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Pay what you want and get your dataviz humble bundle here.
More from the Humble Bundle website:
Make insights & analytics take shape
Learn how to give your raw materials form and substance with books like Visual Analytics with Tableau, Data Fluency, and Visualize This from Wiley, filled with expert advice on the tools and techniques you & your organization need. Plus, your purchase helps support Girls Who Code!
Complete list of data visualization books in this humble bundle:
UPDATE July 25, 2022: Along with other data visualization book authors, we already raised over $23,000 in donations for Girls Who Code through the Dataviz Humble Bundle: https://bit.ly/DataVizBundle
How can a Tableau dashboard that displays contacts (name & company) automatically look up LinkedIn profile URLs?
Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…
Step by Step: Integrating Azure Cognitive Services in Tableau
1. Python and TabPy
We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.
2. Microsoft Azure Cognitive Services
One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + „linkedin“. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.
3. Calculated Field in Tableau
Let’s wrap our Python script together and create a Calculated Field in Tableau:
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Adding a URL action with our new Calculated Field will do the trick. Now you can click on the LinkedIn icon and a new browser tab (or the LinkedIn app if installed) opens.
This tutorial is just the tip of the iceberg. If you want to dive deeper into the world of data visualization and analytics, don’t forget to order your copy of my new book, Visual Analytics with Tableau (Amazon). This comprehensive guide offers an in-depth exploration of data visualization techniques and best practices.
I’d love to hear your thoughts. Feel free to leave a comment, share this tweet, and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Azure Cognitive Services in Tableau and other data analytics topics.
Is crypto dead? With Bitcoin crashing to its lowest this year, losing over 25% in a week, many investors are asking this exact question.
Yesterday, on my way to an AI roundtable, I had an interesting conversation about the future of crypto assets. I met Michael, who works for one of the world’s biggest insurance companies, on the train from Frankfurt to Munich. Of course, our conversation started with a nifty 7-minute Tableau demo – a wonderful ice breaker!
After closing the demo with the Bitcoin Dashboard on Tableau Public, the conversation quickly headed towards crypto assets and (non-)blockchain FinTechs. These are the top 3 of Michael’s questions that I want to share with you – together with my answers:
1. Is the blockchain innovation dead or is crypto dead?
Absolutely not! Blockchain is a relatively new technology which has a long way to go before it becomes mainstream. Last year, the most successful projects were those that aimed at adapting new technologies for convenient use. Furthermore, crypto assets create a new structure of safe and anonymous storage and management of information. Projects like Ethereum have proven to be extremely useful for building steady and secure smart contracts, cloud storage, and product quality control.
2. Are there still interesting crypto assets to buy or to mine?
Yes, indeed! In particular, I suggest focusing on crypto assets targeting innovative use cases. If you buy these, you are actually investing in technology projects:
Chainlink (LINK): Provides decentralized oracles that connect smart contracts with off-chain data, essential for the future of decentralized finance (DeFi).
Stellar (XLM): Focuses on cross-border payments and financial inclusion, making it ideal for emerging markets.
VeChain (VET): Specializes in supply chain management and ensures transparency in logistics.
Factom (FCT): recently announced a partnership with Equator PRO, and according to the press release that announced the -partnership, Equator PRO is a software-as-a-service (SaaS) solution that aims to offer efficiency and oversight to help other mortgage servicers.
For mining, consider these:
Grin (GRIN): Uses the privacy-focused Mimblewimble protocol, offering scalable and anonymous transactions.
Ravencoin (RVN): Focuses on asset tokenization and remains a strong candidate for mining enthusiasts.
3. Which non-blockchain related FinTech might be worth looking at?
Definitely! FinTech innovation goes far beyond blockchain. Key areas to watch include:
AI-powered Financial Analytics: Platforms like Tableau and Microsoft Power BI are transforming how businesses visualize and analyze data in real-time. AI integration can deliver actionable insights faster than ever.
Robo-Advisors: Tools like Betterment and Wealthfront use machine learning to provide personalized investment strategies.
Alternative Lending Platforms: Companies like Mintos and Funding Circle are making loans more accessible to small businesses, leveraging AI and data analytics for risk assessment.
Outlook: Is Crypto Dead or Ready to Evolve?
The question „Is Crypto Dead?“ often arises during downturns, but history suggests resilience. Blockchain technology is still in its early stages, and future innovations, such as quantum-resistant cryptography and tokenized real-world assets, will shape its evolution.
We may soon witness seamless integration of AI with blockchain, enabling self-governing smart contracts and predictive financial models. Moreover, decentralized finance (DeFi) is set to challenge traditional banking structures, creating opportunities for financial inclusion on a global scale.
Anyone can analyze basic social media data in a few steps. But once you’ve started diving into social analytics, how do you bring it to the next level? This session will cover strategies for scaling a social data program. You’ll learn skills such as how to directly connect to your social media data with a Web Data Connector, considerations for building scalable data sources, and tips for using metadata and calculations for more sophisticated analysis.
Here are some key takeaways and links (i.e. additional resources) featured during my TC18 sessions to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:
Step 1: Understand How to Succeed with Social Media
Apple has officially joined Instagram on 7th August 2017. This isn’t your average corporate account as the company doesn’t want to showcase its own products. Instead, Apple is going to share photos shot with an iPhone:
And there are plenty takeaways for every business:
Wrap your data around your customers, in order to create business value
Interact with your customer in a natural way
Understand your customer and customer behaviour better by analyzing social media data
Step 2: Define Your Social Objectives and KPIs
A previous record-holding tweet: In 2014, actor and talk show host Ellen DeGeneres took a selfie with a gaggle of celebrities while hosting the Oscars. That photo has 3.44 million retweets at the time of writing:
Social listening means that you look beyond your own content. E.g. Talkwalker offers AI for image recognition and ggregation for online/offline media: http://bit.ly/tc17_talkwalker
Are you ready for Tableau Conference 2018? Don’t miss my Social Media Analytics sessions!
Why do we need Social Media Analytics?
Social Media Analytics transforms raw data from social media platforms into insight, which in turn leads to new business value.
What will your learn in this sessions?
Once you dive into Social Media Analytics, how do you bring it to the next level? Social data can offer powerful insights right away. In this session, you will learn how to build a mature social data program from that foundation and strategies for scaling a social data programme, as well as how to connect directly to your social media data with a web data connector; considerations for building scalable data sources; and tips for using metadata and calculations for more sophisticated analysis.
Where and when are the sessions?
Do you want to learn more about Social Media Analytics with Tableau? Meet me at the 2018 Tableau Conferences in London or New Orleans and join my sessions:
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