May, 2019

Designing Charts and Graphs: How to Choose the Right Data Visualization Types

datapine

Modern dashboard software makes it simpler than ever to merge and visualize data in a way that’s as inspiring as it is accessible. But while doing so is easy, a great dashboard still requires a certain amount of strategic planning and design thinking.

Research quality data and research quality databases

Simply Statistics

When you are doing data science, you are doing research. You want to use data to answer a question, identify a new pattern, improve a current product, or come up with a new product.

Moving from reactive analytics to proactive analytics

Corinium

Becoming a machine learning company means investing in foundational technologies

O'Reilly on Data

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year.

Build Product Progress with a Strong Data Culture

Speaker: Nima Gardideh, CTO, Pearmill

Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you. Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.

Statistics 101: Introduction to the Central Limit Theorem (with implementation in R)

Analytics Vidhya

Introduction What is one of the most important and core concepts of statistics that enables us to do predictive modeling, and yet it often. The post Statistics 101: Introduction to the Central Limit Theorem (with implementation in R) appeared first on Analytics Vidhya.

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Top 5 Tips For Conducting Successful BI Projects With Examples & Templates

datapine

BI 253

Five benefits of data analytics for your sales branches

Phocas

Your branches are the frontline of your business, leading the charge when it comes to customer interaction and building customer loyalty.

Sales 187

4th Industrial Revolution, What is it? And What Impact does it Actually have?

Corinium

Industry 4.0…. it is just a buzz word thrown about? What does it actually mean? And how does it have an impact? We went straight to the source and asked the founding father and driving force behind the Industry 4.0 workgroup- Henrik von Scheel. What exactly is the Fourth Industrial Revolution

IT 195

Applications of data science and machine learning in financial services

O'Reilly on Data

The O’Reilly Data Show Podcast: Jike Chong on the many exciting opportunities for data professionals in the U.S. and China. In this episode of the Data Show , I spoke with Jike Chong , chief data scientist at Acorns , a startup focused on building tools for micro-investing.

What Is (and Isn’t) Product Management?

Speaker: Steve Johnson, VP of Products, Pragmatic Institute

Product Management is one of the most exciting - and most misunderstood - functions in technical organizations. Is it strategic or tactical? Is it a planning role or a support role? Many product professionals are unclear about what is (and isn't) product management. After all, product management spans many activities from business planning to market readiness. In this session, we’ll examine many product activities and artifacts for product strategy, planning, and growth, and introduce a simple tool that you can use in your organization to clarify the roles of product management and others. Steve Johnson explores the many roles of Product Management in this fun talk focused on why product managers should obsess on problems instead of solutions.

10 Useful Data Analysis Expressions (DAX) Functions for Power BI Beginners

Analytics Vidhya

Introduction We have worked on plenty of drag-and-drop tools in our business intelligence (BI) journey. But none has come close to matching the Swiss. The post 10 Useful Data Analysis Expressions (DAX) Functions for Power BI Beginners appeared first on Analytics Vidhya.

BI 202

How Artificial Intelligence Will Disrupt the Financial Sector

DataFloq

Artificial intelligence thrives with data. The more data you have, the better your algorithms will be. However, just having a lot of data is not sufficient anymore.

Top 14 Must-Read Data Science Books You Need On Your Desk

datapine

“Big data is at the foundation of all the megatrends that are happening.” – Chris Lynch, big data expert. We live in a world saturated with data. At present, around 2.7

How to compare multiple data streams: actual sales v budget v stretch budget

Phocas

Comparison of sales information against targets has many benefits such as allowing individuals to be more strategic, more motivated and more in control of their actions.

Sales 177

Dresner Advisory Services’ 2019 Wisdom of Crowds Data Catalog Market Study

The 3rd annual Dresner 2019 Wisdom of Crowds® Data Catalog Market Study explores the strong link between data catalogs and successful BI usage. Learn about the core set of capabilities that make data catalogs critical for self-service analytics.

Snowflake: 3 Benefits of a Self-Adapting Data Warehouse

Corinium

With the rise of new data streams, the ability to access more data and derive insights from it more quickly is critical. By 2023, worldwide revenue for big data solutions will reach $260 billion.*

Sustaining machine learning in the enterprise

O'Reilly on Data

Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning. Continue reading Sustaining machine learning in the enterprise

A Beginner’s Guide to Tidyverse – The Most Powerful Collection of R Packages for Data Science

Analytics Vidhya

Introduction Data scientists spend close to 70% (if not more) of their time cleaning, massaging and preparing data. That’s no secret – multiple surveys. The post A Beginner’s Guide to Tidyverse – The Most Powerful Collection of R Packages for Data Science appeared first on Analytics Vidhya. Data Science Data Visualization R data cleaning data preprocessing data science data visualization ggplot r packages tidy tidyverse

A Guide to a Career In Big Data

DataFloq

Some might feel that data science is intimidating. This is particularly the case when someone is just getting started. They might wonder what tool should they start learning such as R or maybe Python. They may feel unsure about what techniques they should put the majority of their focus on.

The Magic of Intent: Start Knowing The Goals of Your Users

Speaker: Terhi Hanninen, Senior Product Manager, Zalando, and Dr. Franziska Roth, Senior User Researcher, Zalando

It's important to know your users - what are their preferences, pain points, ultimate goals? With user research and usage data, you can get a great idea of how your users act. The tricky part is, very few users reliably act the same way every time they use your product. Join Terhi Hanninen, Senior Product Manager, and Dr. Franziska Roth, Senior User Researcher at Zalando, as they explain how they were able to reach a new level of user understanding - by taking their user research and segmenting their users by point-in-time intent. You'll leave with a strategy to change how your product team, and organization at large, understands your users.

Introduction To The Basic Business Intelligence Concepts

datapine

“Without big data, you are blind and deaf and in the middle of a freeway.” – Geoffrey Moore, management consultant, and author.

Use Collaboration to Maximize the Value of Your Analytics

David Menninger's Analyst Perspectives

About 10 years ago, social media tools like Facebook, Twitter and LinkedIn introduced a wave of collaborative analytics and BI capabilities. We saw chat streams associated with specific analyses that users could like or endorse.

BI 177

Exclusive Q&A with Phillipa Cameron, CCO of Stuff NZ

Corinium

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195

Real-time entity resolution made accessible

O'Reilly on Data

The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies. In this episode of the Data Show , I spoke with Jeff Jonas , CEO, founder and chief scientist of Senzing , a startup focused on making real-time entity resolution technologies broadly accessible.

Your 2-Part Metrics Audit for High-Value Products

Speaker: Sam McAfee, Product Development Consultant, Startup Patterns

You know what they say: what's measured improves. As product managers we're in a golden age of being able to get all sorts of metrics and run all sorts of experiments. But what are your measurements and analytics focused on? Are they really truly objective? Do they contribute to the ultimate vision of your product? And is everybody clear on that vision? Join Sam McAfee, Product Development Consultant, as he takes you through a two-part measurement audit. First, you'll learn how to make sure your measurements actually align with your product strategy. And second, you'll learn how to evaluate your culture of using measurements, so future experiments will more consistently provide high-value results.

Data Science Project: Scraping YouTube Data using Python and Selenium to Classify Videos

Analytics Vidhya

This article was submitted as part of Analytics Vidhya’s Internship Challenge. Introduction I’m an avid YouTube user. The sheer amount of content I can. The post Data Science Project: Scraping YouTube Data using Python and Selenium to Classify Videos appeared first on Analytics Vidhya. Machine Learning Python classification data collection Data Extraction machine learning python random forest scraping selenium web scraping

Machine Learning Will Be the Next Big Thing in Supply Chain Management

DataFloq

Supply chain management, or SCM, is becoming a more critical job every year, as more consumers turn to e-commerce and warehouses and distribution centers grow. Technology is catching up to the exponential expansion of this industry, but it's been a slow process.

How Restaurant Analytics Can Make Your Business More Profitable

datapine

The restaurant industry is one of the most competitive sectors on the planet. Not only do we as a species need to eat and drink to survive; communal dining is an experience that people have cherished for centuries.

Big Data for Business: A Requirement for Today’s Business Analytics

David Menninger's Analyst Perspectives

Organizations now must store, process and use data of significantly greater volume and variety than in the past.

Measure the Immeasurable: Beyond Vanity Metrics

Speaker: Sari Harrison, Product Management Instructor, Product School

As a product manager, it's your job to realize your product’s vision by executing your chosen strategy. It’s also your job to deliver value to the business. Ultimately, these two outcomes are aligned so the temptation is to focus primarily on business metrics. Doing this can cause you to lose focus on the real value you are trying to achieve, in favor of moving the vanity metrics such as launches and time spent. Join Sari Harrison, Product Management Instructor at Product School, as she explains how to use immeasurable success criteria along with your more standard KPIs to deliver products that don't just get used a lot, but deliver real value.

Facebook Insight - Connecting your Data Science & Marketing Teams

Corinium

Over it's lifetime Facebook has become possibly the biggest B2C/B2B marketing channel available to marketers. The platform is a mass generator of big customer data that holds immense potential value for making smarter marketing decisions.

Highlights from the Strata Data Conference in London 2019

O'Reilly on Data

Watch highlights from expert talks covering machine learning, predictive analytics, data regulation, and more. People from across the data world are coming together in London for the Strata Data Conference. Below you'll find links to highlights from the event. Making the future.

A Practical Introduction to Prescriptive Analytics (with Case Study in R)

Analytics Vidhya

This article was submitted as part of Analytics Vidhya’s Internship Challenge. Introduction “What are the different branches of analytics?” ” Most of us, when we’re. The post A Practical Introduction to Prescriptive Analytics (with Case Study in R) appeared first on Analytics Vidhya. Business Analytics Data Science R data science prescriptive analytics