D-Tale: One of the Best Python Libraries You Have Ever Seen by Ismael Araujo via @TDataScience

Here is his take on this must-have Python library and why you should give it a try.

I like this - it looks incredibly easy to use and very intuitive. Definitely, one to add to your list of very useful Python libraries.

Top 10 Pandas Functions for Preparing Data by Holly Dalligan via @BttrProgramming

Because she wanted to create useful, accurate analysis with as little work as possible.

I found this really interesting and it looks very useful too - data preparation if done well can help you to produce much better results from your analysis,

Improve your Model Performance with Auto-Encoders by Satyam Kumar via @TDataScience

Use Autoencoders as a Feature Extractor.

This was really useful and could potentially save a lot of time and effort if you can get the balance right.

Mito: One of the Coolest Python Libraries You Have Ever Seen by Ismael Araujo via @TDataScience

Here is Ismael Araujo's take on this cool Python library and why you should give it a try.

It does look interesting, saves so much time and I certainly want to play more with it as I already can see how useful it is but I'm sure I could achieve much more if I understood it better.

A step-by-step guide for creating an authentic data science portfolio project by Felix Vemmer in @kdnuggets

Especially if you are starting out launching yourself as a Data Scientist, you will want to first demonstrate your skills through interesting data science project ideas that you can implement and share. This step-by-step guide shows you how to do go through this process, with an original example that explores Germany’s biggest frequent flyer forum, Vielfliegertreff.

This is a great roadmap into what you need to do, what you need to create and document, what you should share and a great suggestion on where to share it. I would also suggest adding it to Kaggle as the site Felix suggest is German and a little niche.

Augmented Intelligence is the New Artificial Intelligence by Priya Dialani via @analyticsinme

Computers are getting more clever and increasingly inventive, offering terrific prospects to improve the human condition. There’s a call to rethink Artificial Intelligence as Augmented Intelligence, to stress the capability of people working with AI instead of being replaced by AI. 

I found this very interesting and well worth a read and think about it.

WEBINAR: Go from Data, to Data Prep, to Data Science 29 July 2020

July 29, 2020   11.00 am ET - 45 min including Q&A     It goes without saying, in order to train data science models to produce predictive forecasts, you need data. But the process of getting the data into models is not always cut and dry, as data science and analytics teams continue to struggle with getting the right kind of data, in the proper format, for the appropriate analysis. As a result, teams end up spending more time uncovering and preparing data for data science models than they do on refining the actual models. But this doesn't have to be the case. Through the powerful combination of Snowflake and DataRobot, it's now easy for data users to leverage the leading cloud data platform to quickly build, train and deploy data science models. Want to hear how? Register today to hear from Josh Klaben-Finegold, Product Manager at Datarobot and Mike Klaczynski, Director of Product Marketing at Snowflake, as they discuss how you can conduct enterprise self-service data prep for data science in just a few clicks. Join and learn how: Snowflake + DataRobot empower users to collaborate, prepare, and process data for machine learning at scale, with enterprise governance You can easily prepare your data for feature engineering Leveraging the power of both Snowflake + DataRobot together is easy and seamless via Snowflake Partner Connect - demo included!     Register today →

Data Prep Still Dominates Data Scientists’ Time, Survey Finds by Alex Woodie via @datanami

Data scientists spend about 45% of their time on data preparation tasks, including loading and cleaning data, according to a survey of data scientists conducted by Anaconda. The company also analyzed the gap between what data scientists learn as students, and what the enterprises demand.

Yes, it does take time, but if you prepare your data right then the results will be good.

WEBINAR: Democratizing Analytics from the Ground Up 28 April 2020

Data Science Central Webinar Series Event

Democratizing Analytics from the Ground Up Join us for the latest DSC Webinar on April 28th, 2020 How can you transition your business users from a manual, slow, and prone to error data transformation process in Excel into a scalable and governed solution leveraging a centralized data lake and change the way your employees access and use data? In this latest Data Science Central webinar you will learn how to: Empower self-service data analysis for all of your employees to use Data Prep and Data Studio Transition from a process that heavily relies on siloed Excel work to a Bring Your Own Data (BYOD) approach leveraging a centralized data lake Improve data governance and inspire business and IT collaboration with a single consistent approach to data preparation Accelerate analytics project delivery from months down to weeks Featured Speakers: Christopher Dean, Head of Data, Business Intelligence & Analytics -- Travis Perkins Bertrand Cariou, Sr. Director Product -- Trifacta Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Democratizing Analytics from the Ground Up Date:  Tuesday, April 28th, 2020 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

WEBINAR: Why Data Prep is Step 1 for Analytics Success - 6th June 2019

Data Science Central Webinar Series Event

Why Data Prep is Step 1 for Analytics Success Join us for the latest DSC Webinar on August 6th, 2019 Sad but true when it comes to data prep - data practitioners spend up to 80% of their time scrubbing and preparing their data before performing any meaningful analytics. Meanwhile, organizations are increasingly moving data and analytics from the on-premise environment to the cloud as part of their digital transformation initiatives. The rapid migration to the cloud further extends this data preparation nightmare given the varying shapes and sizes of the data stored in the cloud. Join David Menninger, SVP & Research Director at Ventana Research, and Jie Wu, Product Marketing Director at Trifacta for a live discussion on how to select a cloud data preparation solution to accelerate your analytics journey in the cloud. In this latest Data Science Central webinar, you will learn: Challenges with data prep in the cloud Why ETL tools alone are not sufficient to deliver well-prepared data in the cloud Key considerations when selecting a data prep tool for cloud data lakes and cloud data warehouses Featured Speakers: Jie Wu, Product Marketing Director -- Trifacta David Menninger, SVP & Research Director -- Ventana Research Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Why Data Prep is Step 1 for Analytics Success Date:  Tuesday, August 6th, 2019 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

WEBINAR: Predictive Modelling's Counterpart, Data Preparation 9 May 2019

Data Science Central Webinar Series Event

Predictive Modeling’s Counterpart, Data Preparation Join us for the latest DSC Webinar on May 9th, 2019 When plunging into predictive analytics, we often forget to talk about the data preparation necessary for it. In this latest Data Science Central webinar, we will use a movie database as a fun example, and we’ll work towards creating a model to predict a movie’s overall rating—to see if certain actors, the genre, or even movie length has an impact on its rating. We will also discuss what to keep in mind in terms of data preparation as we work towards developing a training dataset; making sure that the data preparation is repeatable, that all team members understand the process (to ensure buy-in), and that additional information can be created from the data available. You’ll learn how Rapid Insight’s Veera platform makes all of this easy, saving time and resources. Key highlights include: Democratizing the data, or creating a process that most people would be able to follow, regardless of professional background or industry Ensuring buy-in because it helps you communicate to everyone in the organization about the model and data preparation Creating a repeatable and schedulable workflow for data preparation Predicting movie ratings and looking at what type of reviews a movie pitch might get Speakers: Jon MacMillan, Senior Data Analyst -- Rapid Insight Alex Herbert, Sales Manager -- Rapid Insight Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Predictive Modeling’s Counterpart, Data Preparation Date:  Thursday, May 9th, 2019 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

WEBINAR: Data Prep & Automated ML: Better Predictions For Consensus - 22 January 2019

Data Prep & Automated ML: Better Predictions For Consensus Join us for the latest DSC Webinar on January 22nd, 2019

Financed smartphones are a magnet for identity theft, leaving retailers in the digital and telecommunication industry vulnerable to fraud. Consensus, a Target-owned subsidiary, has developed a highly accurate solution to identify fraud at the point-of-sale before it happens. In this latest Data Science Central webinar, you will learn how Consensus put together agile processes on a cloud analytic solution leveraging Trifacta data preparation and DataRobot automated machine learning to prevent fraud. Attendees will learn: How Consensus developed an AWS cloud-based solution The role of data preparation in supplying accurate data for machine learning models How automated machine learning can drive more accurate predictions Consensus’s time-saving ROI from building models, deploying them on AWS, and the improvement in accuracy and recall Speakers: David McNamara, Lead Product Specialist -- Trifacta Harrison Lynch, Sr. Director of PM -- Consensus Corporation Rajiv Shah, Data Scientist -- DataRobot Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Data Prep For Data Ops: How To Select & Deploy Date:  Tuesday, January 22nd, 2019 Time:  9 AM - 10 AM PST

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WEBINAR: Data Prep For Data Ops: How To Select & Deploy - 12 December 2018

Data Science Central Webinar Series Event

Data Prep For Data Ops: How To Select & Deploy Join us for the latest DSC Webinar on December 12th, 2018 In recent years, a new term in data has cropped up more frequently: DataOps. As an adaptation of the software development methodology DevOps, DataOps refers to the tools, methodology and organisational structures that businesses must adopt to improve the velocity, quality and reliability of analytics. Widely recognised as the biggest bottleneck in the analytics process, data preparation is a critical element of building a successful DataOps practice by providing speed, agility and trust in data. Join guest speaker, Forrester Senior Analyst Cinny Little, for this latest Data Science Central webinar focusing on how to successfully select and deploy a data preparation solution for DataOps. The presentation will include insights on data preparation found in the Forrester Wave™: Data Preparation Solutions, Q4 2018. During this webinar you will learn: Where does data preparation fit within DataOps What are the key technical & business differentiators of data preparation solutions How to align the right technologies, people and processes Speakers: Will Davis, Sr. Director of Product Marketing -- Trifacta Cinny Little, Senior Analyst -- Forrester Hosted by: Bill Vorhies, Editorial Director -- Data Science Central   Title:  Data Prep For Data Ops: How To Select & Deploy Date:  Wednesday, December 12th, 2018 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now   After registering you will receive a confirmation email containing information about joining the Webinar.

WEBINAR: 4 Ways to Tackle Common Data Prep Issues - 25 September 2018

Anyone who's ever analysed data knows the pain of digging in only to find that  it is poorly structured, full of inaccuracies, or just plain incomplete. But "dirty data"  isn't just a pain point for analysts; it can have a major financial and cultural impact on  an organisation.  In this latest Data Science Central webinar, you will learn four actionable ways to  overcome common data preparation issues, including how to establish a company  standard for "clean data" and how to democratize data prep across your organisation.  Speaker: Louis Archer, London Manager -- Tableau Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  4 Ways to Tackle Common Data Prep Issues Date:  Tuesday, September 25th, 2018 Time:  9:00 AM - 10:00 AM PDT

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WEBINAR: Minimizing Model Risk with Automated Data Preparation & Machine Learning - 19 April 2018

Webinar - Thursday, April 19, 2018 2:00 pm ET/ 11:00 am PT - 60 minutes with Q&A   REGISTER     In today's business landscape, predictive analytics are a necessity to remain competitive, but working with data and developing accurate predictive models is challenging. The quality of predictive output relies on the quality of input. That's why proper data preparation is such a critical success factor for achieving optimal machine learning results. However, getting the data prepared for analysis is a time-consuming process. In addition, models are inherently complex - and if developed poorly can do more harm than good. Register for this webinar to learn how to use Automated Data Preparation & Machine Learning to gain a competitive advantage, while quickly aligning your business operations to regulatory requirements. We discuss current trends and expectations for model risk management regulatory compliance, how to reduce the time it takes to prepare data, and how industry-leading organizations are leveraging Machine Learning to provide a much stronger framework for model development and validation than traditional manual efforts. You'll discover: How Self-Service Data Preparation reduces the work required to get data ready for predictive modeling Efficient methods to organize complex data and marry multiple datasets for predictive modeling How Automated Machine Learning reduces model risk, while ensuring the implementation of cutting edge machine learning models How Automated Machine Learning enhances compliance to model risk management regulation     REGISTER     Speakers       Seph Mard Head of Model Risk Management  DataRobot     Christopher Moore Lead Solution Engineer & Data Wrangler  Trifacta

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