- Models of Data Science teams: Chess vs Checkers - Apr 14, 2021.
Should we still consider data scientists and data engineers as separate roles? When should a team grow with full-stack data developers? Introducing the Checkers-like data team.
- Six Tips on Building a Data Science Team at a Small Company - Jan 4, 2021.
When a company decides that they want to start leveraging their data for the first time, it can be a daunting task. Many businesses aren’t fully aware of all that goes into building a data science department. If you're the data scientist hired to make this happen, we have some tips to help you face the task head-on.
- KDnuggets™ News 20:n42, Nov 4: Top Python Libraries for Data Science, Data Visualization & Machine Learning; Mastering Time Series Analysis - Nov 4, 2020.
Top Python Libraries for Data Science, Data Visualization, Machine Learning; Mastering Time Series Analysis with Help From the Experts; Explaining the Explainable AI: A 2-Stage Approach; The Missing Teams For Data Scientists; and more.
- The Missing Teams For Data Scientists - Nov 2, 2020.
Still today, too large a percent of data science projects fail, many of which can be attributed to the impacts of how hard missing data teams hit the data science team. Advocating for the missing data engineering and operations components to your team will make your professional life easier and more productive.
- The Maslow’s hierarchy your data science team needs - Sep 15, 2020.
Domino Data Lab was announced as a leader for the second year in a row in the recently released “Forrester Wave™: Notebook-based Predictive Analytics and Machine Learning (PAML), Q3 2020” analyst report. True to our data science roots, we’ve built a Maslow’s hierarchy of data science team needs.
- How to Make Remote Work Effective for Data Science Teams - Mar 23, 2020.
This post aims to highlight some work from home best practices, both general and data science-specific, in order to help data scientists and teams remain productive, connected and happy while working remotely.
- Building a Mature Machine Learning Team - Mar 13, 2020.
After spending a lot of time thinking about the paths that software companies take toward ML maturity, this framework was created to follow as you adopt ML and then mature as an organization. The framework covers every aspect of building a team including product, process, technical, and organizational readiness, as well as recognizes the importance of cross-functional expertise and process improvements for bringing AI-driven products to market.
- 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.
- KDnuggets™ News 20:n01, Jan 8: How to “Ultralearn” Data Science; How teams do AutoML? - Jan 8, 2020.
First issue of 2020 brings you a summary of how to "Ultralearn" Data Science - for those in a hurry; Explains how teams work on AutoML project; Why Python is a preferred language for Data Science; and a cartoon on teaching ethics to AI.
- Automated Machine Learning: How do teams work together on an AutoML project? - Jan 2, 2020.
In this use case, available to the public on GitHub, we’ll see how a data scientist, project manager, and business lead at a retail grocer can leverage automated machine learning and Azure Machine Learning service to reduce product overstock.
- How to Make an Agile Team Work for Big Data Analytics - Oct 31, 2019.
Learn how to approach the challenges when merging an agile methodology into a data science team to bring out the best value for your Big Data products.
- How to better manage your data science team’s workflow - Aug 5, 2019.
This workshop, Aug 14 @ 12 PM ET, will give you the proper tools and tactics to manage the entire lifecycle of your machine learning projects, from research to exploration to development and production.
- How to Build Disruptive Data Science Teams: 10 Best Practices - Jul 16, 2019.
Building a data science team from the ground up isn't easy. This strategic roadmap will help hiring managers with tactical advice and how to properly support a data science team once established.
- How to Make a Success Story of your Data Science Team - Jun 25, 2019.
Today, data science is a crucial component for an organization's growth. Given how important data science has grown, it’s important to think about what data scientists add to an organization, how they fit in, and how to hire and build effective data science teams.
- KDnuggets™ News 19:n15, Apr 17: Time Series Forecasting with Neural Nets and LSTM; Why Data Scientists Need To Work In Groups - Apr 17, 2019.
Also: Why Data Scientists Need To Work In Groups; Data Science with Optimus - Intro; Make Your Own Job in Data Science; 2019 Best Masters in Data Science and Analytics - Europe Edition.
- KDnuggets™ News 19:n10, Mar 6: What no one will tell you about data science job applications; The rise of ML Engineering - Mar 6, 2019.
Also most impactful AI trends of 2018: The rise of ML Engineering; How to do Everything in Computer Vision; GANs Need Some Attention, Too; OpenAI GPT-2.
- On Building Effective Data Science Teams - Mar 4, 2019.
We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.
- The Analytics Engineer – new role in the data team - Feb 13, 2019.
In a constantly changing landscape and with many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing, forcing the introduction of a new role: The Analytics Engineer.
- How to Build a Machine Learning Team When You Are Not Google or Facebook - Nov 28, 2018.
If you don’t have a clear application for machine learning, you’re going to regret your investment. We provide tips on how to go about setting up your machine learning team - no matter the size of your business.
- Machine Learning in Action: Going Beyond Decision Support Data Science - Nov 20, 2018.
In order to disrupt business, machine learning models must adopt a product-focused approach, which is a much more significant undertaking.
- 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.
- How to Balance the Load on a Data Team - Jul 11, 2018.
This post will help you to better understand a data team’s workflow and allocate their resources to business users.
- How should I organize a larger data science team? - Jun 15, 2018.
VP of Data Science is asking opinions on how should he organize a larger Data Science team.
- Solve Data Science Challenges Through Collaboration - Apr 3, 2018.
Get this eBook to learn key issues that hamper fragmented data science teams; how accelerate innovation via collaborative workspaces, and how top data science teams boosted productivity by up to 4x.
- Generalists Dominate Data Science - Feb 2, 2018.
An interesting insight into why small teams generalists outperform large teams of specialists.
- How to Make Life Easy for a Newly Hired Data Scientist - Jan 30, 2018.
In this post, I am going to describe the life of a newly hired data scientist. The use case is that the data scientist is given a project where he needs to build an online learning model.
- How to build a Successful Advanced Analytics Department - Jan 4, 2018.
This article presents our opinions and suggestions on how an Advanced Analytics department should operate. We hope this will be useful for those who want to implement analytics work in their company, as well as for existing departments.
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- 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."
- How To Become a 10x Data Scientist, part 2 - Sep 19, 2017.
A 10x developer is someone who is 10 times more productive than average. We adapt tips and tricks from the developer community to help you become a more proficient data scientist loved by team members, including code design and selecting right tools for the job.
- How To Become a 10x Data Scientist, part 1 - Sep 18, 2017.
A 10x developer is someone who is 10 times more productive than average. We adapt tips and tricks from the developer community to help you become a more proficient data scientist loved by team members and stakeholders.
- KDnuggets™ News 17:n28, Jul 26: 5 Free Resources to start with Deep Learning for NLP; Emotional Intelligence for Data Science Teams - Jul 26, 2017.
Also AI and Deep Learning, Explained Simply; When not to use deep learning; Optimism for AI drop with experience developing AI systems.
- Emotional Intelligence for Data Science Teams - Jul 20, 2017.
Here are three lessons for making and demonstrating a greater business impact to your organization, according to Domino Labs most successful customers.
- Top KDnuggets tweets, Jul 12-18: 10 Free #MustRead Books for #MachineLearning and #DataScience; Why #AI and Machine Learning? - Jul 19, 2017.
Also top 32 Reasons #DataScience Projects and Teams Fail; Text Classifier Algorithms in #MachineLearning; The 4 Types of #Data #Analytics: Descriptive, Diagnostic ...
- DataScience.com, H2O.ai Partner to Bring AI Capabilities to Enterprise Data Science Teams - Jun 23, 2017.
DataScience.com Platform customers can now easily deploy artificial intelligence and deep learning models built with H2O.ai’s open source AI platform.
- The Evolution of a Productive Data Team - Apr 11, 2017.
Successful data teams at companies of any size are able to produce results because they develop gradually through a series of stages and acquire skills along the way that help them stay efficient and effective.
- The Librarian, the Scientist, the Alchemist and the Engineer: Anatomy of a DataOps Expert - Apr 10, 2017.
We know various job profiles in data science – data engineer, data scientist, data analyst etc. Here we explain how these roles fits in a real world data science team and what they do.
- The Data Science Project Playbook - Mar 1, 2017.
Keep your development team from getting mired in high-complexity, low-return projects by following this practical playbook.
- Getting Real World Results From Agile Data Science Teams - Feb 10, 2017.
In this post, I’ll look at the practical ingredients of managing agile data science. By using agile data science methods, we help data teams do fast and directed work, and manage the inherent uncertainty of data science and application development.
- Laying the Foundation for a Data Team - Dec 28, 2016.
Admittedly, there is a lot more to building a successful data team, and we would be lying if we pretended we have it all figured out. But hopefully focusing on the elements in this post is a good start.
- Supercharge Your Data Science Team, Dec 21 Webinar - Dec 20, 2016.
On December 21st, Continuum Analytics CTO Peter Wang will share how you can supercharge your Data Science team by simplifying the building process for even the most complicated dashboards and display streaming data in real time.
- Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed - Nov 25, 2016.
Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.
- Data Avengers… Assemble! - Nov 19, 2016.
The Avengers are perfectly capable of defending the Earth from our worst enemies. But are they up to the task of taking care of our data? Read this terribly punny "opinion" piece to find out.
- Reasons Why Data Projects Fail - Nov 10, 2016.
Many companies seem to go through a pattern of hiring a data science team only for the entire team to quit or be fired around 12 months later. Why is the failure rate so high?
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- Big Data Science: Expectation vs. Reality - Oct 27, 2016.
The path to success and happiness of the data science team working with big data project is not always clear from the beginning. It depends on maturity of underlying platform, their cross skills and devops process around their day-to-day operations.
- How to Get Stuff Done at a Data Startup - Oct 13, 2016.
This post is a followup to how to structure data science teams, with a focus on how we get stuff done. The same principles we follow can be applied at your data startup or data science team.
- How to Structure Your Team When Building a Data Startup - Oct 1, 2016.
Data Startup in mind? Need to structure different teams? Here are guidelines for structuring Data Team, Crawl Development Team, Data Infrastructure Team, and more.
- The Core of Data Science - Aug 1, 2016.
This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.
- KDnuggets™ News 16:n21, Jun 15: What Big Data, Data Science tools go together? Oppys for Machine Learning Startups - Jun 15, 2016.
What Big Data, Data Science, Deep Learning software goes together? Opportunities for Machine Learning Startups; Top NoSQL Database Engines; How Do You Identify the Right Data Scientist for Your Team?
- How Do You Identify the Right Data Scientist for Your Team? - Jun 8, 2016.
Have you been trying to answer the question of what type of a data scientist would be the best fit for your team? Is there a single all-encompassing answer or does it vary based on the client objectives? Read on for some insight.
- The Benefits of Decentralizing Analytics Talent - Jun 4, 2016.
Over the next several years data will be served in a variety of ways, greater innovation will come from companies that look to share raw data. Here we talk about, democratizing the data which requires a different philosophy to allow all business functions to participate in analytics.
- Building effective “Citizens Data Scientist” teams - Apr 28, 2016.
The idea of citizen data scientists is being for more than a year, which suggests businesses to put the people from the business side in the work of exploring and analyzing data. Understand how you and your organisation can be benefitted by this.
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- Three Pitfalls to Avoid When Building Data Science Into Your Business - Apr 27, 2016.
An overview of pitfalls to avoid when building data science into your business, how to avoid them, and what to do instead.
- H2O World 2015 – Day 3 Highlights - Nov 20, 2015.
Highlights from talks delivered by machine learning experts from Fast Forward Labs, H20.ai, Kaiser and Macy's at H2O World held in Mountain View.
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- Three Essential Components of a Successful Data Science Team - Aug 10, 2015.
A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization,
- Webinar On-Demand: Your First Hire in Predictive Analytics (Hint: it is not a Data Scientist) - Jul 28, 2015.
In a thriving analytic practice, the role of a data scientist is not defined by a person, but by a team. Within that team, several roles may be filled by one person-and several people may fulfill a given role.
- Analytics Outsourcing to India: Should or Shouldn’t? - Feb 5, 2015.
Outsourcing analytics talent to India will continue to grow as a trend as evidenced by the increasing number of Fortune 500 companies participating in the practice.
- KDnuggets talks to IBM: Data scientists: Hire an individual or team? - Feb 13, 2014.
KDnuggets recent poll about Data Science - Individual vs Team has caught attention of IBM. Listen to the podcast where I discuss the unexpected findings of this poll and other Data Science topics.
- KDnuggets 14:n02, Split on Data Science; Data Science in Python Tutorial - Jan 22, 2014.
Split on Data Science - Team vs Individual Approach, Data Science in Python - free tutorial, PASS Free Online Business Analytics Training - Feb 5, Confessions of a Dataholic, and more analytics/data mining news.