scientist.

  Career Advice, Data Science, Interview

• The Hackathon Guide for Aspiring Data Scientists - Jul 15, 2019.
  
  This article is an overview of how to prepare for a hackathon as an aspiring data scientist, highlighting the 4 reasons why you should take part in one, along with a series of tips for participation.

  Data Science, Flask, Hackathon, Mobile, Product

• Top 10 Data Science Leaders You Should Follow - Jul 12, 2019.
  
  If you’re in the data science field, I strongly encourage you to follow these giants— which I’ll list down in the section below — and be a part of our data science community to learn from the best and share your experience and knowledge.

  Data Science, Experts, Influencers, Social Media

• How to Showcase the Impact of Your Data Science Work - Jul 10, 2019.
  
  You're a Data Scientist -- or preparing to land your first job -- and communicating your work to others, especially employers, so they understand your impact is essential. These five tips will help you help others appreciate your data science.

  Advice, Data Science, Industry

• What’s wrong with the approach to Data Science? - Jul 10, 2019.
  
  The job ‘Data Scientist’ has been around for decades, it was just not called “Data Scientist”. Statisticians have used their knowledge and skills using machine learning techniques such as Logistic Regression and Random Forest for prediction and insights for longer than people actually realize.

  Advice, Data Science, Data Science Education, Data Scientist

• How Data Science Is Used Within the Film Industry - Jul 5, 2019.
  
  As Data Science is becoming pervasive across so many industries, Hollywood is certainly not being left behind. Learn about how Big Data, analytics, and AI are now core drivers of the movies we watch and how we watch them.

  Data Science, Industry, Marketing, Movies, Predictive Analytics, Recommender Systems

• 5 Probability Distributions Every Data Scientist Should Know - Jul 4, 2019.
  
  Having an understanding of probability distributions should be a priority for data scientists. Make sure you know what you should by reviewing this post on the subject.

  Data Science, Data Scientist, Distribution, Normal Distribution, Probability

• How do you check the quality of your regression model in Python? - Jul 2, 2019.
  
  Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.

  Data Science, Multicollinearity, Python, Regression, Statistics

• The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph - Jun 25, 2019.
  
  Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.

  Advice, Data Science, Data Scientist, Graphs, Machine Learning

• 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition - Jun 24, 2019.
  
  Interested in mastering data preparation with Python? Follow these 7 steps which cover the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.

  7 Steps, Data Preparation, Data Preprocessing, Data Science, Data Wrangling, Machine Learning, Pandas, Python

• Data Literacy: Using the Socratic Method - Jun 20, 2019.
  
  How can organizations and individuals promote Data Literacy? Data literacy is all about critical thinking, so the time-tested method of Socratic questioning can stimulate high-level engagement with data.

  Advice, Data Science

• Ten random useful things in R that you might not know about - Jun 20, 2019.
  
  Because the R ecosystem is so rich and constantly growing, people can often miss out on knowing about something that can really help them in a task that they have to complete

  Advice, Analytics, Data Science, R

• Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS - Jun 17, 2019.
  
  Data science jobs continue to grow in 2019, and this report shares the change and spread of jobs by software over recent years.

  Data Science, indeed, Jobs, Python, R, SAS, TensorFlow

• How to Learn Python for Data Science the Right Way - Jun 14, 2019.
  
  The biggest mistake you can make while learning Python for data science is to learn Python programming from courses meant for programmers. Avoid this mistake, and learn Python the right way by following this approach.

  Advice, Data Science, Jupyter, Matplotlib, Pandas, Python, scikit-learn, StatsModels

• 5 Useful Statistics Data Scientists Need to Know - Jun 14, 2019.
  
  A data scientist should know how to effectively use statistics to gain insights from data. Here are five useful and practical statistical concepts that every data scientist must know.

  Data Science, Data Scientist, Statistics

• If you’re a developer transitioning into data science, here are your best resources - Jun 11, 2019.
  
  This article will provide a background on the data scientist role and why your background might be a good fit for data science, plus tangible stepwise actions that you, as a developer, can take to ramp up on data science.

  Advice, Career, Data Science, Data Scientist

• The Infinity Stones of Data Science - Jun 10, 2019.
  
  Do you love data science 3000? Don't want to be embarrassed in front of the other analytics wizards? Aspire to be one of Earth's mightiest heroes, like Kevin Bacon? Help make data science a snap with these simple insights.

  Comic, Data Science

• A Step-by-Step Guide to Transitioning your Career to Data Science – Part 2 - Jun 7, 2019.
  
  How do you identify the technical skills a hiring manager is looking for? How do you build a data science project that draws the attention of a hiring manager?

  Career Advice, Data Science, Skills, SQL, Tableau

• Top 10 Statistics Mistakes Made by Data Scientists - Jun 7, 2019.
  
  The following are some of the most common statistics mistakes made by data scientists. Check this list often to make sure you are not making any of these while applying statistics to data science.

  Data Science, Data Scientist, GitHub, Mistakes, Statistics

• Jupyter Notebooks: Data Science Reporting - Jun 6, 2019.
  
  Jupyter does bring us some benefits of being able to organize code but many of us still find ourselves with messy and unnecessary code chunks. Here are some ways including a NEW EXTENSION that anyone can use to begin organizing your code on your notebooks.

  Anaconda, Data Science, Jupyter

• Mongo DB Basics - Jun 5, 2019.
  
  Mongo DB is a document oriented NO SQL database unlike HBASE which has a wide column store. The advantage of Document oriented over relation type is the columns can be changed as an when required for each case as opposed to the same column name for all the rows.

  Big Data, Data Engineering, Data Science, MongoDB

• The Whole Data Science World in Your Hands - Jun 5, 2019.
  
  Testing MatrixDS capabilities on different languages and tools: Python, R and Julia. If you work with data you have to check this out.

  Data Science, Data Scientist, Julia, Jupyter, MatrixDS, Python, R

• A Step-by-Step Guide to Transitioning your Career to Data Science – Part 1 - May 31, 2019.
  
  If you are looking to transition your career to data science, don't immediately start learning Python or R. Instead, leverage the domain expertise you have accumulated over the years. Here's a foolproof guide on how to do that.

  Career Advice, Data Science, Skills

• Animations with Matplotlib - May 30, 2019.
  
  Animations make even more sense when depicting time series data like stock prices over the years, climate change over the past decade, seasonalities and trends since we can then see how a particular parameter behaves with time.

  Data Science, Data Visualization, Matplotlib, Python

• Becoming a Level 3.0 Data Scientist - May 29, 2019.
  
  Want to be a Junior, Senior, or Principal Data Scientists? Find out what you need to do to navigate the Data Science Career Game.

  Advice, Career, Data Science, Data Science Education, Data Scientist

• Choosing Between Model Candidates - May 29, 2019.
  
  Models are useful because they allow us to generalize from one situation to another. When we use a model, we’re working under the assumption that there is some underlying pattern we want to measure, but it has some error on top of it.

  Data Science, Modeling, Regression, Time Series

• Your Guide to Natural Language Processing (NLP) - May 23, 2019.
  
  This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more.

  AI, Data Science, Machine Learning, Natural Language Processing, NLP, Tokenization

• The Data Fabric for Machine Learning – Part 1 - May 21, 2019.
  
  How the new advances in semantics and the data fabric can help us be better at Machine Learning

  Advice, Data Science, Data Scientist, Machine Learning

• PyCharm for Data Scientists - May 17, 2019.
  
  This article is a discussion of some of PyCharm's features, and a comparison with Spyder, an another popular IDE for Python. Read on to find the benefits and drawbacks of PyCharm, and an outline of when to prefer it to Spyder and vice versa.

  Data Science, Data Scientist, Programming, PyCharm, Python

• 7 Steps to Mastering SQL for Data Science — 2019 Edition - May 17, 2019.
  
  Follow these updated 7 steps to go from SQL data science newbie to practitioner in a hurry. We consider only the necessary concepts and skills, and provide quality resources for each.

  7 Steps, Data Science, Database, Relational Databases, SQL

• Machine Learning in Agriculture: Applications and Techniques - May 14, 2019.
  
  Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.

  Agriculture, AI, Data Science, Machine Learning, Sciforce

• What’s Going to Happen this Year in the Data World - May 14, 2019.
  
  "If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science." Henri Poncairé.

  Advice, AI, Big Data, Data Science, Deep Learning

• Data Science Poem - May 11, 2019.
  
  A poem about Data Science.

  Data Science, Humor

• 5 Things to Review Before Accepting That Data Scientist Job Offer - May 10, 2019.
  
  Before you get too excited and sign the papers for that new data scientist job, and solidify your role as a new hire, make sure you look over these 5 things first.

  Career, Career Advice, Data Science, Data Scientist

• [White Paper] Unlocking the Power of Data Science & Machine Learning with Python - May 8, 2019.
  
  This guide from ActiveState provides an executive overview of how you can implement Python for your team’s data science and machine learning initiatives.

  ActiveState, Data Science, Machine Learning, Python, White Paper

• 2019 KDnuggets Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? - May 7, 2019.
  
  Vote in KDnuggets 20th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will publish the anon data, results, and trends here.

  Big Data, Data Mining Software, Data Science, Deep Learning, Machine Learning, Poll, Programming Languages

• Best US/Canada Masters in Analytics, Business Analytics, Data Science - May 7, 2019.
  
  In the final part of this series, we provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across the US and Canada.

  Data Science, Education, Master of Science, MS in Analytics, MS in Business Analytics, MS in Data Science

• 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.

  Pages: 1 2

  Algorithms, Data Science, Machine Learning, Naive Bayes, Python, scikit-learn, Statistics

• Data Science vs. Decision Science - May 7, 2019.
  
  Data science and decision science are related but still separate fields, so at some points, it might be hard to compare them directly. We attempted to show our vision of the commonalities, differences, and specific features of data science and decision science.

  Advice, Data Science, Data Scientist

• The Third Wave Data Scientist - May 6, 2019.
  
  An extensive look at what skills are needed to make up the portfolio of the third wave of data scientists.

  Advice, Career, Data Science, Data Science Skills, Venn Diagram

• The 3 Biggest Mistakes on Learning Data Science - May 6, 2019.
  
  Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.

  Advice, Data Science, Data Scientist

• Which Deep Learning Framework is Growing Fastest? - May 1, 2019.
  
  In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity. TensorFlow was the champion of deep learning frameworks and PyTorch was the youngest framework. How has the landscape changed?

  Data Science, Data Scientist, Deep Learning, fast.ai, Keras, Python, PyTorch, TensorFlow

• 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.

  Data Science, Domino, Hedge fund, Investment, New York City, NY

• 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.

  Career, Data Science, Interview Questions, Kaiser Fung

• 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.

  Algorithms, Clustering, Data Science, Deep Learning, Machine Learning, Poll, Regression

• 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.

  Data Science, Pandas, Python

• 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.

  Data Science, Data Science Skills, Kaiser Fung, Self-Driving Car

• 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.”

  Career Advice, Data Science, Dataquest, Portfolio

• 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.

  Data Analytics, Data Science, Education, Master of Science, MS in Analytics, MS in Data Science, Online Education

• 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

  Advice, Career, Data Science, Data Scientist, UK

• 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.

  Advice, Career, Data Science, Data Science Education, Data Scientist, Online Education

• 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.

  Data Science, Data Scientist, Gradient Descent, Optimization, Prescriptive Analytics

• 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.

  Pages: 1 2

  Data Analytics, Data Science, Education, Europe, Master of Science, MS in Analytics, MS in Data Science

• 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.

  Apache Spark, Data Operations, Data Science, Python, Workflow

• 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.

  Apache Spark, Data Science, Python, Workflow

• 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.

  Bootcamp, Data Science, Pivigo

• 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.

  Algorithms, Data Science, Machine Learning, Poll

• 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.

  AI, Data Science, Deep Learning, Keras, Machine Learning, NLP, Reinforcement Learning, TensorFlow, U. of Washington, UC Berkeley, Unsupervised Learning

• 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.

  Books, Data Science, Data Science Education, MOOC, Podcast, Programming Languages, Youtube

• 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.

  Data Science, Gaming, Use Cases

• 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.

  Data Science, Data Scientist, Mistakes, Programming

• How to Capture Data to Make Business Impact - Mar 21, 2019.
  
  We take a look at the formula for calculating the efficiency of a data capturing method, before going onto explain the concept of Smart Data.

  Analytics, Big Data, Data Science, ROI, Smart Data

• Top 8 Data Science Use Cases in Manufacturing - Mar 21, 2019.
  
  Data science is said to change the manufacturing industry dramatically. Let's take under consideration several data science use cases in manufacturing that have already become common and brought benefits to the manufacturers.

  Data Science, Manufacturing, Use Cases

• Top R Packages for Data Cleaning - Mar 15, 2019.
  
  Data cleaning is one of the most important and time consuming task for data scientists. Here are the top R packages for data cleaning.

  Data Cleaning, Data Preparation, Data Science, Machine Learning, R

• The Pareto Principle for Data Scientists - Mar 11, 2019.
  
  In this article, I’ll share a few ways in which we, as data scientists, can use the power of the Pareto Principle to guide our day-to-day activities.

  Data Science, Data Scientist

• 19 Inspiring Women in AI, Big Data, Data Science, Machine Learning - Mar 8, 2019.
  
  For the 2019 international women's day, we profile a new set of 19 inspiring women who lead the field in AI, Big Data, Data Science, and Machine Learning fields.

  AI, Data Science, Machine Learning, Women

• Another 10 Free Must-Read Books for Machine Learning and Data Science - Mar 6, 2019.
  
  Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.

  Books, Data Science, ebook, Free ebook, Machine Learning

• Top 7 Data Science Use Cases in Travel - Feb 28, 2019.
  
  To satisfy all the needs of the growing number of consumers and process enormous data chunks, data science algorithms are vital. Let’s consider several of widespread and efficient data science use cases in the travel industry.

  Data Science, Travel, Use Cases

• 4 Reasons Why Your Machine Learning Code is Probably Bad - Feb 26, 2019.
  
  Your current ML workflow probably chains together several functions executed linearly. Instead of linearly chaining functions, data science code is better written as a set of tasks with dependencies between them. That is your data science workflow should be a DAG.

  Data Science, Machine Learning, Programming, Python, Workflow

• Asking Great Questions as a Data Scientist - Feb 25, 2019.
  
  We outline the importance of asking yourself the questions you need to ask to effectively produce something that the business wants. Once you start asking questions, it’ll become second nature and you’ll immediately see the value and find yourself asking even more questions as you gain more experience.

  Data Science, Data Scientist

• 6 Books About Open Data Every Data Scientist Should Read - Feb 20, 2019.
  
  Check out this collection of six books which tackle the hard skills required to make sense of the changing field known as open data and muse on the ethical implications of a digitally connected world.

  Books, Data Science, Open Data

• Python Data Science for Beginners - Feb 20, 2019.
  
  Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.

  Beginners, Data Science, Matplotlib, numpy, Pandas, Python, scikit-learn, SciPy

• Top 10 Data Science Use Cases in Telecom - Feb 14, 2019.
  
  In this article, we attempt to present the most relevant and efficient data science use cases in the field of telecommunication.

  Data Science, Telecom, Use Cases

• Data Science For Our Mental Development - Feb 11, 2019.
  
  In this blog, I aim to generalize how AI can help us with mental development in the future as well as discuss some of the present-day solutions.

  Data Science, Development, Emotion

• Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry. - Feb 7, 2019.
  
  The following is a method I developed, which is based on my personal experience managing a data-science-research team and was tested with multiple projects. In the next sections, I’ll review the different types of research from a time point-of-view, compare development and research workflow approaches and finally suggest my work methodology.

  Agile, Data Science, Development, Project

• How I used NLP (Spacy) to screen Data Science Resumes - Feb 6, 2019.
  
  A real life example of when using NLP can help filter down a list of candidates for a job opening, with full source code and methodology.

  Data Science, Hiring, NLP, Resume

• From Good to Great Data Science, Part 1: Correlations and Confidence - Feb 5, 2019.
  
  With the aid of some hospital data, part one describes how just a little inexperience in statistics could result in two common mistakes.

  Correlation, Data Science, Python, Statistics

• The Essential Data Science Venn Diagram - Feb 4, 2019.
  
  A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.

  Analytics, Data Science, Machine Learning, Statistics, Venn Diagram

• What Is Dimension Reduction In Data Science? - Jan 31, 2019.
  
  An extensive introduction into Dimension Reduction, including a look at some of the different techniques, linear discriminant analysis, principal component analysis, kernel principal component analysis, and more.

  Data Science, Dimensionality Reduction, Linear Discriminant Analysis, Principal component analysis

• The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them - Jan 24, 2019.
  
  Because big data touches almost every industry across the board, those who aren’t already working in data and analytics will soon be utilizing the technology for its undeniable business benefits. Whichever way you slice it, the future of work is through data.

  Business Analyst, Data Engineer, Data Science, Data Scientist, Hiring, Jobs

• Data Science Project Flow for Startups - Jan 24, 2019.
  
  The aim of this post, then, is to present the characteristic project flow that I have identified in the working process of both my colleagues and myself in recent years. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.

  Data Science, Startups, Workflow

• How AI and Data Science is Changing the Utilities Industry - Jan 22, 2019.
  
  Together, artificial intelligence (AI) and data science are causing positive developments for the utilities providers that choose to investigate these things. Here are some examples of technology at work.

  AI, Data Science, Industry, Utilities