Audit Facebook and overhaul competition law, say MEPs responding to breach scandals

After holding a series of hearings in the wake of the Facebook -Cambridge Analytica data misuse scandal this summer, and attending a meeting with Mark Zuckerberg himself in May, the European Union parliament’s civil liberties committee has called for an update to competition rules to reflect what it dubs “the digital reality”, urging EU institutions […]

After holding a series of hearings in the wake of the Facebook -Cambridge Analytica data misuse scandal this summer, and attending a meeting with Mark Zuckerberg himself in May, the European Union parliament’s civil liberties committee has called for an update to competition rules to reflect what it dubs “the digital reality”, urging EU institutions to look into the “possible monopoly” of big tech social media platforms.

Top level EU competition law has not touched on the social media axis of big tech yet, with the Commission concentrating recent attention on mobile chips (Qualcomm); and mobile and ecommerce platforms (mostly Google; but Amazon’s use of merchant data is in its sights too); as well as probing Apple’s tax structure in Ireland.

But last week Europe’s data protection supervisor, Giovanni Buttarelli, told us that closer working between privacy regulators and the EU’s Competition Commission is on the cards, as regional lawmakers look to evolve their oversight frameworks to respond to growing ethical concerns about use and abuse of big data, and indeed to be better positioned to respond to fast-paced technology-fuelled change.

Local EU antitrust regulators, including in Germany and France, have also been investigating the Google, Facebook adtech duopoly on several fronts in recent years.

The Libe committee’s call is the latest political call to spin up and scale up antitrust effort and attention around social media. 

The committee also says it wants to see much greater accountability and transparency on “algorithmic-processed data by any actor, be it private or public” — signalling a belief that GDPR does not go far enough on that front.

Libe committee chair and rapporteur, MEP Claude Moraes, has previously suggested the Facebook Cambridge Analytica scandal could help inform and shape an update to Europe’s ePrivacy rules, which remain at the negotiation stage with disagreements over scope and proportionality.

But every big tech data breach and security scandal lends weight to the argument that stronger privacy rules are indeed required.

In yesterday’s resolution, the Libe committee also called for an audit of the advertising industry on social media — echoing a call made by the UK’s data protection watchdog, the ICO, this summer for an ‘ethical pause‘ on the use of online ads for political purposes.

The ICO made that call right after announcing it planned to issue Facebook with the maximum fine possible under UK data protection law — again for the Cambridge Analytica breach.

While the Cambridge Analytica scandal — in which the personal information of as many as 87 million Facebook users was extracted from the platform without the knowledge or consent of every person, and passed to the now defunct political consultancy (which used it to create psychographic profiles of US voters for election campaigning purposes) — has triggered this latest round of political scrutiny of the social media behemoth, last month Facebook revealed another major data breach, affecting at least 50M users — underlining the ongoing challenge it has to live up to claims of having ‘locked the platform down’.

In light of both breaches, the Libe committee has now called for EU bodies to be allowed to fully audit Facebook — to independently assess its data protection and security practices.

Buttarelli also told us last week that it’s his belief none of the tech giants are directing adequate resource at keeping user data safe.

And with Facebook having already revealed a second breach that’s potentially even larger than Cambridge Analytica fresh focus and political attention is falling on the substance of its security practices, not just its claims.

While the Libe committee’s MEPs say they have taken note of steps Facebook made in the wake of the Cambridge Analytica scandal to try to improve user privacy, they point out it has still not yet carried out the promised full internal audit.

Facebook has never said how long this historical app audit will take. Though it has given some progress reports, such as detailing additional suspicious activity it has found to date, with 400 apps suspended at the last count. (One app, called myPersonality, also got banned for improper data controls.)

The Libe committee is now urging Facebook to allow the EU Agency for Network and Information Security (ENISA) and the European Data Protection Board, which plays a key role in applying the region’s data protection rules, to carry out “a full and independent audit” — and present the findings to the European Commission and Parliament and national parliaments.

It has also recommended that Facebook makes “substantial modifications to its platform” to comply with EU data protection law.

We’ve reached out to Facebook for comment on the recommendations — including specifically asking the company whether it’s open to an external audit of its platform.

At the time of writing Facebook had not responded to our question but we’ll update this report with any response.

Commenting in a statement, Libe chair Moraes said: “This resolution makes clear that we expect measures to be taken to protect citizens’ right to private life, data protection and freedom of expression. Improvements have been made since the scandal, but, as the Facebook data breach of 50 million accounts showed just last month, these do not go far enough.”

The committee has also made a series of proposals for reducing the risk of social media being used as an attack vector for election interference — including:

  • applying conventional “off-line” electoral safeguards, such as rules on transparency and limits to spending, respect for silence periods and equal treatment of candidates;
  • making it easy to recognize online political paid advertisements and the organisation behind them;
  • banning profiling for electoral purposes, including use of online behaviour that may reveal political preferences;
  • social media platforms should label content shared by bots and speed up the process of removing fake accounts;
  • compulsory post-campaign audits to ensure personal data are deleted;
  • investigations by member states with the support of Eurojust if necessary, into alleged misuse of the online political space by foreign forces.

A couple of weeks ago, the Commission outted a voluntary industry Code of Practice aimed at tackling online disinformation which several tech platforms and adtech companies had agreed to sign up to, and which also presses for action in some of the same areas — including fake accounts and bots.

However the code is not only voluntary but does not bind signatories to any specific policy steps or processes so it looks like its effectiveness will be as difficult to quantify as its accountability will lack bite.

A UK parliamentary committee which has also been probing political disinformation this year also put out a report this summer with a package of proposed measures — with some similar ideas but also suggesting a levy on social media to ‘defend democracy’.

Meanwhile Facebook itself has been working on increasing transparency around advertisers on its platform, and putting in place some authorization requirements for political advertisers (though starting in the US first).

But few politicians appear ready to trust that the steps Facebook is taking will be enough to avoid a repeat of, for example, the mass Kremlin propaganda smear campaign that targeted the 2016 US presidential election.

The Libe committee has also urged all EU institutions, agencies and bodies to verify that their social media pages, and any analytical and marketing tools they use, “should not by any means put at risk the personal data of citizens”.

And it goes as far as suggesting that EU bodies could even “consider closing their Facebook accounts” — as a measure to protect the personal data of every individual contacting them.

The committee’s full resolution was passed by 41 votes to 10 and 1 abstention. And will be put to a vote by the full EU Parliament during the next plenary session later this month.

In it, the Libe also renews its call for the suspension of the EU-US Privacy Shield.

The data transfer arrangement, which is used by thousands of businesses to authorize transfers of EU users’ personal data across the Atlantic, is under growing pressure ahead of an annual review this month, as the Trump administration has failed entirely to respond as EU lawmakers had hoped their US counterparts would at the time of the agreement being inked in the Obama era, back in 2016.

The EU parliament also called for Privacy Shield to be suspended this summer. And while the Commission did not act on those calls, pressure has continued to mount from MEPs and EU consumer and digital and civil rights bodies.

During the Privacy Shield review process this month the Commission will be pressuring US counterparts to try to gain concessions that it can sell back home as ‘compliance’.

But without very major concessions — and who would bank on that, given the priorities of the current US administration — the future of the precariously placed mechanism looks increasingly uncertain.

Even as more oversight coming down the pipe to rule social media platforms looks all but inevitable in Europe.

Nvidia launches Rapids to help bring GPU acceleration to data analytics

Nvidia, together with partners like IBM, HPE, Oracle, Databricks and others, is launching a new open-source platform for data science and machine learning today. Rapids, as the company is calling it, is all about making it easier for large businesses to use the power of GPUs to quickly analyze massive amounts of data and then […]

Nvidia, together with partners like IBM, HPE, Oracle, Databricks and others, is launching a new open-source platform for data science and machine learning today. Rapids, as the company is calling it, is all about making it easier for large businesses to use the power of GPUs to quickly analyze massive amounts of data and then use that to build machine learning models.

“Businesses are increasingly data-driven,” Nvidia’s VP of Accelerated Computing Ian Buck told me. “They sense the market and the environment and the behavior and operations of their business through the data they’ve collected. We’ve just come through a decade of big data and the output of that data is using analytics and AI. But most it is still using traditional machine learning to recognize complex patterns, detect changes and make predictions that directly impact their bottom line.”

The idea behind Rapids then is to work with the existing popular open-source libraries and platforms that data scientists use today and accelerate them using GPUs. Rapids integrates with these libraries to provide accelerated analytics, machine learning and — in the future — visualization.

Rapids is based on Python, Buck noted; it has interfaces that are similar to Pandas and Scikit, two very popular machine learning and data analysis libraries, and it’s based on Apache Arrow for in-memory database processing. It can scale from a single GPU to multiple notes and IBM notes that the platform can achieve improvements of up to 50x for some specific use cases when compared to running the same algorithms on CPUs (though that’s not all that surprising, given what we’ve seen from other GPU-accelerated workloads in the past).

Buck noted that Rapids is the result of a multi-year effort to develop a rich enough set of libraries and algorithms, get them running well on GPUs and build the relationships with the open-source projects involved.

“It’s designed to accelerate data science end-to-end,” Buck explained. “From the data prep to machine learning and for those who want to take the next step, deep learning. Through Arrow, Spark users can easily move data into the Rapids platform for acceleration.”

Indeed, Spark is surely going to be one of the major use cases here, so it’s no wonder that Databricks, the company founded by the team behind Spark, is one of the early partners.

“We have multiple ongoing projects to integrate Spark better with native accelerators, including Apache Arrow support and GPU scheduling with Project Hydrogen,” said Spark founder Matei Zaharia in today’s announcement. “We believe that RAPIDS is an exciting new opportunity to scale our customers’ data science and AI workloads.”

Nvidia is also working with Anaconda, BlazingDB, PyData, Quansight and scikit-learn, as well as Wes McKinney, the head of Ursa Labs and the creator of Apache Arrow and Pandas.

Another partner is IBM, which plans to bring Rapids support to many of its services and platforms, including its PowerAI tools for running data science and AI workloads on GPU-accelerated Power9 servers, IBM Watson Studio and Watson Machine Learning and the IBM Cloud with its GPU-enabled machines. “At IBM, we’re very interested in anything that enables higher performance, better business outcomes for data science and machine learning — and we think Nvidia has something very unique here,” Rob Thomas, the GM of IBM Analytics told me.

“The main benefit to the community is that through an entirely free and open-source set of libraries that are directly compatible with the existing algorithms and subroutines that their used to — they now get access to GPU-accelerated versions of them,” Buck said. He also stressed that Rapids isn’t trying to compete with existing machine learning solutions. “Part of the reason why Rapids is open source is so that you can easily incorporate those machine learning subroutines into their software and get the benefits of it.”

How to Create Pivot Tables in Google Sheets

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A pivot table allows you to generate a summary of the data in your spreadsheet. It also makes it easier to analyze or compare large chunks of data with a few clicks of a button. In Google Spreadsheets, creating a pivot table is a simple task. If you’re using a spreadsheet for a budget, for example, you can create a pivot chart to quickly see how much you’re spending on each category of your expenses. Pivot tables are, of course, much more powerful tools. Learning the basics of how to create a pivot table can allow you to experiment and…

Read the full article: How to Create Pivot Tables in Google Sheets

A pivot table allows you to generate a summary of the data in your spreadsheet. It also makes it easier to analyze or compare large chunks of data with a few clicks of a button. In Google Spreadsheets, creating a pivot table is a simple task.

If you’re using a spreadsheet for a budget, for example, you can create a pivot chart to quickly see how much you’re spending on each category of your expenses.

Pivot tables are, of course, much more powerful tools. Learning the basics of how to create a pivot table can allow you to experiment and do far more complex calculations with the spreadsheet feature.

How to Create a Pivot Table in Google Sheets

Using the example of a spreadsheet budget, we’re going to walk through the steps to create a pivot table in Google Sheets. Before you begin, make sure that each column with data has a column header in order for this to work.

Select the cells you want to use to generate the table unless you want to use the entire table.

In the Google Sheets menu, click Data > Pivot table.

This will create a new sheet in your spreadsheet. In the Pivot table editor, you can select which criteria you want to appear.

(Google Sheets may also make some recommended Pivot tables for you to generate with the click of a button.)

For Rows, click Add and select the data you want to calculate. In a budget spreadsheet, this would be the Category Expense. In this example, we’re using the Google Spreadsheet monthly budget template.

For Columns, if there are certain criteria that allows you to drill down about the data in your Rows, you can add it here. In a budget, for example, you might want to see the type of expenses summarized, but spread out across the dates the transactions took place.

For values, you can select to calculate the numbers of instances based on your rows, so simply select the same Row you added in the first step. In the case of the budget, you’ll want to select the Amount.

Once you’ve created your Pivot Table, it will be viewable at any time in that sheet, and as you add more data to your spreadsheet, the Pivot Table will also change dynamically as long as the cells you’re updating are in the original selection you made when creating the table.

To find out about more in-depth ways in which you can use pivot tables, and to find out how to create them in Microsoft Excel, take a look at How to Use an Excel Pivot Table for Data Analysis.

Read the full article: How to Create Pivot Tables in Google Sheets

AI could help push Neo4j graph database growth

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, Neo4j, the makers of the open source and commercial graph database platform, announced the release of Neo4j 3.5, which has a number of […]

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial intelligence and machine learning too. Today, Neo4j, the makers of the open source and commercial graph database platform, announced the release of Neo4j 3.5, which has a number of new features aimed specifically at AI and machine learning.

Neo4j founder and CEO Emil Eifrem says he had recognized the connection between AI and machine learning and graph databases for awhile, but he says that it has taken some time for the market to catch up to the idea.

“There has been a lot momentum around AI and graphs…Graphs are very fundamental to AI. At the same time we were seeing some early use cases, but not really broad adoption, and that’s what we’re seeing right now,” he explained.

AI graph uses cases. Graphic: Neo4j

To help advance AI uses cases, today’s release includes a new full text search capability, which Eifrem says has been one of the most requested features. This is important because when you are making connections between entities, you have to be able to find all of the examples regardless of how it’s worded — for example, human versus humans versus people.

Part of that was building their own indexing engine to increase indexing speed, which becomes essential with ever more data to process. “Another really important piece of functionality is that we have improved our data ingestion very significantly. We have 5x end-to-end performance improvements when it comes to importing data. And this is really important for connected feature extraction, where obviously, you need a lot of data to be able to train the machine learning,” he said. That also means faster sorting of data too.

Other features in the new release include improvements to the company’s own Cypher database query language and better visualization of the graphs to give more visibility, which is useful for visualizing how machine learning algorithms work, which is known as AI explainability. They also announced support for the Go language and increased security.

Graph databases are growing increasingly important as we look to find connections between data. The most common use case is the knowledge graph, which is what lets us see connections in a huge data sets. Common examples include who we are connected to on a social network like Facebook, or if we bought one item, we might like similar items on an ecommerce site.

Other use cases include connected feature extraction, a common machine learning training techniques that can look at a lot of data and extract the connections, the context and the relationships for a particular piece of data, such as suspects in a criminal case and the people connected to them.

Neo4j has over 300 large enterprise customers including Adobe, Microsoft, Walmart, UBS and NASA. The company launched in 2007 and has raised $80 million. The last round was $36 million in November 2016.

EU antitrust regulator eyeing Amazon’s use of merchant data

The European Union’s competition commission is looking into how Amazon uses data from retailers selling via its ecommerce marketplace, Reuters reports. Competition commissioner Margrethe Vestager revealed the action today during a press conference. “We are gathering information on the issue and we have sent quite a number of questionnaires to market participants in order to […]

The European Union’s competition commission is looking into how Amazon uses data from retailers selling via its ecommerce marketplace, Reuters reports.

Competition commissioner Margrethe Vestager revealed the action today during a press conference. “We are gathering information on the issue and we have sent quite a number of questionnaires to market participants in order to understand this issue in full,” she said.

It’s not a formal antitrust probe at this stage, with Vestager also telling reporters: “These are very early days and we haven’t formally opened a case. We are trying to make sure that we get the full picture.”

The Commission appears to be trying to determine whether or not third-party merchants selling on Amazon’s platform are being placed at a disadvantage vs the products Amazon also sells, thereby competing directly with some of its marketplace participants.

Companies found to be in breach of EU antitrust rules can be fined up to 10 per cent of their global annual turnover.

We’ve reached out to Amazon for comment.

In recent years the ecommerce giant has greatly expanded the own-brand products it sells via its marketplace, such as via its Amazon Elements line, which includes vitamin supplements and baby wipes, and AmazonBasics — which covers a wide array of ‘everyday’ items including batteries and even towels.

The company does not always brand its own-brand products with the Amazon label, also operating a raft of additional own brands — including for kids clothes, women’s fashion, sportswear, home furnishings and most recently diapers, to name a few. So it is not always immediately transparent to shoppers on its marketplace when they are buying something produced by Amazon itself.

Meanwhile, tech giants’ grip on big data has been flagged as a potential antitrust concern by Vestager for several years now.

In a speech at the DLD conference back in 2016 she said: “If a company’s use of data is so bad for competition that it outweighs the benefits, we may have to step in to restore a level playing field,” adding then that she was continuing to “look carefully at this issue”.

It’s not clear how the Amazon probe will pan out but it signifies a stepping up of the Commission’s action in this area.

The EU also issued Google with a recordbreaking $5BN fine this summer, for abusing the dominance of its Android mobile operating system.

That fine followed another recordbreaking penalty in 2017, when Google was slapped with an $2.7BN antitrust fine related to its search comparison service, Google Shopping.

Google is appealing against both rulings.

7 Online Tableau Software Training Courses to Lead You to Certification

tableau-software-courses

One of the best ways to enhance your employability is to earn certifications in a particular skillset. Doing so proves to would-be employers that you have the technical ability to use the app, software, or technology to a high standard. So, in today’s article, we’re going to look at online training courses for Tableau. Do the training, grab the certification, negotiate a higher salary—simple. What Is Tableau? Tableau is a software company that specializes in making interactive data visualization products. The company’s software can use information from relational databases, OLAP cubes, cloud databases, and spreadsheets, then produce a huge number…

Read the full article: 7 Online Tableau Software Training Courses to Lead You to Certification

One of the best ways to enhance your employability is to earn certifications in a particular skillset. Doing so proves to would-be employers that you have the technical ability to use the app, software, or technology to a high standard.

So, in today’s article, we’re going to look at online training courses for Tableau. Do the training, grab the certification, negotiate a higher salary—simple.

What Is Tableau?

Tableau is a software company that specializes in making interactive data visualization products.

The company’s software can use information from relational databases, OLAP cubes, cloud databases, and spreadsheets, then produce a huge number of graphs and outputs. It also supports mapping and spatial files.

Tableau offers six core products: Desktop, Prep, Server, Online, Reader, and Public. The final two apps—Reader and Public—are free to use.

The Reader app lets anyone open and view (but not edit) visualizations made in the Desktop app, while Public lets you create graphs, charts, maps, and other visualizations, then publish them on the web. It’s a great tool for blogs and hobbyist data analysts.

The Best Online Training and Tutorials for Tableau

Keep reading to discover the best online training and tutorials for Tableau. We’ll cover some free and paid options for all skill levels.

1. Tableau’s Official Resources

 tableau training homepage

Skill level: All
Price: Free-$2,500

The easiest way to start learning is to use Tableau’s official training resources. They are available via Tableau’s website.

The resources are split into two sections: the free-to-use training videos and the paid courses.

The free training videos are singlehandedly capable of taking you to a good standard. There are more than 100 available.

Introductory lessons include Getting Started and troubleshooting, the intermediate courses move on to cover connecting to data, using dashboards, and mapping. There are also tutorials for the admin side of the app such as security and user management. All the videos are a few minutes long.

For advanced courses, you’ll need to shell out for the professional courses. Prices run from $1,000 to $2,000 per course. They are much more detailed and cover more advanced ideas. You can take them in-person or in a virtual classroom.

2. Tableau Tutorial for Beginners

udemy tableau beginners homepage

Skill level: Beginner
Price: Free

If you’re a complete Tableau beginner, you should check out this free Udemy course. The entire course only lasts 2.5 hours, so it’s not going to keep you tied up for days. You’ll be ready for the intermediate courses in no time.

The course will teach you all the basics of using the software—though you will need to download the free trial of Tableau Desktop before you start.

Topics covered include how to connect to data sources, how to turn your data into graphs, charts, and maps, and how to join and merge multiple datasets.

The Tableau Tutorial for Beginners course also starts to introduce databases, geographical data types, and how to create complex calculations.

3. Intellipaat: Tableau Tutorial

intellipaat tutorial example

Skill level: Beginner
Price: Free

The text-based beginners’ tutorial from Intellipaat is the perfect complement to the introductory Udemy course we discussed above. It builds on the fundamental principles you that you have already been introduced to, without over complicating the course with information you’re still not ready for.

The course begins with an overview of the Tableau apps and working environment; it covers some apps (such as Tableau Reader and Tableau Public) which aren’t discussed in the Udemy course.

The second half of the course covers topics such as connecting to data sources, blending data, and extracting data. It ends with a short section on how to create dashboards.

4. Tutorial Gateway: Tableau Tutorial

tutorial gateway example

Skill Level: Intermediate
Price: Free

Tutorial Gateway’s Tableau tutorial is also entirely text and image-based; if you like working with videos, it’s not the course for you.

However, if you can work with text, it’s definitely worth checking out. The amount of content the course covers is breathtaking, especially considering it is free.

The course starts out at a gentle pace. Even if you’re a complete beginner, you should be able to work through the first few chapters without too much difficulty. When you arrive at the latter sections—covering features such as interactive dashboards and Tableau’s filters, functions, and calculations, the complexity starts to increase.

There are 11 chapters in total. We’d estimate it would take most people a couple of weeks to get through all the content.

5. Tableau 10 for Data Scientists

tableau for data scientists video

Skill Level: Intermediate
Price: Requires a Lynda subscription ($29.99 per month, 30-day free trial available)

At its core, Tableau is a data science tool. So, if you’re interested in learning how to get the most out of the suite, check out the Tableau 10 for Data Scientists course on Lynda. (If you want to learn more about data science, check out some of the data science Udemy courses.)

Despite the name, the course is suitable for anyone who has a basic foundation of understanding in the Tableau apps and is ready to take their knowledge up a notch.

The entire course lasts for two hours and 24 minutes. It is divided into 62 individual videos, none of which are more than four minutes long. As such, it’s a perfect course for dipping into and brushing up on parts of the app you’re less familiar with.

Content covered includes connecting and extracting data, transforming data, analytics, mapping data, and working with parameters.

6. Tutorials Point: Tableau Tutorial

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Skill level: Intermediate/Expert
Price: Free

Tutorials Point’s Tableau Tutorial marks a good step up in complexity from Tutorial Gateway’s guide. It reinforces some of the things you learned as an immediate user while simultaneously laying the groundwork for themes you will learn about as an expert.

After a short opening chapter for beginners, the guide looks in detail at worksheets, data sources, calculations, filters, and charts.

In the end, the Advanced section has four chapters: Dashboard, Formatting, Forecasting, and Trend Lines.

All the lessons are easily digestible; they go into too much detail and focus on image-based learning rather than text.

7. Tableau Expert: Top Visualization Techniques in Tableau 10

tableau expert homepage

Skill Level: Expert
Price: $199.99

If you hope to step up and Tableau certification, you’re going to need to splash out on some high-quality expert courses.

This Udemy course is one of the best. At $199.99 it’s not cheap, but Udemy often offers discounts—keep your eyes peeled. And anyway, it’ll be money well-spent if you training leads to a nice salary hike, right?

The 5.5-hour course focuses on making your visualizations stand out from the crowd. It’s not as easy as it sounds when you consider that complete beginners can start making charts with just a couple of hours of learning.

It includes 68 videos and 14 downloadable resources. The course covers topics such as creating hexbin charts, Sankey diagrams, visualizing Likert scale survey data. None of the visualizations are part of the software “out of the box.”

Udemy’s other advanced Tableau courses are Mastering Tableau 10, Tableau 10 Development, and Tableau 10 Business Intelligence Solutions. They are all similarly priced.

Getting Your Tableau Certification

Tableau offers official certification through its website. Four certifications are available:

  • Tableau Desktop Qualified Associate
  • Tableau Desktop Certified Professional
  • Tableau Server Qualified Associate
  • Tableau Server Certified Professional

In order to become a Certified Professional, you first need to become a Qualified Associate. Before applying for either Qualified Associate exam, Tableau suggests you should have five months of experience and complete some of its training programs.

The Qualified Associate exams cost $250, the Desktop Certified Professional costs $600, and the Server Certified Professional is $800.

Get Certification, Improve Your Job Prospects

Regardless of your profession, getting certified in the software, machinery, tools, or protocols you use every day is a great way to make you stand out from the crowd in an increasingly competitive job market.

Another way of standing is to make sure you have a killer CV. If you’d like to learn how technology can help to build your resume, check out our articles about the best resume-building sites and free resume templates.

Read the full article: 7 Online Tableau Software Training Courses to Lead You to Certification

7 Certified Data Science Courses to Upgrade Your Job Skills With Coursera

Looking to start a career path around data science? Why not get started with Coursera? We’ve previously highlighted some of the best Coursera courses worth paying for. But if those were too broad for you, have a look at these excellent courses in data science. What Is Data Science? Just in case you’re not aware, we’ll briefly describe the field of data science so you have an idea of what these courses involve. Data science, in a sentence, is a field that uses all sorts of methods to pull insights from data. This enables people to make better decisions. With…

Read the full article: 7 Certified Data Science Courses to Upgrade Your Job Skills With Coursera

Looking to start a career path around data science? Why not get started with Coursera?

We’ve previously highlighted some of the best Coursera courses worth paying for. But if those were too broad for you, have a look at these excellent courses in data science.

What Is Data Science?

Just in case you’re not aware, we’ll briefly describe the field of data science so you have an idea of what these courses involve.

Data science, in a sentence, is a field that uses all sorts of methods to pull insights from data. This enables people to make better decisions.

With the explosion of big data and easier ways to collect data in large quantities than ever before, having data science around to process and make meaningful choices based on it is essential. These courses will introduce you to data science and help you branch out to a specific area that you’re interested in.

1. The Data Scientist’s Toolbox by Johns Hopkins University

The first course in the university’s data science specialization. It serves as an overview of what data scientists do and work with. You’ll learn the basics of how to turn data into information you can take action on, as well as technical tools used in other data science courses like R programming, git, and similar.

You won’t get into the nitty-gritty of data science just yet, but this serves as a valuable foundation for the tools of the trade.

2. Getting and Cleaning Data by Johns Hopkins University

As this course’s description says, before you can work with data, you need some data! Thus, this class focuses on ways to obtain some. You’ll learn how to grab data from the internet, databases, various APIs, and more.

Similarly, you’ll learn the essentials of data cleaning, the process of making your data neat so you can work with it more easily. By keeping your data in good shape, it becomes much easier to work with and more useful.

3. Machine Learning by Stanford

Machine learning, the process of making computers act without explicit programming, is huge today. The progress made in self-driving cars, automated web technologies, and similar fields has been fantastic, and machine learning powers them all.

It’s an important part of data science, making it a great Coursera course to take. You’ll get some practice working with machine learning techniques, how to apply them, and some best practices in the field. Interestingly, this course is taught by Andrew Ng, the co-founder of Coursera.

4. Introduction to Data Science in Python by University of Michigan

Python is a popular programming language for all sorts of purposes, so it’s no surprise to see it used in data science. This course, the first in a five-part Applied Data Science with Python Specialization set from the University of Michigan, looks at the basics of Python and data manipulation.

After this course, you’ll know how to clean and manipulate data in Python. It’s an intermediate-level course, so total newcomers to Python or statistics need not apply.

5. Google Cloud Platform Fundamentals: Core Infrastructure by Google Cloud

Google’s cloud technology is one of the front runners of data science, so why not learn from the best? This course is the first part of Google’s cloud platform specialization, and walks you through the basics of working with the various services. You’ll meet Google App Engine and Google Computer Engine, for starters.

It’s a great overview of the powerful services Google has at its disposal and will help you decide if you want to continue learning about them. Notably, this course has just one week of study, so you can complete it in roughly seven hours.

6. Inferential Statistics by University of Amsterdam

If you don’t have any experience with statistics, you might run into trouble understanding data science. In those cases, this course will provide you with some background on the field.

You’ll learn basic principles for testing specifics, then explore common statistical tests and how to interpret them.

7. Data Science Specialization by Johns Hopkins University

If you’re serious about data science, take a look at Coursera’s data science specialization. This is a nine-course introduction to the discipline, capped off by a real-world project.

Some of the above courses were taken from this specialization, so you can take them individually if you only have a passing interest in the subject. But working through the whole package enables you to learn much more, and you’ll have a valuable certificate upon completion.

This specialization takes about nine months to complete, with five hours of work per week. It’s intended for beginners and doesn’t require any background knowledge other than a basic working knowledge of Python.

Ready to Learn About Data Science?

We’ve highlighted six courses that cover different areas of the data scientist’s toolkit for you to explore on Coursera. If you want to go further, look into the Johns Hopkins specialization for much more on this topic.

Data science is an exciting field, and it will only continue to grow as technology becomes more powerful. Take advantage of Coursera‘s excellent (and affordable) courses now to get your career started!

Read the full article: 7 Certified Data Science Courses to Upgrade Your Job Skills With Coursera

George Church’s genetics on the blockchain startup just raised $4.3 million from Khosla

Nebula Genomics, the startup that wants to put your whole genome on the blockchain, has announced the raise of $4.3 million in Series A from Khosla Ventures and other leading tech VC’s such as Arch Venture Partners, Fenbushi Capital, Mayfield, F-Prime Capital Partners, Great Point Ventures, Windham Venture Partners, Hemi Ventures, Mirae Asset, Hikma Ventures and […]

Nebula Genomics, the startup that wants to put your whole genome on the blockchain, has announced the raise of $4.3 million in Series A from Khosla Ventures and other leading tech VC’s such as Arch Venture Partners, Fenbushi Capital, Mayfield, F-Prime Capital Partners, Great Point Ventures, Windham Venture Partners, Hemi Ventures, Mirae Asset, Hikma Ventures and Heartbeat Labs.

Nebula has also has forged a partnership with genome sequencing company Veritas Genetics.

Veritas was one of the first companies to sequence the entire human genome for less than $1,000 in 2015, later adding all that info to the touch of a button on your smartphone. Both Nebula and Veritas were cofounded by MIT professor and “godfather” of the Human Genome Project, George Church.

The partnership between the two companies will allow the Nebula marketplace, or the place where those consenting to share their genetic data can earn Nebula’s cryptocurrency called “Nebula tokens” to build upon Veritas open-source software platform Arvados, which can process and share large amounts of genetic information and other big data. According to the company, this crossover offers privacy and security for the physical storage and management of various data sets according to local rules and regulations.

“As our own database grows to many petabytes, together with the Nebula team we are taking the lead in our industry to protect the privacy of consumers while enabling them to participate in research and benefit from the blockchain-based marketplace Nebula is building,” Veritas CEO Mirza Cifric said in a statement.

The partnership will work with various academic institutions and industry researchers to provide genomic data from individual consumers looking to cash in by sharing their own data, rather than by freely giving it as they might through another genomics company like 23andMe .

“Compared to centralized databases, Nebula’s decentralized and federated architecture will help address privacy concerns and incentivize data sharing,” added Nebula Genomics co-founder Dennis Grishin. “Our goal is to create a data flow that will accelerate medical research and catalyze a transformation of health care.”

Outlier raises $6.2 M Series A to change how companies use data

Traditionally, companies have gathered data from a variety of sources, then used spreadsheets and dashboards to try and make sense of it all. Outlier wants to change that and deliver a handful of insights right to your inbox that matter most for your job, company and industry. Today the company announced a $6.2 million Series […]

Traditionally, companies have gathered data from a variety of sources, then used spreadsheets and dashboards to try and make sense of it all. Outlier wants to change that and deliver a handful of insights right to your inbox that matter most for your job, company and industry. Today the company announced a $6.2 million Series A to further develop that vision.

The round was led by Ridge Ventures with assistance from 11.2 Capital, First Round Capital, Homebrew, Susa Ventures and SV Angel. The company has raised over $8 million.

The startup is trying to solve a difficult problem around delivering meaningful insight without requiring the customer to ask the right questions. With traditional BI tools, you get your data and you start asking questions and seeing if the data can give you some answers. Outlier wants to bring a level of intelligence and automation by pointing out insight without having to explicitly ask the right question.

Company founder and CEO Sean Byrnes says his previous company, Flurry, helped deliver mobile analytics to customers, but in his travels meeting customers in that previous iteration, he always came up against the same question: “This is great, but what should I look for in all that data?”

It was such a compelling question that after he sold Flurry in 2014 to Yahoo for more than $200 million, that question stuck in the back of his mind and he decided to start a business to solve it. He contends that the first 15 years of BI was about getting answers to basic questions about company performance, but the next 15 will be about finding a way to get the software to ask good questions based on the huge amounts of data.

Byrnes admits that when he launched, he didn’t have much sense of how to put this notion into action, and most people he approached didn’t think it was a great idea. He says he heard “No” from a fair number of investors early on because the artificial intelligence required to fuel a solution like this really wasn’t ready in 2015 when he started the company.

He says that it took four or five iterations to get to today’s product, which lets you connect to various data sources, and using artificial intelligence and machine learning delivers a list of four or five relevant questions to the user’s email inbox that points out data you might not have noticed, what he calls “shifts below the surface.” If you’re a retailer that could be changing market conditions that signal you might want to change your production goals.

Outlier email example. Photo: Outlier

The company launched in 2015. It took some time to polish the product, but today they have 14 employees and 14 customers including Jack Rogers, Celebrity Cruises and Swarovski.

This round should allow them to continuing working to grow the company. “We feel like we hit the right product-market fit because we have customers [generating] reproducible results and really changing the way people use the data,” he said.

SessionM customer loyalty data aggregator snags $23.8 M investment

SessionM announced a $23.8 million Series E investment led by Salesforce Ventures. A bushel of existing investors including Causeway Media Partners, CRV, General Atlantic, Highland Capital and Kleiner Perkins Caufield & Byers also contributed to the round. The company has now raised over $97 million. At its core, SessionM aggregates loyalty data for brands to […]

SessionM announced a $23.8 million Series E investment led by Salesforce Ventures. A bushel of existing investors including Causeway Media Partners, CRV, General Atlantic, Highland Capital and Kleiner Perkins Caufield & Byers also contributed to the round. The company has now raised over $97 million.

At its core, SessionM aggregates loyalty data for brands to help them understand their customer better, says company co-founder and CEO Lars Albright. “We are a customer data and engagement platform that helps companies build more loyal and profitable relationships with their consumers,” he explained.

Essentially that means, they are pulling data from a variety of sources and helping brands offer customers more targeted incentives, offers and product recommendations “We give [our users] a holistic view of that customer and what motivates them,” he said.

Screenshot: SessionM (cropped)

To achieve this, SessionM takes advantage of machine learning to analyze the data stream and integrates with partner platforms like Salesforce, Adobe and others. This certainly fits in with Adobe’s goal to build a customer service experience system of record and Salesforce’s acquisition of Mulesoft in March to integrate data from across an organization, all in the interest of better understanding the customer.

When it comes to using data like this, especially with the advent of GDPR in the EU in May, Albright recognizes that companies need to be more careful with data, and that it has really enhanced the sensitivity around stewardship for all data-driven businesses like his.

“We’ve been at the forefront of adopting the right product requirements and features that allow our clients and businesses to give their consumers the necessary control to be sure we’re complying with all the GDPR regulations,” he explained.

The company was not discussing valuation or revenue. Their most recent round prior to today’s announcement, was a Series D in 2016 for $35 million also led by Salesforce Ventures.

SessionM, which was founded in 2011, has around 200 employees with headquarters in downtown Boston. Customers include Coca-Cola, L’Oreal and Barney’s.