Data Analytics

In this article, we introduce a few tools and techniques for studying relationships between the stock market and the news. We explore time series processing, anomaly detection, and an event-based view of the news. We also generate intuitive charts to demonstrate some of these concepts, and share the code behind all of this in a notebook. You can view the code in this notebook.

Reflection and lessons from my performance review as a first-year data scientist. “Pain plus reflection equals progress”: a famous quote by Ray Dalio, investor and founder of the largest hedge fund in the world. The quote implies the pursuit of truth at all costs, or in other words, having an open mind and disconnecting ourselves from our ego when listening to other people’s criticism about us. It is the only way that we can truly learn from our mistakes and objectively assess our own strengths and weaknesses. It is with this in mind that I dedicate this blog post as a reflection on the performance review that I received recently which marks the end of my first year working as a graduate data scientist.

A video replay for this webinar is available at the bottom of the blog. In this webinar, our analytics team covers what data prep looks like in a modern analytics stack and makes suggestions for a solid data prep environment. When you’re going through the initial data prep process, there are four distinct steps that guide you from start to finish: exploring, cleaning, enriching and shaping: While this isn’t a comprehensive list, it’s a good bird’s eye view of what a standard data prep process might look like. It’s not out of the ordinary for analysts to spend well over 50% of their development time on data prep, and sometimes it can go even higher. A common statistic thrown around is that 80% of work time is data prep.

Welcome to the June 2022 update. We are excited to announce the general availability of the new Format Pane, error bars and information protection updates, table navigation improvements, Connect to datamarts and Power BI Data Storytelling features. There is more to explore, please continue to read on.

Since Google Data Studio’s beginnings in 2016, new features and updates have been rolling out frequently to improve the platform and move the needle on data visualization. More importantly, the Data Studio team has always listened to the community and taken its feedback seriously. As a result, Data Studio has quickly become one of the most used and insightful data visualization tools out there. Unfortunately—but fortunately for those of you reading—most people only know how to use the basic features in Google Data Studio and are not aware of certain advanced capabilities that can take their data game to the next level.

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…

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 […]

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…

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.

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 […]

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.

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.

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”.

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.

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.

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.

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.

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.…

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.

In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.