- 8 New Tools I Learned as a Data Scientist in 2020 - Jan 14, 2021.
The author shares the data science tools learned while making the move from Docker to Live Deployments.
- 5 Tools for Effortless Data Science - Jan 11, 2021.
The sixth tool is coffee.
- 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use - Nov 18, 2020.
If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.
- Data Science Tools Illustrated Study Guides - Aug 25, 2020.
These data science tools illustrated guides are broken up into four distinct categories: data retrieval, data manipulation, data visualization, and engineering tips. Both online and PDF versions of these guides are available.
- What I learned from looking at 200 machine learning tools - Jul 21, 2020.
While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.
- Top 10 Data Visualization Tools for Every Data Scientist - May 5, 2020.
At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.
- 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.
- Command Line Basics Every Data Scientist Should Know - Aug 15, 2019.
Check out this introductory guide to completing simple tasks with the command line.
- Five Command Line Tools for Data Science - Jul 31, 2019.
You can do more data science than you think from the terminal.
- Using the ‘What-If Tool’ to investigate Machine Learning models - Jun 6, 2019.
The machine learning practitioner must be a detective, and this tool from teams at Google enables you to investigate and understand your models.
- The 2018 Data Scientist Report is Here - Aug 23, 2018.
Learn about the data and tools that data scientists are working with in 2018, Ethical issues around AI, Algorithmic bias, Job satisfaction, and more.
- 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.
- KDnuggets™ News 18:n13, Mar 28: Where did you apply Data Science/ML? 12 Essential Command Line Tools for Data Scientists - Mar 28, 2018.
Also: 8 Common Pitfalls That Can Ruin Your Prediction; Text Data Preprocessing: A Walkthrough in Python; CatBoost vs. Light GBM vs. XGBoost.
- 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 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.
- Top KDnuggets tweets, Dec 06-12: Top #DataScience and #MachineLearning Methods Used in 2017; Geoff Hinton Capsule Networks – a new way for machines to see - Dec 13, 2017.
Also The first international #beauty contest decided by #AI #algorithm sparked controversy; 4 Common #Data Fallacies That You Need To Know; Using #DeepLearning to Solve Real World Problems; Best Online Masters in #DataScience and #Analytics.
- KDnuggets™ News 17:n46, Dec 6: Why You Should Forget for-loop for Data Science Code; Reinforcement Learning: Exclusive Interview with Rich Sutton; Big Data Key Trends - Dec 6, 2017.
Also Big Data: Main Developments in 2017 and Key Trends in 2018; Exclusive: My interview with Rich Sutton, the Father of Reinforcement Learning; Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras.
- New Poll: Which Data Science / Machine Learning methods and tools you used? - Nov 20, 2017.
Please vote in new KDnuggets poll which examines the methods and tools used for a real-world application or project.
- 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.
- From Notebooks to JupyterLab – The Evolution of Data Science IDEs - Aug 16, 2017.
This live webinar (Aug 22) will discuss the impact that the notebook experience has had on data science, and how JupyterLab - the next generation data science IDE - has evolved from the classic notebooks.
- Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists - Jul 7, 2016.
Here is an interview with Florian Douetteau, founder of Dataiku, on how their tools empower data scientists, and how data science itself is evolving.
- Glimpses & Impressions: Strata Silicon Valley AI + ML Review – Part One - Jul 7, 2016.
Read some impressions from a visit to Strata Silicon Valley in March. The focus is on integration of data science and machine learning tools, as well as the simplification of related processes.
Pages: 1 2
- Top 15 Frameworks for Machine Learning Experts - Apr 19, 2016.
Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.
- Caravel: Airbnb’s data exploration platform - Apr 13, 2016.
For data exploration, discovery, and collaborative analytics, AirBnB have built and open sourced, a data exploration and dashboarding platform named Caravel. It allows data exploration through rich visualizations while performing fast and intuitive “slicing and dicing” of your dataset.
- Data Science Tools – Are Proprietary Vendors Still Relevant? - Mar 25, 2016.
We examine and quantify the dramatic impact of open source tools like R and Python on SAS, IBM, Microsoft, and other proprietary Data Science vendors. We also investigate how open source tools were faring against each other, which are growing, which are falling, and look R versus Python debate.
Pages: 1 2
- Doing Data Science at Twitter - Sep 16, 2015.
Data scientist career exciting, fulfilling and multidimensional career path. Understand through the journey of a data scientist of twitter about data scientists roles, responsibilities and skills required to perform them.
Pages: 1 2 3
- The Big ‘Big Data’ Question: Hadoop or Spark? - Aug 5, 2015.
With a considerable number of similarities, Hadoop and Spark are often wrongly considered as the same. Bernard carefully explains the differences between the two and how to choose the right one (or both) for your business needs.
Pages: 1 2
- Emacs for Data Science - Jul 10, 2015.
Data science nowadays demands a polyglot developer and, choosing a correct code editor would definitely be a worthy investment. Here we provide, important features of Emacs and its advantages over other editors.
- Top 30 Social Network Analysis and Visualization Tools - Jun 1, 2015.
We review major tools and packages for Social Network Analysis and visualization, which have wide applications including biology, finance, sociology, network theory, and many other domains.
Pages: 1 2 3
- 21 Essential Data Visualization Tools - May 28, 2015.
We have collected leading data visualization tools, with a short overview of each tool, its strong and weak points.
Pages: 1 2 3
- R vs Python for Data Science: The Winner is … - May 26, 2015.
In the battle of "best" data science tools, python and R both have their pros and cons. Selecting one over the other will depend on the use-cases, the cost of learning, and other common tools required.