The post Deploy Watson Conversation Chatbots to WordPress appeared first on Cognitive Class.

]]>Most people consider chatbots to be in the realm of what only programmers can create, out of reach of business users who would otherwise have a need for them.

Thankfully, IBM provides the Watson Conversation service on their IBM Cloud platform which, combined with our WordPress plugin, solves that.

The plugin provides you with an easy way to deploy chatbots you create with IBM Watson Conversation to WordPress sites. In fact, you may have noticed a floating chatbot icon at the bottom of this page. Click on it to see the plugin in action.

Watson Conversation is IBM’s chatbot service. Its intuitive interface allows chatbot creators to build their chatbot and have it ready to deploy in short time. You can sign up for a free IBM Cloud Lite account to get started.

Building your chatbot won’t be covered in this article but we have a great Chatbot course that guides you through this process and doesn’t require any coding expertise.

This is where the Watson Conversation WordPress plugin saves you time and money. If you have a website built using WordPress, deploying your chatbot to your website takes about 5 minutes and no code at all (as opposed to having to build your own application just to deploy a chatbot on the web.)

You can install it like any other WordPress plugin from your Admin page, that is, the first page you see after signing in.

Just search for Watson Conversation in the “Add New” section of the Plugins page and click “Install Now”.

Now you can find a page for “Watson” in your Settings. This is where you’ll find all the settings and customization to do with the plugin. When you first open it, you’ll see several tabs along the top.

For now, the only one you have to worry about is “Main Setup”.

You can find the credentials for the three required fields on the Deploy page of your Watson Conversation workspace.

Now just click save changes and you’re done. Browse your website and see your chatbot in action!

If you’re not quite satisfied with the appearance, you can customize this in the “Appearance” tab of the settings page.

You can also choose which pages to display the chat box on from the “Behaviour” tab. However, that’s not all you can do.

If you want to make the options clear to the user, you can create predefined responses to the chatbot messages for the users to select. The VOIP feature can connect users to your phone line over the internet from directly within the plugin.

In this brief article, we focused on how to deploy Watson Conversation chatbots to WordPress. Stay tuned for future articles on how to customize and use these exciting advanced features!

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]]>The post From Python Nested Lists to Multidimensional numpy Arrays appeared first on Cognitive Class.

]]>Before you create a Deep Neural network in TensorFlow, Build a regression model, Predict the price of a car or visualize terabytes of data you’re going to have to learn Python and deal with multidimensional data. So this blog post is expanded from our introductory course on Python for Data Science and help you deal with nesting lists in python and give you some ideas about numpy arrays.

Nesting involves placing one or multiple Python lists into another Python list, you can apply it to other data structures in Python, but we will just stick to lists. Nesting is a useful feature in Python, but sometimes the indexing conventions can get a little confusing so let’s clarify the process expanding from our courses on Applied Data Science with Python We will review concepts of nesting lists to create 1, 2, 3 and 4-dimensional lists, then we will convert them to numpy arrays.

Lists are a useful datatype in Python; lists can be written as comma separated values. You can change the size of a Python list after you create it and lists can contain an integer, string, float, Python function and Much more. Indexing for a one-dimensional (1-D) list in Python is straightforward; each index corresponds to an individual element of the Python list. Python’s list convention is shown in figure 1 where each item is accessed using the name of the list followed by a square Bracket. For example, the first index is obtained by A[0]:”0″; the means that the zeroth element of the List contains the string 0. Similarly, the value of A[4] is an integer 4. For the rest of this blog, we are going to stick with integer values and lists of uniform size as you may see in many data science applications.

Lists are useful but for numerical operations such as the ones you will use in data science, Python has many useful libraries one of the most commonly used is numpy.

Numpy is a fast Python library for performing mathematical operations. The numpy class is the “ndarray” is key to this framework; we will refer to objects from this class as a numpy array. Some key differences between lists include, numpy arrays are of fixed sizes, they are homogenous I,e you can only contain, floats or strings, you can easily convert a list to a numpy array, For example, if you would like to perform vector operations you can cast a list to a numpy array. In example 1 we import numpy then cast the two list to numpy arrays:

import nunpy as np u=np.array([1,0]) v=np.array([0,1])

If you check the type of *u* or *v* (**type**(*v*) ) you will get a “numpy.ndarray”. Although **u** and **v** points in a 2 D space there dimension is one, you can verify this using the data attribute “ndim”. For example, v.ndim will output a one. In numpy dimension or axis are better understood in the context of nesting, this will be discussed in the next section. It should be noted the sometimes the data attribute shape is referred to as the dimension of the numpy array.

The numpy array has many useful properties for example vector addition, we can add the two arrays as follows:

z=u+v z:array([1,1])

Where the term “z:array([1,1])” means the variable z contains an array. The actual vector operation is shown in figure 2, where each component of the vector has a different color.

Numpy arrays also follow similar conventions for vector scalar multiplication, for example, if you multiply a numpy array by an integer or float:

y=np.array([1,2]) y=2*z y:array([2,4])

The equivalent vector operation is shown in figure 3:

Like list you can access the elements accordingly, for example, you can access the first element of the numpy array as follows u[0]:1. Many of the operations of numpy arrays are different from vectors, for example in numpy multiplication does not correspond to dot product or matrix multiplication but element-wise multiplication like Hadamard product, we can multiply two numpy arrays as follows:

u=np.array([1,2]) v=np.array([3,2) z=u*v z:array([6,3])

The equivalent operation is shown in figure 4:

Nesting two lists are where things get interesting, and a little confusing; this 2-D representation is important as tables in databases, Matrices, and grayscale images follow this convention. When each of the nested lists is the same size, we can view it as a 2-D rectangular table as shown in figure 5. The Python list “A” has three lists nested within it, each Python list is represented as a different color. Each list is a different row in the rectangular table, and each column represents a separate element in the list. In this case, we set the elements of the list corresponding to row and column numbers respectively.

In Python to access a list with a second nested list, we use two brackets, the first bracket corresponds to the row number and the second index corresponds to the column. This indexing convention to access each element of the list is shown in figure 6, the top part of the figure corresponds to the nested list, and the bottom part corresponds to the rectangular representation.

Let’s see some examples in figure 4, Example 1 shows the syntax to access element A[0][0], example 2 shows the syntax to access element A[1][2] and example 3 shows how to access element A[2][0].

We can also view the nesting as a tree as we did in Python for Data Science as shown in figure 5 The first index corresponds to a first level of the tree, the second index corresponds to the second level.

Turns out we can cast two nested lists into a 2-D array, with the same index conventions. For example, we can convert the following nested list into a 2-D array:

V=np.array([[1, 0, 0],[0,1, 0],[0,0,1]])

The convention for indexing is the exact same, we can represent the array using the table form like in figure 5. In numpy the dimension of this array is 2, this may be confusing as each column contains linearly independent vectors. In numpy, the dimension can be seen as the number of nested lists. The 2-D arrays share similar properties to matrices like scaler multiplication and addition. For example, adding two 2-D numpy arrays corresponds to matrix addition.

X=np.array([[1,0],[0,1]]) Y=np.array([[2,1][1,2]]) Z=X+Y; Z:array([[3,1],[1,3]])

The resulting operation corresponds to matrix addition as shown in figure 9:

Similarly, multiplication of two arrays corresponds to an element-wise product:

X=np.array([[1,0],[0,1]]) Y=np.array([[2,1][1,2]]) Z=X*Y; Z:array([[2,0],[2,0]])

Or Hadamard product:

To perform standard matrix multiplication you world use np.dot(X,Y). In the next section, we will review some strategies to help you navigate your way through arrays in higher dimensions.

We can nest three lists, each of these lists intern have nested lists that have there own nested lists as shown in figure 11. List “A” contains three nested lists, each color-coded. You can access the first, second and third list using A[0], A[1] and A[2] respectively. Each of these lists contains a list of three nested lists. We can represent these nested lists as a rectangular table as shown in figure 11. The indexing conventions apply to these lists as well we just add a third bracket, this is also demonstrated in the bottom of figure 6 where the three rectangular tables contain the syntax to access the values shown in the table above.

Figure 12 shows an example to access elements at index A[0][2][1] which contains a value of 132. The first index A[0] contains a list that contains three lists, which can be represented as a rectangular table. We use the second index i.e A[0][2] to access the last list contained in A[0]. In the table representation, this corresponds to the last row of the table. The list A[0][2] corresponds to the list [131,132,133]. As we are interested in accessing the second element we simply append the index [1]; Therefore the final result is A[0][2][1].

A helpful analogy is if you think of finding a room in an apartment building on the street as shown in Figure 13. The first index of the list represents the address on the road, in Figure 8 this is shown as depth. The second index of the list represents the floor where the room is situated, depicted by the vertical direction in Figure 13. To keep consistent with our table representation the lower levels have a larger index. Finally, the last index of the list corresponds to the room number on a particular floor, represented by the horizontal arrow.

For example, in figure 9 the element in the list A[2][2][1]: corresponds to building 2 on the first floor the room is in the middle, the actual element is 332.

The mathematical operations for 3D numpy arrays follow similar conventions i.e element-wise addition and multiplication as shown in figure 15 and figure 16. In the figures, X, Y first index or dimension corresponds an element in the square brackets but instead of a number, we have a rectangular array. When the add or multiply X and Y together each element is added or multiplied together independently. More precisely each 2D arrays represented as tables is X are added or multiplied with the corresponding arrays Y as shown on the left; within those arrays, the same conventions of 2D numpy addition is followed.

Adding another layer of nesting gets a little confusing, you cant really visualize it as it can be seen as a 4-dimensional problem but let’s try to wrap our heads around it. Examining, figure 17 we see list “A” has three lists, each list contains two lists, which intern contain two lists nested in them. Let’s go through the process of accessing the element that contains 3122. The third element A[2] contains 2 lists; this list contains two lists in figure 10 we use the depth to distinguish them. We can access the second list using the second index as follows A[2][1]. This can be viewed as a table, from this point we follow the table conventions for the previous example as illustrated in figure 17.

We can also use the apartment analogy as shown in figure 18 this time the new list index will be represented by the street name of 1st street and 2nd street. As before the second list index represents the address, the third list index represents the floor number and the fourth index represents the apartment number. The analogy is summarized in Figure 11. For example directions to element A[2][1][0][0] would be 2nd Street , Building 1, Floor 0 room 0.

We see that you can store multiple dimensions of data as a Python list. Similarly, a Numpy array is a more widely used method to store and process data. In both cases, you can access each element of the list using square brackets. Although Numpy arrays behave like vectors and matrices, there are some subtle differences in many of the operations and terminology. Finally, when navigating your way through higher dimensions it’s helpful to use analogies.

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]]>The post Data Science Survey: The Results Are In! appeared first on Cognitive Class.

]]>2,233 people participated in the survey. This is a statistically significant participation for our students, but not the Data Science community in general. Among other factors, the Cognitive Class’ catalog of courses influences who we attract to our site and ultimately who responded to the survey.

We presented respondents with eight data-related technologies and asked them to express their level of interest for each of them. The chart below shows the results.

As expected, there is a high degree of interest (green bars) for Data Science, Big Data, and AI. Virtually everyone showed some degree of interest for these three categories.

Participants showed relatively low interest in hot technologies such as Blockchain, Virtual Reality, and Chatbots. I was somewhat surprised by this result. Though, as the author of our first Chatbot course and an enthusiast of cutting edge technology, I might be biased.

Perhaps, our learners are primarily professionals who might not have yet a concrete business application for these emerging, but still green, technologies. But this is just speculation, of course.

Our second question drilled down to the Data Science field, asking about the level of interest for specific areas of Data Science.

The data shows a strong interest in all areas of Data Science, exception made for Data Journalism which received a lukewarm response. If you are interested in this topic, I highly recommend taking our Data Journalism course. Storytelling is underrated and I think it will benefit your Data Science career, even if you aren’t a journalist.

Our third question narrowed the scope further to the programming language of choice for Data Science.

Almost half of the respondents use or have an interest in Python for Data Science. R and SQL sit strong at 20.96% and 12.4%, respectively. No huge surprises here, but I was expecting Scala to have the fourth place. Instead, Java appears to be ahead of it, with JavaScript in 6th place, beating by a wide margin Julia.

Julia is actually a fantastic language for Data Science and I’d love to see it grow in popularity. Its performance characteristics alone are noteworthy. Unfortunately, it’s still somewhat niche in the Data Science community in general, and clearly among our students. (If you’d like to change this by authoring a course on the subject, feel free to get in touch with us.)

What’s interesting about this question is the fact that we allowed an open-ended Other option. As a result, we truly experienced the diversity of languages people adopt to perform Data Science in. In fact, our respondents also mentioned C#, Clojure, Perl, C, and a few others programming languages.

Finally, we asked about the primary tool or IDE of choice.

Respondents could only pick their most used tool, so it’s not surprising to see Hadoop and Spark do so well among our respondents, who showed a clear inclination for Big Data.

RStudio is also fairly popular at 15.99%, a figure somewhat in line with the results of the previous question. The primary R tool is more popular than any other Python tool among our respondents.

Please note that there is no contradiction here. Python users simply had more choices available, splitting the vote between IBM DataScience Experience (IBM DSX for short), Anaconda, and Jupyter. Combined, over 35% of respondents selected Python tools as their primary tool for Data Science, confirming that Python is at least twice as popular as R among our users.

There you have it. It will be interesting to see how these change over time. In the meantime, feel free to play with the data yourself by using the Jupyter notebook created by my colleague Alex Aklson, author of the excellent Data Visualization with Python course.

If you enroll in his course, you’ll have access to our Labs environment to run the Data Science Survey notebook in the cloud, without having to install anything on your machine. Alternatively, you can sign up with a professional Data Science tool like IBM Data Science Experience.

Since most of our respondents showed a great deal of interest in Data Science with Python and Big Data, allow me to recommend a couple of resources useful to learn more about these topics:

**Applied Data Science with Python (Learning Path)****Big Data Fundamentals (Learning Path)**

And if your interest lies elsewhere, feel free to check out our other learning paths and courses. All available for free.

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]]>The post Cognitive Class Uses Machine Learning to Help SETI Find Little Green Men appeared first on Cognitive Class.

]]>The goal of the event was to help the SETI Institute develop new signal classification algorithms and models that can aid in the radio signal detection efforts at the SETI Institute’s Allen Telescope Array (ATA) in Northern California.

Our Chief data scientist Saeed Aghabozorgi developed several Jupyter notebooks including one to transform the signals into spectrograms using a Spark cluster. In addition, Saeed provided several Tensorflow notebooks, one of which used a Convolutional Neural Network [1] to classify the Spectrogram. Check out the Github page and see all the scripts from Saeed Aghabozorgi , Adam Cox and Patrick Titzler.

Our developer Daniel Rudnitski developed a scoreboard that evaluates everyone’s algorithms. The scoreboard works by comparing the predicted results and the true labels in a holdout set for which the participants did not know the labels (shown in Figure 1). I gave a tutorial on Neural Networks and Tensorflow, helped the participants debug their code, and enjoyed the free food.

Figure 1: Cognitive Class’ leaderboard used to assess results of Hackathons

SETI searches for E.T. by scanning star systems with known exoplanets. The idea is that nature does not produce sine waves, therefore the system looks for narrow-band carrier waves like sign waves. The detection system sometimes triggers on signals that are not narrow-band signals. The goal of the event was to classify these signals accurately in real-time, allowing the signal detection system to make better informed observational decisions. [2]

We transformed the observed time-series radio signals into a spectrogram. A spectrogram is a 2-dimensional chart that represents how the power of the signal is distributed over time and frequency [3]. An example is shown in Figure 2. The top chart is a spectrogram in which the bright green represents higher intensity values, and the blue represents low intensity values. The bottom chart contains two amplitude modulated signals labeled A and B. The two brightly colored patches in the spectrogram directly above the signal represent the distribution of the signal energy in time and frequency. The horizontal axis represents time, while the vertical axis represents frequency. If we examine signal A we see that it oscillates at a much lower rate than signal B, meaning that it has a much lower frequency. This is exhibited by a much lower location of the energy on the vertical axis of the Spectrogram.

Fig 2: Spectrogram (top) of two amplitude modulated Gaussian signals (bottom)

The 2D representation provided by the spectrogram allows us to change the problem into a visual recognition problem. Allowing us to apply methods such as convolutional neural networks. Individuals without expertise in design and implementing Deep Neural Networks could focus on the signal processing problem and let IBM Watson Visual Recognition tool handle the complex problem of image classification. The process is demonstrated in figure 3 with a Chirp signal (a signal in which the frequency increases or decreases over time). After the spectrogram, several convolutional layers are applied to extract features from the image, then the output is flattened and placed as inputs into a fully connected neural network. To learn more about deep learning check out our Deep Learning 101 and Deep Learning with TensorFlow courses.

Figure 3: Example architecture used in the event. (Source: Wikipedia)

To speed up the process of developing and testing these neural network, participants were given access to GPUs on IBM PowerAI Deep Learning. Participants used libraries such as Caffe, Theano, Torch, and Tensorflow. In addition, given the vast amounts of data for signal processing, participants were also given access to an IBM Apache Spark Enterprise cluster. For example, the spectrograms where calculated on several nodes as shown in figure 4.

Figure 4: Example architecture used in the event.

The top team was *Magic AI*. This team used a wide neural net, a network that has less layers than a deep network, but more neurons per layer. According to Jerry Zhang, a Graduate Researcher at UC Berkeley Radio Astronomy Lab, the spectrogram exhibited less complex shapes then a standard image like those in Modified National Institute of Standards and Technology database (MIST), as a result less convolutional layers where required to encode features like edges. We see this by examining figure 5, the left image shows 5 spectrograms and the right image shows 5 images from MIST. The Spectrogram is colored using the standard gray scale where white represents the largest values and black represents the lowest values. We see the edges of the spectrogram are predominantly vertical and straight while the numbers exhibit horizontal lines, parallel lines, arches and circles.

Figure 5: Spectrograms and the right image shows 5 images from MIST

The Best Signal Processing Solution was by the *Benders*. They applied a method for detecting earthquakes to improve signal processing. Arun Kumar Ramamoorthy, one of the members, also made an interesting discovery while plotting out some of the data points. Check out their blog post here.

The prize for best Non Neural Network/Watson: went to team *Explorers* and most Interesting went to team *Signy McSigFace*. The trophies are shown in Figure 6.

Figure 6: Custom trophies designed for winners of this hackathon.

The weekend was quite interesting with talks from , Dr. Jill Tarter, Dr. Gerry Harp, and Jon Richards who gave talks about SETI, the radio data processing and operations. They were also available to answer questions from participants. Kyle Buckingham gave a talk about the radio telescope he built in his backyard! Everyone who participated is shown in the image below.

Figure 7: SETI Hackathon participants

Check out the event GitHub page: https://github.com/setiQuest/ML4SETI/

For more information on SETI, please check out: https://www.seti.org/

To donate to SETI: https://www.seti.org/donate/astrobiology-sb

Would you like to make your own predictions? Learn about Deep Learning with our Deep Learning 101 and Deep Learning with TensorFlow courses.

**References**

[1] Krizhevsky, Alex, Ilya Sutskever, and. Hinton Geoffrey E ,. “Imagenet classification with deep convolutional neural networks.” *Advances in neural information processing systems*. 2012.

[2] Aghabozorgi, Saeed with Cox, Adam and Titzler, Patrick,. ML4SETI https://github.com/setiQuest/ML4SETI

[3] Cohen, Leon. “Time-frequency distributions-a review.” *Proceedings of the IEEE* 77.7 (1989): 941-981.

The post Cognitive Class Uses Machine Learning to Help SETI Find Little Green Men appeared first on Cognitive Class.

]]>The post We’re Now Cognitive Class appeared first on Cognitive Class.

]]>One has been called “the sexiest profession of the 21st century” and the other is the biggest revolution in the world of computing since the invention of the Turing Machine.

When we – a small group of IBMers – first launched this volunteer initiative back in 2010, we chose to call the site DB2 University. Our goal at the time was to provide online data education and literacy free of charge. Our goal hasn’t changed; our name, implementation, and the industry itself have.

Most of our team had worked on DB2 at IBM and so we launched the site with a handful of courses related to SQL and specifically IBM DB2. Despite the limited amount of content at the time, we quickly managed to attract the attention of many professionals and students.

As the number of registrants grew, we quickly realized that our mission for the site would be to democratize learning beyond the scope of a single product.

Seven years ago our students were expressing interest in learning not just about databases but also about this hot new thing called Big Data. Our own team’s scope at IBM expanded to include emerging technologies in the cloud and analytics space. Big Data very much included. So in 2011, we renamed DB2 University to **Big Data University**.

We kept that name for six years and it has served us well. Our Big Data courses are extremely popular and the site has grown to just shy of** 700,000 learners worldwide**.

Our catalog expanded considerably and, despite our name, the site became an education and skill building tool for more than just Big Data. For Data Science at first, and in more recent times, for Cognitive Computing as well. (By the way, the latter is IBM’s favorite terminology for technologies that include, but are not limited, to Artifical Intelligence.)

Our industry never sits still. Teaching data analytics today is no longer just about relational databases. And it’s no longer about Big Data alone either.

Those two topics remain foundational, but the future of data insight that serves human and business needs lies in Data Science and Cognitive Computing.

As we go about creating more and more content that satisfies your demands for future-proof skills, we decided to double down on them. And with this new focus in mind, we had to pick a better name to represent our aim. We chose Cognitive Class.

The domain is a .ai to further reinforce what we are all about.

Realistically, not much should change for you in terms of logistics. Our emails will come from Cognitive Class rather than BDU. Your account will still work. The courses you’re enrolled in and completed will still be there. You keep your certificates and badges. (However, if you run into any issue, please use our Support tab to open a ticket with us.)

You can expect us to release many new courses on hot topics within our industry. Think machine learning, deep learning, AI, NLP, chatbots, blockchain, and so on.

We look forward to continuing to provide you with the indispensable skills you need to tackle the problems of the future. Please stay tuned.

PS: If you’re interested in becoming a course author, feel free to contact us. If you are a business or academic institution interested in deploying your own Cognitive Class, reach us via the Business page.

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]]>The post This Week in Data Science (May 30, 2017) appeared first on Cognitive Class.

]]>Don’t forget to subscribe if you find this useful!

- Managing Spark data handles in R – How to work with data handles and Spark in R.
- 5 ways to measure running time of R code – A quick rundown of five ways R code can be benchmarked.
- Free Data Science Resources for Beginners – A list of 65 resources to for beginners in Data Science.
- R Packages worth a look – A short list of lesser known but useful R packages.
- Google, IBM, and Lyft launch open source project Istio – The first public release of the open source service that developers a vendor-neutral way to work with networks of different microservices on cloud platforms.
- R vs Python: Different similarities and similar differences – A look at how the two languages compare.
- The Major Do’s and Don’ts of Data Visualization – Some guidelines to creating an effective and successful Data Visualization.
- Machine Learning Workflows in Python from Scratch Part 1: Data Preparation – Implementing a Machine Learning Workflow from scratch.
- Arranging subplots with ggplot2 –Brief tutorial to working with subplots in R’s ggplot2 package.
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- Big Data Toronto 2017 –June 20, 2017 – June 21, 2017 all-day Metro Toronto Convention Center, 255 Front Street West Toronto, Ontario M5V 2W6 Canada

- SQL and Relational Databases 101 – Learn the basics of the database querying language, SQL.
- Big Data 101 – What Is Big Data? Take Our Free Big Data Course to Find Out.
- Predictive Modeling Fundamentals I – Take this free course and learn the different mathematical algorithms used to detect patterns hidden in data.
- Using R with Databases – Learn how to unleash the power of R when working with relational databases in our newest free course.
- Deep Learning with TensorFlow – Take this free TensorFlow course and learn how to use Google’s library to apply deep learning to different data types in order to solve real world problems.

- Big Data Toronto 2017 –June 20, 2017 – June 21, 2017 all-day Metro Toronto Convention Center, 255 Front Street West Toronto, Ontario M5V 2W6 Canada

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]]>The post This Week in Data Science (May 23, 2017) appeared first on Cognitive Class.

]]>Don’t forget to subscribe if you find this useful!

- General Tips for Web Scraping with Python – Thoughts and tips about scraping data from websites for personal use.
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- What is optimization and how it improves planning outcomes – An discussion about the goals of Optimization and its importance.
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- Must-Know: What are common data quality issues for Big Data and how to handle them? – A look into the common issues facing Big Data.
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- Do I Need An Advanced Degree To Become A Data Scientist? – Quora response to the question of data science and education level.

- Data Science: Basic Statistics and Simulation Modeling in Python (Hands-On) – May 25, 2017 @ 6:00 pm – 8:30 pm; Lighthouse Labs 46 Spadina Avenue Toronto

- SQL and Relational Databases 101 – Learn the basics of the database querying language, SQL.
- Big Data 101 – What Is Big Data? Take Our Free Big Data Course to Find Out.
- Predictive Modeling Fundamentals I – Take this free course and learn the different mathematical algorithms used to detect patterns hidden in data.
- Using R with Databases – Learn how to unleash the power of R when working with relational databases in our newest free course.
- Deep Learning with TensorFlow – Take this free TensorFlow course and learn how to use Google’s library to apply deep learning to different data types in order to solve real world problems.

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]]>Don’t forget to subscribe if you find this useful!

- General Tips for Web Scraping with Python – Tips on scrapping and saving data from the web.
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Data Visualization. - Top 3 ways to measure the success of your analytics investment – Three factors to consider when evaluating technologies that aid in business decisions.
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- IBM pushes for NVMe adoption to boost storage speeds – Why the adoption of NVMe is necessary for today’s vast amounts of data.

In case you missed it: April 2017 roundup – A look back at all the stories from Revolutions R blog.- Machine Learning Pipelines for R – How the R package pipeliner helps to streamline the process of building machine learning and statistical models.
- Machine Learning. Linear Regression Full Example (Boston Housing). – Short tutorial on performing linear regression on a data set.

- SQL and Relational Databases 101 – Learn the basics of the database querying language, SQL.
- Big Data 101 – What Is Big Data? Take Our Free Big Data Course to Find Out.
- Predictive Modeling Fundamentals I – Take this free course and learn the different mathematical algorithms used to detect patterns hidden in data.
- Using R with Databases – Learn how to unleash the power of R when working with relational databases in our newest free course.
- Deep Learning with TensorFlow – Take this free TensorFlow course and learn how to use Google’s library to apply deep learning to different data types in order to solve real world problems.

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- The Colors Used by the Ten Most Popular Sites– Data Visualization showing plots of the colors used by some of the most popular sites.
- 42 Essential Quotes by Data Science Thought Leaders – Quotes from industry leaders giving insight into problems faced by those in the field.
- Warp your data to make it visually appealing – A short tutorial on how to create visually interesting plots without change important statistical properties.
- Deep Learning intuition for a business user – Deep Learning from a business leader perspective.
- Machine Learning overtaking Big Data? – A look at the different trends around ML and Big Data and other fields.
- Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing –An illustration and explanation of the importance data visualization.
- Deep Learning – Past, Present, and Future – A brief discussion of the field of Deep Learning and its impact.
- Infographic: Cloud use is growing, but the data center isn’t dead yet – The results of a survey on cloud usage among IT professionals and its implications.
- Gartner publishes Magic Quadrant for Sales Performance Management 2017: IBM identified as a Leader – IBM is apart of the ‘Leader’ quadrant of the Gartner Magic Quadrant for the fourth consecutive year.
- MIT Step by Step Instructions for Creating Your Own R Package. – A guide to creating a package for R using RStudio.
- IBM’s Watson wants to help you make a movie – The Storytellers With Watson competition for artists who believe Watson may assist in their creativity.
- Which deep learning network is best for you? – A comparison of some open source frameworks.
- The key to identifying the next big threat: Data analytics & cybersecurity – A discussion about the use of data avert cyberattacks.
- R Packages worth a look – A look at some useful R packages.
- Leveraging Big Data and Machine Learning – How Trulia uses Big Data and Machine Learning to provide a better user experience.
- IBM says big data has provided new insight into how Ebola spread – IBM employs Big Data analytics to investigate the spread of Ebola among animal carriers.

- SQL and Relational Databases 101 – Learn the basics of the database querying language, SQL.
- Big Data 101 – What Is Big Data? Take Our Free Big Data Course to Find Out.

- CBF New York – Foursquare API – May 11, 2017 @ 5:30 pm – 7:00 pm

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- Video Roundup: New from IBM Watson – A brief run down of some new things IBM Watson is tackling.
- Five Missteps to Avoid on your First Big Data Journey. – Steps to take in order to avoid common Big Data pitfalls.
- How Machine Learning Is Changing The Future Of Digital Businesses – How Machine Learning impacts automation and digital transformation.
- Hacking maps with ggplot2 – Short look at mapping with the R package ggplot2.
- AI & Machine Learning Black Boxes: The Need for Transparency and Accountability – The importance of comprehending the inner workings Machine Learning Algorithms.
- How just 30 machines beat a warehouse-sized supercomputer to set a new world record – IBM partners with Nvidia to showcase the ability of massively parallel processing on GPUs.
- Data Analytics Is The Key Skill For The Modern Engineer – How engineers can embrace Data Analytics to streamline business operations and task integration.
- Building and Exploring a Map of Reddit with Python – A tutorial on how to explore a map of the most popular subreddits with python.
- Data scientists really love their jobs, survey finds – The results of a survey showing how satisfied Data Scientists are with their jobs.
- Reproducible Data Science with R – A presentation on the application of a Reproducible Workflow to Data Science in R.
- IBM uses deep learning to better detect a leading cause of blindness – IBM has made another application of cognitive computing to the medical field.
- Awesome Deep Learning: Most Cited Deep Learning Papers – A list of fairly recent must read publications on Deep Learning.
- Emotion Detection Using Machine Learning – An example of the use of Deep Learning to perform feature extractions.
- Plotting Data Online via Plotly and Python – Introductory steps to creating plots with Plotly.
- Machine Learning Classification Using Naive Bayes – A classification exercise using the Naive-Bayes algorithm in R.
- The Art of Data –How Watson, fed with data about different subjects, helped to create art.

- SQL and Relational Databases 101 – Learn the basics of the database querying language, SQL.
- Big Data 101 – What Is Big Data? Take Our Free Big Data Course to Find Out.

- UofT Data Science Workshop: Intro to Clustering with R – May 2, 2017 @ 6:00 pm – 9:00 pm
- UofT Data Science Workshop: Intro to Classification with R –May 4, 2017 @ 6:00 pm – 7:00 pm
- IBM Webinar: Charting Your Analytical Future Webinar: Get the best of Self-service Analytics and Managed reporting together – May 4, 2017 @ 12:00 pm – 1:00 pm

- Machine Learning With Python – Collaborative Filtering & Its Challenges – An Exploration of Collaborative Filtering Techniques.
- Machine Learning With Python – Course Summary – A review of the BDU course Machine Learning 101.

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