Big data, data science and machine learning explained Data are considered the new secret sauce, are everywhere and have been the cornerstone for the success of many high-tech companies, from Google to...

Here is a nice summary of traditional machine learning methods, from Mathworks. I also decided to add the following picture below, as it illustrates a method that was very popular 30 years ago but that seems to have been forgotten recently: mixture of Gaussian. In the example below, it is used to separate the data set… Read More »Machine Learning Summarized in One Picture

Page with free resources for learning data science including whitepapers, infographics and blog posts, lessons, and pages. You can download them right now.

Statistical fallacies are common tricks data can play on you, which lead to mistakes in data interpretation and analysis. Explore some common fallacies, with real-life examples, and find out how yo...

The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information…

Get the new R Cheat Sheet that makes learning data science with R quick and efficient.

A list of the different types of data in statistics, marketing research, and data science. Explanation and examples of data types plus infographics in PDF.

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one… Read More »Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics

To be a real “full-stack” data scientist, or what many bloggers and employers call a “unicorn,” you’ve to master every step of the data science process — all the way from storing your data, to…

Five years ago the McKinsey Global Institute (MGI) released Big Data: The Next Frontier For Innovation, Competition, and Productivity, and in the years since McKinsey sees data science adoption and value accelerate, specifically in the areas of machine learning and deep learning.

What is the difference between structured and unstructured data? Learn about it's application to A.I., blockchain, analytics and search in legaltech.

Though Facebook has been in the spotlight, it's only one part of a complex, multi-billion dollar industry that makes a living from your personal data.

The reciprocal connection between knowledge management and social collaboration platforms would be clear to most. But what about a connection between knowledge management and big data? Using KM can help facilitate additional value from big data.

Almost all the techniques of modern data science, including machine learning, have a deep mathematical underpinning. A solid understanding of a few key topics will give you an edge in the industry.

Venture Scanner: Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data. Deep Learning/Machine Learning (Applications): Companies that utilize computer … Continue reading →

Big Data can be intimidating! If you are new to Big Data, please read ‘What is Big Data’, ‘Who coined Big Data’, ‘Big Data … So what’ to get you started. With the basic concepts under your belt, let’s focus on some key terms to impress your date or boss or family. By the way,… Read More »25 Big Data Terms You Must Know To Impress Your Date (Or whoever you want to)

The answer to the question "What are the two main objectives associated with data mining? " Uncovering data trends and patterns. Explained.

Nowadays, with all these data we consume and generate every single day, algorithms must be good enough to handle operations in large volumes of data. In this post, we will understand a little more…

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Google’s Ngram Viewer exposes Gartner’s Hype Cycles

Today, most business value is derived from the analysis of data and products powered by data, rather than the software itself. The data generated by several application silos are combined and greatly…

The definitions, meaning and types of data and information - and how to unlock value from them: from data to value.

I provide an overview of the data science workflow and highlight some challenges that data scientists face in their work.

Data science has become an integral part of many modern projects and businesses, with an increasing number of decisions now based on data analysis. The data science industry is experiencing an acute shortage of talents, not only of data scientists but also of managers, having some understanding of analytics and data science. As a manager, you… Read More »Intro to Data Science for Managers [Mindmap]

Data Lake and the Data Warehouse. They seemed similar, but there are differences.

Getting a job in data science is as much about finding a company whose needs match your skills as it is developing those data science skills.

Infomages|Images| = 11Infomages Data Science Summarized in One Picture Machine Learning applied to Big Data Machine Learning Summarized in One Picture The Periodic Table Of AI Machine Learning Canv…

The number of people in the C-suite has grown, often including the addition of the Chief Data Officer. If an organization doesn’t have one yet, it likely

If you are tired of all the calculations that are usually involved with statistics, learning about the Student’s T distribution will be right up your alley.

What does 2015 hold for Data Science? Infographic from @CrowdFlower 'What's Hot & What's Not' #datascience #bigdata

This infograph compares the roles of data scientists, data analysts, data architects, data engineers and more in the data science industry.

How best can these two disciplines — data science and design, be combined towards the delivery of best experiences for digital products?

Python and R cheat sheets for machine learning algorithms. It contains codes on data science topics, decision trees, random forest, gradient boost, k means.

Learning data analytics is a challenge for beginners. Take your learning experience of data analytics one step ahead with these nine data analytics books. Explore a range of topics, from big data to artificial intelligence.

FacebookTweetPinLinkedIn What is Data Science? Data Science started with statistics, and has evolved to include concepts/practices such as Artificial Intelligence, Machine Learning, and the Internet of Things, to name a few. Data science makes use of data mining, machine learning, Artificial Intelligence techniques. As more and more data has become available, first by way of […]