Bamboolib: One of the Most Useful Python Libraries You Have Ever Seen Here is my take on this cool Python by Ismael Araujo via @TDataScience

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

I thought this was a really useful library and definitely worth a try to see if it can help you. I definitely thought it was worth using as it made life a little easier.

DATA festival #online - 15 - 16 September 2021

 

"The DATA festival #online surprised me in a very positive way. The sessions were held by a wide variety of global companies, whose representatives reported on both their best practices and challenges. It's always great to exchange ideas with people who love data and analytics as much as I do!" - Jessica Weiler, Technical Service Data Manager at WMF Professional Coffee Machines We’re counting the days until the DATA festival #online returns on September 15th & 16th. Explore how #DATA & AI bring value to business. Enjoy the inspiring talks, panels and tables, join 2,000 other members of Europe’s #datacommunity! For one last time, you have the chance to take part without any travel or any costs for you!Save your free spot

A Complete Data Analytics Project with Python by Natassha Selvaraj via @TDataScience

Data collection, analysis, visualization, and presentation.

I really enjoyed this as it worked completely through the one example from start to finished explaining all of the thought processes. Go through this and use it as a bit of a blueprint on how to do this going forward.

Data doesn't speak for itself: Why data storytelling is so important by/via @YellowfinBI

“Letting the data speak for itself” is a well-known phrase with a hard truth: Data presented on its own rarely communicates meaning for itself. For most people, it’s the context behind the numbers, the story, that helps us understand and care to act.

This is so true - you have to try and tell a story with the data that you find and analyse if you want to communicate it adequately to others - if you don't do that then you have failed and it was almost a waste of time to look at it to start with. We all need to tell the story well enough that we bring those reading it with us - get them to believe and be invested in what you are saying. We can all learn from sales techniques when it comes to selling a vision with data.

Top 5 big data challenges and how you can address them by Terry Wilson via @datafloq

A decade on, big data challenges remain overwhelming for most organizations. Since ‘big data’ was formally defined and called the next game-changer in 2001, investments in big data solutions have become nearly universal. However, only half of companies can boast that their decision-making is driven by data, according to a recent survey from Capgemini Research Institute.

I love that this looks at the challenges and solutions to those challenges.

4 SQL Tips for Data Scientists and Data Engineers by @SeattleDataGuy via @BttrProgramming

 Please, don’t average averages is the first tip he has for us. 

These are really valuable insights and I completely agree with his observations. I love that he has given you code segments as well so there are no excuses for not understanding these.  Some of these links seamlessly into basic rules of data analytics and make sure that you do not skew your results.

10 big data blunders businesses should avoid by Sara Brown via @MITSloan

Big data is a promising investment for firms, but embracing data can also bring confusion and potential minefields -  everything from where companies should be spending money to how they should be staffing their data teams.

This was an interesting read and definitely a good list to use as a basis of what you need to avoid in order to not make a mistake.

WEBINAR: DataOps: How Bell Canada Powers their Business with Data - 15 July 2020

Data Science Central Webinar Series Event

DataOps: How Bell Canada Powers their Business with Data Join us for the latest DSC Webinar on July 15th, 2020 Agile data management has become a necessity for organizations that need to maximize the value of their data. The business is focused on outcomes but the bulk of the effort is built around data processing and delivery. Demand for data outstrips the capacity of IT organizations and data engineering teams to deliver. New data management practices that adapt the practices DevOps to support data operations (DataOps) are the key to agility in data management. The enabling technologies exist today and data management practices are moving quickly toward a future of DataOps. DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. In this latest Data Science Central webinar, you will learn: How to identify the most impactful bottlenecks sitting the way of streamlined data processing How to evaluate multiple strategies for improving data processing outcomes and their relative impact Where to prioritize people, process, and technology changes to maximize impact How Bell revolutionized their data delivery framework by incorporating DataOps principles and technology Featured Speakers: Johnathan Bald, Sr. Director of Sales -- Hitachi Vantara Jude Vanniasinghe, Sr. Manager of Business Intelligence -- Bell Presentation Moderator: Mike Williams, Global Solution Lead, Analytics and IoT -- Hitachi Vantara Hosted by: Sean Welch, Host and Producer -- Data Science Central   Title:  DataOps: How Bell Canada Powers their Business with Data Date:  Wednesday, July 15th, 2020 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Scraping Court Records Data to Find Dirty Cops by Kristin via @LawsuitDotOrg

Given recent events, it's more important than ever to root out "bad apples." This article introduces some of the issues that citizen data investigators face, available resources, and how you can help. 

This is a great us to technology and available data to do some good even if it is looking backwards - maybe it can prove as a deterrent long time as they will know that they will be caught.

WEBINAR: Developing and Testing Shiny Apps - 12 March 2020

Data Science Central Webinar Series Event

Developing and Testing Shiny Apps Join us for the latest DSC Webinar on March 12th, 2020 Shiny is the most popular framework among R users for developing dashboards and web applications. It is commonly used by statisticians and data scientists to present and share their work with broader groups. These dashboards are often developed inside the RStudio IDE and then published to hosting servers. RStudio IDE users have been enjoying the power of Databricks clusters and other workspace features since 2018. Now they can use Shiny on Databricks as well. In this latest Data Science Central webinar, we will review how RStudio Server works on Databricks clusters and the advantages of running RStudio Server inside the Unified Data Analytics Platform. We will introduce a new addition to the Unified Platform for R users on Databricks: support for Shiny applications. This webinar will include a demo that will focus on the lifecycle of developing and testing Shiny applications inside hosted RStudio Server, as well as what can be done with a high-bandwidth connection to a powerful Apache Spark cluster. Speaker: Hossein Falaki, Tech Lead -- Databricks Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Developing and Testing Shiny Apps Date:  Thursday, March 12th, 2020 Time:  09:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Overcoming the five most common data analytics challenges by Christopher Roberts via @Infomgmt

To optimize your business, you must accumulate and analyze the data and feedback you’ve been getting from all aspects of your business. Here are the solutions to top challenges.

A very useful article that I think deserves a read and bookmark.

6 Data Analytics and Business Intelligence Trends to Fire Up Your Business by @eSparkBiz via @Datafloq

Nowadays, when business intelligence and data analytics are in huge demand, this blog will help you to be aware of its latest trends with ease.

Time and time again we come back to the need for quality data and proper data management. We really need to learn the lessons and actually start sorting it out. Yes it will cost money, yes it will use resources, but it is an INVESTMENT into your company just as buying a new software package or piece of machinery is.

Data Warehouse or Data Lake? When to Use Each by @LimiMaayan via @Datafloq

What platform should you use to power your data analytics machine? Data warehouses and data lakes are common two alternatives.

Some thoughts to help you decide between the two. Many analytics rely on the data having some kind of structure which points more towards a data warehouse, but I can also see that you can discover new things when you do more with unstructured data (data lake). It really depends on what you are trying to do.

Will AI make data analytics jobs obsolete? by Anna Johansson via @infomgmt

If developers create an algorithm that can process vast volumes of data, present it in an easily recognisable form, and even draw basic conclusions from it, it could threaten many job positions.

Maybe data analysts need to start expanding and retraining ready for when they will no longer be needed?  I do think that at the moment you definitely need a human to interpret some results and their meaning.

The unexpected benefits of data analytics by Bob Violino via @CIOonline

Data analytics can uncover surprising insights that lead to unexpected new program or product ideas, as these six real-world examples show.

This is great as it gives some real world examples of the benefits of using analytics.

Why Blockchain and Analytics Don't Mix Well by @billfranksga via @iianalytics

Blockchain and analytics might not mix as analysing data within a blockchain environment would be different from how we analyse data within other platforms.

Bill has some great points in this article and it's only when you sit and think hard about this that you realise that this is not the same as any traditional database and that you could get lost in processing and searching the chains to get the anwser to all the questions you are faced with.

Dirty Data Is OK, How You Cleanse It Matters by Chirag Shivalker via @DZone

It has been an unsolved mystery for companies if they should get their data cleansed first to opt for data analytics or if they should opt for data analytics to conclude whether their data is dirty.

There are some really good points in this article.  I cannot emphasise enough the single source of truth point.  We must all have worked for organisations where department A's figures don't match department B's.  You cannot run an organisation if the numbers in your reporting don't match, and even worse you have no idea why they don't match. You need data management, agreed definitions for data, and just the one source of the truth across the entire company.