Data Analytics

When work flows, insights grow with Tableau for Slack

When work flows, insights grow with Tableau for Slack
Data has never been more important. It answers key questions that drive business growth. It helps organizations connect with customers and provide personalized experiences. It leads to smarter decisions grounded in both intuition and insights. And there’s a lot of it: According to IDC, more than 64 zettabytes (yes, zettabytes) of data were generated in 2020, and global data creation and replication will grow by 23% through 2025.
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Can you be a Data Scientist without coding?

Can you be a Data Scientist without coding?
Whether you can call yourself a data scientist if you can't code is as hotly debated as Brexit. Type the question "Can you be a Data Scientist without coding?" into Google and you'll get a hundred different answers. The opinion will vary wildly depending on whether the author is a coder, or a non-coder. Search the job listings, and you won't find a definitive answer there either. Rather than fill the internet with yet another opinion (as I am not a coder, my opinion would be quite biased), I thought I would perform a little meta analysis. For this post, I pooled data from about sixty different sources, to uncover current thinking on what is quite a debatable topic. You won't find all sources listed below, as I have no wish…
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A Guide to Google Data Studio for Better Reporting

A Guide to Google Data Studio for Better Reporting
Data is the backbone of marketing. It can show you where to focus your ad spend, what types of customers are most profitable, and so much more. On its own, however, data is just numbers. To make the most of your data, you need to understand what it means. That is where Google Data Studio […]
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Introducing the new Tableau platform, reimagined for the enterprise

As more businesses accelerate digital transformation initiatives, leaders are gaining a better understanding of the value of data, and want to scale analytics across their organization. However, on the road to becoming a data-driven organization, choosing an enterprise platform often presents leaders with compromises: Strong governance or agile, self-service? Deep, powerful capabilities or approachable features for broader use cases? A platform that’s readily scalable or cost-effective? You shouldn’t have to consider trade-offs in order to enable everyone in your organization to make better decisions, faster. And you shouldn’t have to stockpile point solutions to address the spectrum of needs for everyone from your specialized, power users to everyday business users. That’s why we’re making it easier for customers to get value from Tableau and to empower all of their employees to…
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A Guide to Using Power BI for Marketers

A Guide to Using Power BI for Marketers
From sales targets to conversion rates, chances are you track a range of valuable analytics data. After all, this data allows you to create a winning marketing strategy and grow your business. The problem? It’s not always easy to make sense of the data, especially if it’s held across a bunch of different files on […]
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To Protect Consumer Data, Don’t Do Everything on the Cloud

To Protect Consumer Data, Don’t Do Everything on the Cloud
When collecting consumer data, there is almost always a risk to consumer privacy. Sensitive information could be leaked unintentionally or breached by bad actors. For example, the Equifax data breach of 2017 compromised the personal information of 143 million U.S. consumers. Smaller breaches, which you may or may not hear about, happen all the time. As companies collect more data — and rely more heavily on its insights — the potential for data to be compromised will likely only grow.
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Power BI June 2021 Feature Summary

Welcome to the June update! Loads of updates on connectors this time around. Also, DirectQuery support for Dataflows is now generally available! On top of that, we are happy to announce the preview of the paginated reports visual – we know many of you have been eagerly awaiting it, so take it for a spin and provide your feedback! Our Small Multiples and DirectQuery for Power BI datasets and Azure Analysis Services previews are still ongoing and receiving some updates this month.
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How to Build a Data Warehouse Using PostgreSQL in Python?

How to Build a Data Warehouse Using PostgreSQL in Python?
Data warehouse generalizes and mingles data in multidimensional space. The construction or structure of a data warehouse involves Data Cleaning, Data Integration, and Data Transformation, and it can be viewed as an “important preprocessing step for data mining”.
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BigQuery vs Snowflake: A Comparison of Data Warehouse Giants

BigQuery vs Snowflake: A Comparison of Data Warehouse Giants
It's essential to understand data warehousing depending on your requirements and business. Many organizations struggle in selecting the data warehouse that suits them. Hence, people are opting for the BigQuery/Snowflake course to understand data warehousing. Here, we are going to compare the two topmost data warehouses: BigQuery and Snowflake.
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Data Science From Scratch

Data Science From Scratch
Introduction Master Python or R Essentials NowPractice 5–10 Machine Learning Algorithms Explain Modeling to a Non-Data Scientist While there may be a few approaches out there to data science from scratch, I wanted to give my take on it, with the thought of what I would do differently in mind if I were to start over. In my case, I started from scratch, majoring in a field that was not data science, to begin with for my undergraduate degree.
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Data Science and Cloud: A Perfect Match for your Data Analytics needs

Data Science and Cloud: A Perfect Match for your Data Analytics needs
The number of devices connected through the Internet of Things (IoT) is increasing rapidly. Statista estimates that there will be about 50 million IoT-connected devices in use across the world by 2030. And these interconnected devices and enterprise systems will generate vast amounts of data. And, most of this data will be stored and analyzed on the cloud.The number of devices connected through the Internet of Things (IoT) is increasing rapidly. Statista estimates that there will be about 50 million IoT-connected devices in use across the world by 2030. And these interconnected devices and enterprise systems will generate vast amounts of data. And, most of this data will be stored and analyzed on the cloud.
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Dean of Big Data: 2021-2022 Data & Analytics Trends

Dean of Big Data: 2021-2022 Data & Analytics Trends
I’m starting to see the big consultancies and advisory services coming out with their lists of “what’s hot” from a data and analytics perspective.  While I may not have the wide purview of these organizations, I certainly do work with some interesting organizations who are at various points in their data and analytics journey. With that in mind, I’d like to share my perspective as to what I think will be big in the area of data and analytics over the next 18 months.
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Five Data Analytics Mistakes Marketers Make (And How to Avoid Them)

Five Data Analytics Mistakes Marketers Make (And How to Avoid Them)
If marketing were an apple pie, data would be the apples — without data supporting your marketing program, it might look good from the outside, but inside it’s hollow. In a recent survey from Villanova University, 100% of marketers said data analytics has an essential role in marketing’s future. With everyone on board with the importance of data analytics, it’s surprising that as of 2020, only 52.7% of marketers were actually using analytics in their marketing efforts (according to Marketing Evolution), and only 9% of marketers polled by Gartner’s Marketing Data and Analytics Survey said their company has a strong understanding of how to effectively use data analytics.  This illuminates a disconnect: Marketers understand data’s significance, but they don’t know how to use it to best serve their business objectives.…
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Awesome list of datasets in 100+ categories

Awesome list of datasets in 100+ categories
With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.
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Data Warehouse, Data Lake, Data Mart, Data Hub A Definition of Terms

Data Warehouse, Data Lake, Data Mart, Data Hub A Definition of Terms
In today’s business environment, most organizations are overwhelmed with data and looking for a way to tame the data overload and make it more manageable to help team members gather and analyze data and make the most of the information contained within the walls of the enterprise. When a business enters the domain of data management, it can often get lost in a morass of terms and concepts and find it nearly impossible to sort through the confusion. Without a clear understanding of the various categories and iterations of data management options, the business may make the wrong choice or become so mired in the review process that it will give up its quest.
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Azure Synpase Analytics & Power BI

Azure Synpase Analytics & Power BI
Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources at scale.I quite agree with the bit related to freedom. Synapse has all the tools you need for different workloads or requirements. If you need a plain data warehouse, then use dedicated or serverless SQL pools. If you want Spark, then Spark pools are available with friendly notebook experience like Databricks (downside is that latest Spark versions are not there yet). Data factory is also integrated with Synapse workspace so there is no need to jump here and there among different tools. Source control is also baked into the same recipe if you want…
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Announcing support for backup and restore of Power BI datasets

Announcing support for backup and restore of Power BI datasets
We are excited to announce the public preview of Backup and Restore for datasets in Power BI Premium and Premium per User (PPU). You can now use SQL Server Management Studio (SSMS), Analysis Services cmdlets for PowerShell, and other tools to perform backup and restore operations in Power BI via XMLA endpoints in much the same way as you would for tabular models in Azure Analysis Services (Azure AS).
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The 7 Tasks in Data Science Management

The 7 Tasks in Data Science Management
An ever increasing number of use cases for data science are evolving in most companies from nearly every sector. From small businesses to big industries, the number of data scientists is continuously increasing and with them, the size and complexity of data science teams gets bigger. At the same time, it is reported that only a few (22%) data science projects show high revenue and big data projects fail in large numbers (60 to 85%) [Atwal 2020]. This leads to the question: how to deal with the complexity of managing data science teams?
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