- You’re Fired: How to develop and manage a happy data science team - Jan 27, 2020.
I want to share a solution called Insight-Driven Development (IDD), a few examples of it, and five steps to adopting it. IDD aims to create a high performing, engaged, and happy Data Science teams that embrace non-ML work as much as the fun ML stuff.
- What Do Data Scientists in Europe Do & How Much Are They Worth? - Jan 23, 2020.
Interested in knowing what a data scientist is worth in Europe, and what one does? Read this overview of a recent survey on the topic and gain some insight into the European data science professional job market.
- The Data Science Interview Study Guide - Jan 22, 2020.
Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job.
- Top 9 Mobile Apps for Learning and Practicing Data Science - Jan 17, 2020.
This article will tell you about the top 9 mobile apps that help the user in learning and practicing data science and hence is improving their productivity.
- Handling Trees in Data Science Algorithmic Interview - Jan 16, 2020.
This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview.
- KDnuggets™ News 20:n02, Jan 15: Top 5 Must-have Data Science Skills; Learn Machine Learning with THIS Book - Jan 15, 2020.
This week: learn the 5 must-have data science skills for the new year; find out which book is THE book to get started learning machine learning; pick up some Python tips and tricks; learn SQL, but learn it the hard way; and find an introductory guide to learning common NLP techniques.
- Graph Machine Learning Meets UX: An uncharted love affair - Jan 13, 2020.
When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX.
- 7 Steps to a Job-winning Data Science Resume - Jan 10, 2020.
A resume plays a key role in bagging that dream data science job. We break down the nuances of a job-winning data science resume so that you can go ahead and transform your own resume.
- Fast Track Your Data Science Career - Jan 9, 2020.
Earn a Master of Professional Studies in Data Analytics online through Penn State World Campus – and you can add in-demand skills to your wheelhouse while you continue to work.
- 5 Hands-on Skills Every Data Scientist Needs in 2020 – Coming to ODSC East - Jan 7, 2020.
Here are our top five hands-on training focus areas that every data scientist should know and that we’re paying extra attention to at ODSC East 2020 this April 13-17 in Boston.
- 7 Resources to Becoming a Data Engineer - Jan 7, 2020.
An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.
- Why Python is One of the Most Preferred Languages for Data Science? - Jan 3, 2020.
Why do most data scientists love Python? Learn more about how so many well-developed Python packages can help you accomplish your crucial data science tasks.
- How HR Is Using Data Science and Analytics to Close the Gender Gap - Jan 3, 2020.
The gender gap can extend to the lack of equal representation in certain industries or career paths, and there's an extraordinarily long way to go before people will be on equal footing in the labor market. Human resources professionals can rely on data analytics to make progress.
- What is the most important question for Data Science (and Digital Transformation) - Dec 31, 2019.
With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.
- How To “Ultralearn” Data Science: summary, for those in a hurry - Dec 30, 2019.
For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.
- How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4 - Dec 27, 2019.
In this fourth and final part of the ultralearning data science series, it's time to take the final steps toward developing a deep understanding of the fundamentals and learning how to experiment -- the two aspects that are the ultimate keys to ultralearning.
- What is a Data Scientist Worth? - Dec 23, 2019.
What is the Salary of a Data Scientist in 2019? Let's have a look at some data to see how we can answer that question.
- How To “Ultralearn” Data Science: optimization learning, Part 3 - Dec 20, 2019.
This third part in a series about how to "ultralearn" data science will guide you through how to optimize your learning through five valuable techniques.
- The Most In Demand Tech Skills for Data Scientists - Dec 20, 2019.
By the end of this article you’ll know which technologies are becoming more popular with employers and which are becoming less popular.
- Alternative Cloud Hosted Data Science Environments - Dec 19, 2019.
Over the years new alternative providers have risen to provided a solitary data science environment hosted on the cloud for data scientist to analyze, host and share their work.
- How To “Ultralearn” Data Science: removing distractions and finding focus, Part 2 - Dec 17, 2019.
This second part in a series about how to "ultralearn" data science will guide you through several techniques to remove those distractions -- because your focus needs more focus.
- How To “Ultralearn” Data Science, Part 1 - Dec 13, 2019.
What is "ultralearning" and how can you follow the strategy to become an expert of data science? Start with this first part in a series that will guide you through this self-motivated methodology to help you efficiently master difficult skills.
- Plotnine: Python Alternative to ggplot2 - Dec 12, 2019.
Python's plotting libraries such as matplotlib and seaborn does allow the user to create elegant graphics as well, but lack of a standardized syntax for implementing the grammar of graphics compared to the simple, readable and layering approach of ggplot2 in R makes it more difficult to implement in Python.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020 - Dec 11, 2019.
We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.
- The 4 Hottest Trends in Data Science for 2020 - Dec 9, 2019.
The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.
- AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - Dec 9, 2019.
As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.
- 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!
- KDnuggets™ News 19:n46, Dec 4: The Future of Data Science Careers; Which Data Visualization Should I Use? - Dec 4, 2019.
This week: The Future of Careers in Data Science & Analysis; Task-based effectiveness of basic visualizations; Open Source Projects by Google, Uber and Facebook for Data Science and AI; Getting Started with Automated Text Summarization; A Non-Technical Reading List for Data Science; and much more!
- 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.
- 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.
- 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.
- 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.
- Cartoon: Thanksgiving, Big Data, and Turkey Data Science… - Nov 28, 2019.
A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.
- 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.
- Would you buy insights from this guy? (How to assess and manage a Data Science vendor) - Nov 25, 2019.
With all the hype from data science vendors selling "actionable insights" to boost your company's bottom line, selecting your analytics partner should proceed through the same, careful process as any traditional business endeavor. Follow these questions and best practices to ensure you manage accordingly.
- 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.
- 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.
- Data Science for Managers: Programming Languages - Nov 19, 2019.
In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.
- KDnuggets™ News 19:n43, Nov 13: Dynamic Reports in Python and R; Creating NLP Vocabularies; What is Data Science? - Nov 13, 2019.
On KDnuggets this week: Orchestrating Dynamic Reports in Python and R with Rmd Files; How to Create a Vocabulary for NLP Tasks in Python; What is Data Science?; The Complete Data Science LinkedIn Profile Guide; Set Operations Applied to Pandas DataFrames; and much, much more.
- 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.
- 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 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?
- DataTech20 Seeking Speaker Submissions (16 March 2020, Glasgow) - Oct 28, 2019.
DataTech is a one-day conference on 16 Mar 2020, at the Technology and Innovation Centre in Glasgow, focusing on key topics in data science, and welcoming members of industry, academia, and the public sector alike. DataTech provides a forum for these different communities to meet, share knowledge and expertise, and forge new collaborations. We are currently welcoming workshop, talk and poster proposals for the DataTech20 conference.
- Bye Data Scientists, Hello AI? Not Likely! - Oct 22, 2019.
AI is becoming more mainstream. The fact that computers/robots will learn after being built and will surpass a human's intelligence is terrifying.
- 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.
- How to Get the Most out of ODSC West 2019 - Oct 18, 2019.
ODSC West comes to San Francisco on Oct 29 - Nov 1. With over 300 hours of content, 200+ speakers, and thousands of attendees, there is certainly a lot to see, learn, and do at the conference. Register by Friday for 10% off your pass.
- KDnuggets™ News 19:n39, Oct 16: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI - Oct 16, 2019.
This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.
- Using DC/OS to Accelerate Data Science in the Enterprise - Oct 15, 2019.
Follow this step-by-step tutorial using Tensorflow to setup a DC/OS Data Science Engine as a PaaS for enabling distributed multi-node, multi-GPU model training.
- 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).
- An ODSC West Guide to the Most Important Topics in Data Science Right Now - Oct 10, 2019.
In this article, we’ll outline just a few of the most important topics in data science that our speakers will be presenting on at ODSC West Oct 29 - Nov 1 in San Francisco.
- Data Science is Boring (Part 2) - Oct 9, 2019.
Why I love boring ML problems and how I think about them.
- 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.
- 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.
- 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.
- KDnuggets™ News 19:n37, Oct 2: The Future of Analytics & Data Science! Starting NLP with spaCy & Python - Oct 2, 2019.
This week, find out what the future of analytics and data science holds; get an introduction to spaCy for natural language processing; find out how to use time series analysis for baseball; get to know your data; read 6 bits of advice for data scientists; and much, much more!
- Will Machine Learning End Retail? Data Science Seattle Oct 17, 2019 - Sep 30, 2019.
In advance of the Data Science Salon taking place in Seattle on Oct 17, we asked our speakers to shed some light on how Artificial Intelligence and Machine Learning are impacting one of America’s most disruptive industries. Read for more insight, and then register with KDnuggets exclusive link for 20% off tickets.
- 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.
- 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.
- 5 Famous Deep Learning Courses/Schools of 2019 - Sep 24, 2019.
Deep Learning is/has become the hottest skill in Data Science at the moment. There is a plethora of articles, courses, technologies, influencers and resources that we can leverage to gain the Deep Learning skills.
- 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.
- 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.
- Data Science is Boring (Part 1) - Sep 18, 2019.
Read about how one data scientist copes with his boring days of deploying machine learning.
- KDnuggets™ News 19:n35, Sep 18: Which Data Science Skills are core and which are hot/emerging ones?; There is No Free Lunch in Data Science Features - Sep 18, 2019.
Check the results of KDnuggets' latest poll to find out which data science skills are core and which are hot/emerging ones; why is there no free lunch in data science?; training Scikit-learn 100x faster; poking fun at unsupervised machine learning; exploring the case for ensemble learning. All this and much more this week on KDnuggets.
- Turbo-Charging Data Science with AutoML - Sep 17, 2019.
Join this technical webinar on Oct 3, where Domino Chief Data Scientist Josh Poduska will dive into popular open source and proprietary AutoML tools, and walk through hands-on examples of how to install and use these tools, so you can start using these technologies in your work right away.
- 5 Alternative Data Science Tools - Sep 17, 2019.
What other creative tools for data science beyond Python and R can you use to make an impression? It's not about the tool -- it's about its impact.
- Data Science Symposium 2019, Oct 10-11, Cincinnati - Sep 16, 2019.
The UC Center for Business Analytics will present the Data Science Symposium 2019 on Oct 10 & 11, featuring 3 keynote speakers and 16 tech talks/tutorials on a wide range of data science topics and tools.
- 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?
- 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.
- Classification vs Prediction - Sep 12, 2019.
It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions.
- 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.
- 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.
- 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.
- KDnuggets™ News 19:n33, Sep 4: Data Science Skills Poll; Object-oriented Programming for Data Scientists - Sep 4, 2019.
This week: Object-oriented programming for data scientists; Deep Learning Next Step: Transformers and Attention Mechanism; R Users' Salaries from the 2019 Stackoverflow Survey; Types of Bias in Machine Learning; 4 Tips for Advanced Feature Engineering and Preprocessing; and much more!
- 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.
- 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.
- 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.)
- The secret sauce for growing from a data analyst to a data scientist - Aug 27, 2019.
Despite the increasing demand and appetite for experienced data scientists, the job is ambiguously described most of the times. Also, the delineation between data science and data analytics or engineering is still loosely defined by a lot of hiring managers.
- 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.
- eBook: How to Enhance Privacy in Data Science - Aug 22, 2019.
Check out this eBook, How to Enhance Privacy in Data Science, to equip yourself with the tools to enhance privacy in data science, including transforming data in a manner that protects the privacy, an overview of the challenges and opportunities of privacy-aware analytics, and more.
- 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.
- KDnuggets™ News 19:n31, Aug 21: Become a Marketable Data Scientist; Data Science Command Line Basics; Chatbots with Keras - Aug 21, 2019.
This week's news: Become More Marketable as a Data Scientist; Command Line Basics Every Data Scientist Should Know; Chatbots with Keras!; Understanding Cancer using Machine Learning; Statistical Modelling vs Machine Learning; Is Kaggle Learn a "Faster Data Science Education?"; and much more!
- 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.
- 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.
- 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.
- 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.
- Command Line Basics Every Data Scientist Should Know - Aug 15, 2019.
Check out this introductory guide to completing simple tasks with the command line.
- 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.
- 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.
- Data Science: Scientific Discipline or Business Process? - Aug 8, 2019.
Simply put, data science is an attempt to understand given data using the scientific method. That's why data science is a scientific discipline. You are free (and encouraged!) to apply data science to business use cases, just as you are encouraged to apply it to many other domains.
- KDnuggets™ News 19:n29, Aug 7: What 70% of Data Science Learners Do Wrong; Pytorch Cheat Sheet for Beginners - Aug 7, 2019.
This week on KDnuggets: What 70% of Data Science Learners Do Wrong; Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree; How a simple mix of object-oriented programming can sharpen your deep learning prototype; Can we trust AutoML to go on full autopilot?; Ten more random useful things in R you may not know about; 25 Tricks for Pandas; and much more!
- Getting Started With Data Science - Aug 5, 2019.
Over the past many months, I’ve received hundreds of messages from people asking me how they could get started with Data Science. Therefore, I thought it would be useful to write down a framework for those wanting to get started with Data Science.
- What 70% of Data Science Learners Do Wrong - Aug 2, 2019.
Lessons learned from repeatedly smashing my head with a 2-meter long metal pole for a college engineering course.
- Are We Ready to Partner With Machines?
Data Science Salon Miami, September 10-11 - Jul 31, 2019.
When it comes to AI, there’s plenty of talk of the future of machines. But it’s the people behind AI development who have the insights needed to shape that future. Register now to catch all of our speakers at the Data Science Salon Miami, Sep 10-11, 2019.
- Five Command Line Tools for Data Science - Jul 31, 2019.
You can do more data science than you think from the terminal.
- 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.
- A Data Science Playbook for explainable ML/xAI - Jul 30, 2019.
This technical webinar on Aug 14 discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- How to Share Data Science Secrets Without Sacrificing Security - Jul 24, 2019.
Learn how to incorporate security into your practices without slowing down your project. Read this ActiveState blog post to learn more.
- 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
- 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.
- Apple: Data Science Engineer [Austin, TX] - Jul 19, 2019.
Seeking a customer-focused, passionate and driven Data Science Engineer with experience in building analytic tools and solutions.
- Rethinking Mentoring In Data Science - Jul 19, 2019.
In recent years, I have heard the conversation of “find a mentor, you need a mentor to advance your career.” I received numerous requests from readers around the world to be their mentor. These requests encouraged me to think more closely about mentorship and the general expectations in the data science community.
- Demystifying Data Science: Free Online Conference July 30-31 - Jul 16, 2019.
On Jul 30-31, join 22 speakers giving 16 talks and 6 workshops during Demystifying Data Science, a FREE two-day live online conference hosted by Metis, a leader in data science education.
- 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.
- 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.
- 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.
- 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.
- Do more data science, do less ops with self-service infrastructure & tools - Jul 11, 2019.
Do more data science, do less ops with self-service infrastructure & tools!
- 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.
- 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.
- KDnuggets™ News 19:n25, Jul 10: 5 Probability Distributions for Data Scientists; What the Machine Learning Engineer Job is Really Like - Jul 10, 2019.
This edition of the KDnuggets newsletter is double-sized after taking the holiday week off. Learn about probability distributions every data scientist should know, what the machine learning engineering job is like, making the most money with the least amount of risk, the difference between NLP and NLU, get a take on Nvidia's new data science workstation, and much, much more.
- DSGO19 Announces Speakers and Training Sessions - Jul 8, 2019.
Tickets now on sale for DataScienceGO, the #1 career-focused data science conference, coming to San Diego, CA, Sep 27-29. Use promo code KD-Nuggets for 20% off.
- 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.
- 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.
- 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.
- Why do we need AWS SageMaker? - Jun 26, 2019.
Today, there are several platforms available in the industry that aid software developers, data scientists as well as a layman in developing and deploying machine learning models within no time.
- 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.
- 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.
- 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.
- 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
- KDnuggets™ News 19:n23, Jun 19: Useful Stats for Data Scientists; Python, TensorFlow & R Winners in Latest Job Report - Jun 19, 2019.
This week on KDnuggets: 5 Useful Statistics Data Scientists Need to Know; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS; How to Learn Python for Data Science the Right Way; The Machine Learning Puzzle, Explained; Scalable Python Code with Pandas UDFs; and much more!
- 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.
- 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.
- Show off your Data Science skills with Kaggle Kernels - Jun 14, 2019.
Kaggle is not just about data science competitions. They also have a platform called Kaggle Kernels, using which you can build a stellar data science portfolio.
- 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.