Interview – The Importance of Machine Learning for the Data Driven Business

To become more data-driven, organizations must mature their analytics and automate more of their decision making processes for innovation and differentiation. Data science seems like the right approach, yet is a new and fast moving field that seems to have as many dead ends as it has high ways to value. Cloudera Fast Forward Labs, led by Hilary Mason, shows companies the way.

Alice Albrecht is a research engineer at Cloudera Fast Forward Labs.  She spends her days researching the latest and greatest in machine learning and artificial intelligence and bringing that knowledge to working prototypes and delivering concrete advice for clients.  Prior to joining Fast Forward Labs, Alice worked in both finance and technology companies as a practicing data scientist, data science leader, and – most recently – a data product manager.  In addition to teaching machines to do cool things, Alice is passionate about mentoring and helping others grow in their careers.  Alice holds a PhD from Yale in cognitive neuroscience where she studied how humans summarize sensory information from the world around them and the neural substrates that underlie those summaries.

Read this article in German:
“Interview – Die Bedeutung von Machine Learning für das Data Driven Business“

Data Science Blog: Ms. Albrecht, you are a well-known keynote speaker for data science and artificial intelligence. While data science has arrived business already, deep learning seems to be the new trend. Is artificial intelligence for business already normal business or is it an overrated hype?

I’d say it isn’t either of those two options.  Data science is now widely adopted but companies still struggle to integrate this new discipline into their existing businesses.  As for deep learning, it really depends on the company that’s looking into using this technique.  I wouldn’t say that deep learning is by any means part of business as usual- nor should it be.  It’s a tool like any other and building a capacity for using a tool without clearly defined business needs is a recipe for disaster.

Data Science Blog: Just to make sure what we are talking about: What are the differences and overlaps between data analytics, data science, machine learning, deep learning and artificial intelligence?

Here at Cloudera Fast Forward Labs, we like to think of data analytics as collecting data and counting things (mostly for quick charts and reports).  Data science solves business problems by counting cleverly and predicting things with the data that’s collected.  Machine learning is about solving problems with new kinds of feedback loops that improve with more data.  Deep learning is a particular type of machine learning and is not itself a separate concept or type of tool.  Artificial intelligence taps into something more complicated than what we’re seeing today – it’s much broader than training machines to repetitively do very specialized tasks or solve very narrow problems.

Data Science Blog: And how can we add the context to big data?

From a theoretical perspective, data science has been around for decades. The building blocks for modern day machine learning, deep learning and artificial intelligence are based on mathematical theorems  that go back to the 1940’s and 1950’s. The challenge was that at the time, compute power and data storage capacity were simply too expensive for the approaches to be implemented. Today that’s all changed.. Not only has the cost of data storage dropped considerably, open source technology like Apache Hadoop has made it possible to store any volume of data at costs approaching zero. Compute power, even highly specialised chip architectures, are now also available on demand and only for the time organisations need them through public and private cloud solutions. The decreased cost of both data storage and compute power, together with a growing list of tools and resources readily available via the open source community allows companies of any size to benefit from data (no matter that size of that data).

Data Science Blog: What are the challenges for organizations in getting started with data science?

I see two big challenges when getting started with data science.  One is ensuring that you have organizational alignment around exactly what type of work data scientists will deliver (and timing for those projects).  The second hurdle is around ensuring that you have the right data in place before you start hiring data scientists. This can be tricky if you don’t have in-house expertise in this area, so sometimes it’s better to hire a data engineer or a data strategist (or director of data science) before you ever get started building out a data science team.

Data Science Blog: There are many discussions about how to build a data-driven business. Is it just about using data science to get a better understanding of customer behavior?

No, being data driven doesn’t just mean better understanding your customers (though that is one way that data science can help in an organization).  Aside from building an organization that relies on data and analytics to help them make decisions (about customer behavior or otherwise), being a data-driven business means that data is powering your core products.

Data Science Blog: The number of technologies, tools and frameworks is increasing. For organizations this also means increasing complexity. Do companies need to stay always up-to-date or could it be an advice to wait and imitate pioneers later?

While it’s not critical (or advisable) for organizations to adopt every new advancement that comes along, it is critical for them to stay abreast of emerging frameworks.  If a business waits to see what others are doing, and therefore don’t invest in understanding how new advancements can affect their particular business, they’ve likely already missed the boat.

Data Science Blog: Global players have big budgets just for doing research and setting up data labs. Middle-sized companies need to see the break even point soon. How can we accelerate the value generation of data science?

Having a team that is highly focused on a specific set of projects that are well-scoped and aligned to the business makes all the difference.  Data science and machine learning don’t have to sacrifice doing research and being innovative in order to produce value.  The biggest difference is that smaller teams will have to be more aware of how their choice of project fits into emerging frameworks and their particular acute and near term business needs.

Data Science Blog: How does Cloudera Fast Forward Labs help other organizations to accelerate their start with machine learning?

We advise organizations, based on their particular needs, on what the latest advancements are in machine learning and data science, how to build and structure their data teams to develop the capabilities they need to meet their goals, and how to quickly implement custom forward-looking solutions using their own data and in-house expertise.

Data Science Blog: Finally, a question for our younger readers who are looking for a career as a data expert: What makes a good data scientist? Do you like to work with introverted coding nerds or the data loving business experts?

A good data scientists should be deeply curious and have a love for the ways in which data can lead to new discoveries and power the next generation of products.  We expect the people who thrive in this field to come from a variety of backgrounds and experiences.

Interview – Python as productive data science environment

Miroslav Šedivý is a Senior Software Architect at UBIMET GmbH, using Python to make the sun shine and the wind blow. He is an enthusiast of both human and programming languages and found Python as his language of choice to setup very productive environments. Mr. Šedivý was born in Czechoslovakia, studied in France and is now living in Germany. Furthermore, he helps in the organization of the events PyCon.DE and Polyglot Gathering.


On 26th June 2018 he will explain at the Python@DWX conference why “Lifelong Text Hackers Use Vim and Python”. Insert the promotion code PY18science to unlock your 10% discount on all tickets. More info and tickets on python-con.com.


Data Science Blog: Mr. Šedivý, how did you find the way to Python as your favorite programming language?

Apart from traditional languages taught at school (Basic, Pascal, C, Java), some twenty years ago I learned Perl to hack a dynamic web site and used it to automate my daily tasks. Later I used it professionally for scientific calculations in the production. This was later replaced by Python, its newer versions and more advanced libraries. Nowadays Python has almost completely replaced Perl as my principal language and I use Perl just to hack some command line filters and to impress colleagues.

Data Science Blog: Python is one of the most popular programming language for data scientists. This is remarkable as it is originally not designed for doing data science with it. What made it a competitor to languages like R or Julia?

Python is the most powerful programming language that is still legible. This appeals to data scientists who can enter each line interactively, and immediately see what happens, because each line actually does something. They can inspect their data easily and build automating systems to process their data transparently.

Data Science Blog: Is there anything you could do better with another programming language?

Sometimes I’m playing with some functional languages that would allow me to write code that is easier to test and parallelize.

Data Science Blog: Which libraries are the most important ones for your daily business?

The whole Pandas ecosystem with Numpy and Scipy. Matplotlib for plots, PyTables and Psycopg2 for storage. I’m also importing a few async libs for webservices and similar network-based software.

I also enjoy discovering the world of Unicode and Timezones – both of them are the spots where the programmers absolutely have to obey the chaotic reality of the outside world.

Data Science Blog: Which editor do you use? And how to set it up as a productive environment?

I tried several editors and IDEs, but always came back to Vi or Vim. This is an extremely powerful editor that is around since over forty years, which was probably before most of today’s active developers learned to type. I’m using it for all text editing tasks, which I’m actually going to show in my talk at DWX [Lifelong Text Hackers Use Vim and Python]. Steep learning curve is not an argument against a tool you can grok during your entire career.

Data Science Blog: In your opinion: For all developers and data scientists, who are used to Java, Scala, R oder Perl, is Python easy to learn? Could it be too late to switch for somebody?

Python is a great general language that can be learned rapidly to a usable level. It’s different from the aforementioned languages. I remember my switching process from Perl to Python over ten years ago with a book “Perl to Python Migration”, which forced me to switch my way of thinking. From the question “Why do I have to import ‘re’ for regular expressions if Perl uses them natively?” to “Actually, I can solve this problem without regular expressions.”.

Applying Data Science Techniques in Python to Evaluate Ionospheric Perturbations from Earthquakes

Multi-GNSS (Galileo, GPS, and GLONASS) Vertical Total Electron Content Estimates: Applying Data Science techniques in Python to Evaluate Ionospheric Perturbations from Earthquakes

1 Introduction

Today, Global Navigation Satellite System (GNSS) observations are routinely used to study the physical processes that occur within the Earth’s upper atmosphere. Due to the experienced satellite signal propagation effects the total electron content (TEC) in the ionosphere can be estimated and the derived Global Ionosphere Maps (GIMs) provide an important contribution to monitoring space weather. While large TEC variations are mainly associated with solar activity, small ionospheric perturbations can also be induced by physical processes such as acoustic, gravity and Rayleigh waves, often generated by large earthquakes.

In this study Ionospheric perturbations caused by four earthquake events have been observed and are subsequently used as case studies in order to validate an in-house software developed using the Python programming language. The Python libraries primarily utlised are Pandas, Scikit-Learn, Matplotlib, SciPy, NumPy, Basemap, and ObsPy. A combination of Machine Learning and Data Analysis techniques have been applied. This in-house software can parse both receiver independent exchange format (RINEX) versions 2 and 3 raw data, with particular emphasis on multi-GNSS observables from GPS, GLONASS and Galileo. BDS (BeiDou) compatibility is to be added in the near future.

Several case studies focus on four recent earthquakes measuring above a moment magnitude (MW) of 7.0 and include: the 11 March 2011 MW 9.1 Tohoku, Japan, earthquake that also generated a tsunami; the 17 November 2013 MW 7.8 South Scotia Ridge Transform (SSRT), Scotia Sea earthquake; the 19 August 2016 MW 7.4 North Scotia Ridge Transform (NSRT) earthquake; and the 13 November 2016 MW 7.8 Kaikoura, New Zealand, earthquake.

Ionospheric disturbances generated by all four earthquakes have been observed by looking at the estimated vertical TEC (VTEC) and residual VTEC values. The results generated from these case studies are similar to those of published studies and validate the integrity of the in-house software.

2 Data Cleaning and Data Processing Methodology

Determining the absolute VTEC values are useful in order to understand the background ionospheric conditions when looking at the TEC perturbations, however small-scale variations in electron density are of primary interest. Quality checking processed GNSS data, applying carrier phase leveling to the measurements, and comparing the TEC perturbations with a polynomial fit creating residual plots are discussed in this section.

Time delay and phase advance observables can be measured from dual-frequency GNSS receivers to produce TEC data. Using data retrieved from the Center of Orbit Determination in Europe (CODE) site (ftp://ftp.unibe.ch/aiub/CODE), the differential code biases are subtracted from the ionospheric observables.

2.1 Determining VTEC: Thin Shell Mapping Function

The ionospheric shell height, H, used in ionosphere modeling has been open to debate for many years and typically ranges from 300 – 400 km, which corresponds to the maximum electron density within the ionosphere. The mapping function compensates for the increased path length traversed by the signal within the ionosphere. Figure 1 demonstrates the impact of varying the IPP height on the TEC values.

Figure 1 Impact on TEC values from varying IPP heights. The height of the thin shell, H, is increased in 50km increments from 300 to 500 km.

2.2 Phase Smoothing

For dual-frequency GNSS users TEC values can be retrieved with the use of dual-frequency measurements by applying calculations. Calculation of TEC for pseudorange measurements in practice produces a noisy outcome and so the relative phase delay between two carrier frequencies – which produces a more precise representation of TEC fluctuations – is preferred. To circumvent the effect of pseudorange noise on TEC data, GNSS pseudorange measurements can be smoothed by carrier phase measurements, with the use of the carrier phase smoothing technique, which is often referred to as carrier phase leveling.

Figure 2 Phase smoothed code differential delay

2.3 Residual Determination

For the purpose of this study the monitoring of small-scale variations in ionospheric electron density from the ionospheric observables are of particular interest. Longer period variations can be associated with diurnal alterations, and changes in the receiver- satellite elevation angles. In order to remove these longer period variations in the TEC time series as well as to monitor more closely the small-scale variations in ionospheric electron density, a higher-order polynomial is fitted to the TEC time series. This higher-order polynomial fit is then subtracted from the observed TEC values resulting in the residuals. The variation of TEC due to the TID perturbation are thus represented by the residuals. For this report the polynomial order applied was typically greater than 4, and was chosen to emulate the nature of the arc for that particular time series. The order number selected is dependent on the nature of arcs displayed upon calculating the VTEC values after an initial inspection of the VTEC plots.

3 Results

3.1 Tohoku Earthquake

For this particular report, the sampled data focused on what was retrieved from the IGS station, MIZU, located at Mizusawa, Japan. The MIZU site is 39N 08′ 06.61″ and 141E 07′ 58.18″. The location of the data collection site, MIZU, and the earthquake epicenter can be seen in Figure 3.

Figure 3 MIZU IGS station and Tohoku earthquake epicenter [generated using the Python library, Basemap]

Figure 4 displays the ionospheric delay in terms of vertical TEC (VTEC), in units of TECU (1 TECU = 1016 el m-2). The plot is split into two smaller subplots, the upper section displaying the ionospheric delay (VTEC) in units of TECU, the lower displaying the residuals. The vertical grey-dashed lined corresponds to the epoch of the earthquake at 05:46:23 UT (2:46:23 PM local time) on March 11 2011. In the upper section of the plot, the blue line corresponds to the absolute VTEC value calculated from the observations, in this case L1 and L2 on GPS, whereby the carrier phase leveling technique was applied to the data set. The VTEC values are mapped from the STEC values which are calculated from the LOS between MIZU and the GPS satellite PRN18 (on Figure 4 denoted G18). For this particular data set as seen in Figure 4, a polynomial fit of  five degrees was applied, which corresponds to the red-dashed line. As an alternative to polynomial fitting, band-pass filtering can be employed when TEC perturbations are desired. However for the scope of this report polynomial fitting to the time series of TEC data was the only method used. In the lower section of Figure 4 the residuals are plotted. The residuals are simply the phase smoothed delay values (the blue line) minus the polynomial fit line (the red-dashed line). All ionosphere delay plots follow the same layout pattern and all time data is represented in UT (UT = GPS – 15 leap seconds, whereby 15 leap seconds correspond to the amount of leap seconds at the time of the seismic event). The time series shown for the ionosphere delay plots are given in terms of decimal of the hour, so that the format follows hh.hh.

Figure 4 VTEC and residual plot for G18 at MIZU on March 11 2011

3.2 South Georgia Earthquake

In the South Georgia Island region located in the North Scotia Ridge Transform (NSRT) plate boundary between the South American and Scotia plates on 19 August 2016, a magnitude of 7.4 MW earthquake struck at 7:32:22 UT. This subsection analyses the data retrieved from KEPA and KRSA. As well as computing the GPS and GLONASS TEC values, four Galileo satellites (E08, E14, E26, E28) are also analysed. Figure 5 demonstrates the TEC perturbations as computed for the Galileo L1 and L5 carrier frequencies.

Figure 5 VTEC and residual plots at KRSA on 19 August 2016. The plots are from the perspective of the GNSS receiver at KRSA, for four Galileo satellites (a) E08; (b) E14; (c) E24; (d) E26. The y-axes and x-axes in all plots do not conform with one another but are adjusted to fit the data. The y-axes for the residual section of each plot is consistent with one another.

Figure 6 Geometry of the Galileo (E08, E14, E24 and E26) satellites’ projected ground track whereby the IPP is set to 300km altitude. The orange lines correspond to tectonic plate boundaries.

4 Conclusion

The proximity of the MIZU site and magnitude of the Tohoku event has provided a remarkable – albeit a poignant – opportunity to analyse the ocean-ionospheric coupling aftermath of a deep submarine seismic event. The Tohoku event has also enabled the observation of the origin and nature of the TIDs generated by both a major earthquake and tsunami in close proximity to the epicenter. Further, the Python software developed is more than capable of providing this functionality, by drawing on its mathematical packages, such as NumPy, Pandas, SciPy, and Matplotlib, as well as employing the cartographic toolkit provided from the Basemap package, and finally by utilizing the focal mechanism generation library, Obspy.

Pre-seismic cursors have been investigated in the past and strongly advocated in particular by Kosuke Heki. The topic of pre-seismic ionospheric disturbances remains somewhat controversial. A potential future study area could be the utilization of the Python program – along with algorithmic amendments – to verify the existence of this phenomenon. Such work would heavily involve the use of Scikit-Learn in order to ascertain the existence of any pre-cursors.

Finally, the code developed is still retained privately and as of yet not launched to any particular platform, such as GitHub. More detailed information on this report can be obtained here:

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Process Analytics – Data Analysis for Process Audit & Improvement

Process Mining: Innovative data analysis for process optimization and audit

Step-by-Step: New ways to detect compliance violations with Process Analytics

In the course of the advancing digitization, an enormous upheaval of everyday work is currently taking place to ensure the complete recording of all steps in IT systems. In addition, companies are increasingly confronted with increasingly demanding regulatory requirements on their IT systems.


Read this article in German:
“Process Mining: Innovative Analyse von Datenspuren für Audit und Forensik “


The unstoppable trend towards a connected world will further increase the possibilities of process transparency, but many processes in the company area are already covered by one or more IT systems. Each employee, as well as any automated process, leaves many data traces in IT backend systems, from which processes can be replicated retroactively or in real time. These include both obvious processes, such as the entry of a recorded purchase order or invoice, as well as partially hidden processes, such as the modification of certain entries or deletion of these business objects.

1 Understanding Process Analytics

Process Analytics is a data-driven methodology of the actual process analysis, which originates in forensics. In the wake of the increasing importance of computer crime, it became necessary to identify and analyze the data traces that potential criminals left behind in IT systems in order to reconstruct the event as much as possible.

With the trend towards Big Data Analytics, Process Analytics has not only received new data bases, but has also been further developed as an analytical method. In addition, the visualization enables the analyst or the report recipient to have a deeper understanding of even more complex business processes.

While conventional process analysis primarily involves employee interviews and monitoring of the employees at the desk in order to determine actual processes, Process Analytics is a leading method, which is purely fact-based and thus objectively approaching the processes. It is not the employees who are asked, but the IT systems, which not only store all the business objects recorded in a table-oriented manner, but also all process activities. Every IT system for enterprise purposes log all relevant activities of the whole business process, in the background and invisible to the users, such as orders, invoices or customer orders, with a time stamp.

2 The right choice of the processes to analyze

Today almost every company works with at least one ERP system. As other systems are often used, it is clear which processes can not be analyzed: Those processes, which are still carried out exclusively on paper and in the minds of the employees, which are typical decision-making processes at the strategic level and not logged in IT systems.

Operational processes, however, are generally recorded almost seamlessly in IT systems. Furthermore, almost all operational decisions are recorded by status flags in datasets.

The operational processes, which can be reconstructed and analyzed with Process Mining very well and which are of equal interest from the point of view of compliance, include for example:

– Procurement

– Logistics / Transport

– Sales / Ordering

– Warranty / Claim Management

– Human Resource Management

Process Analytics enables the greatest possible transparency across all business processes, regardless of the sector and the department. Typical case IDs are, for example, sales order number, procurement order number, customer or material numbers.

3 Selection of relevant IT systems

In principle, every IT system used in the company should be examined with regard to the relevance for the process to be analyzed. As a rule, only the ERP system (SAP ERP or others) is relevant for the analysis of the purchasing processes. However, for other process areas there might be other IT systems interesting too, for example separate accounting systems, a CRM or a MES system, which must then also be included.

Occasionally, external data should also be integrated if they provide important process information from externally stored data sources – for example, data from logistics partners.

4 Data Preparation

Before the start of the data-driven process analysis, the data directly or indirectly indicating process activities must be identified, extracted and processed in the data sources. The data are stored in database tables and server logs and are collected via a data warehousing procedure and converted into a process protocol or – also called – event log.

The event log is usually a very large and wide table which, in addition to the actual process activities, also contains parameters which can be used to filter cases and activities. The benefit of this filter option is, for example, to show only process flows where special product groups, prices, quantities, volumes, departments or employee groups are involved.

5 Analysis Execution

The actual inspection is done visually and thus intuitively with an interactive process flow diagram, which represents the actual processes as they could be extracted from the IT systems. The event log generated by the data preparation is loaded into a data visualization software (e.g. Celonis PM Software), which displays this log by using the case IDs and time stamps and transforms this information in a graphical process network. The process flows are therefore not modeled by human “process thinkers”, as is the case with the target processes, but show the real process flows given by the IT systems. Process Mining means, that our enterprise databases “talk” about their view of the process.

The process flows are visualized and statistically evaluated so that concrete statements can be made about the process performance and risk estimations relevant to compliance.

6 Deviation from target processes

The possibility of intuitive filtering of the process presentation also enables an analysis of all deviation of our real process from the desired target process sequences.

The deviation of the actual processes from the target processes is usually underestimated even by IT-affine managers – with Process Analytics all deviations and the general process complexity can now be investigated.

6 Detection of process control violations

The implementation of process controls is an integral part of a professional internal control system (ICS), but the actual observance of these controls is often not proven. Process Analytics allows circumventing the dual control principle or the detection of functional separation conflicts. In addition, the deliberate removal of internal control mechanisms by executives or the incorrect configuration of the IT systems are clearly visible.

7 Detection of previously unknown behavioral patterns

After checking compliance with existing controls, Process Analytics continues to be used to recognize previously unknown patterns in process networks, which point to risks or even concrete fraud cases and are not detected by any control due to their previously unknown nature. In particular, the complexity of everyday process interlacing, which is often underestimated as already mentioned, only reveals fraud scenarios that would previously not have been conceivable.

8 Reporting – also possible in real time

As a highly effective audit analysis, Process Analytics is already an iterative test at intervals of three to twelve months. After the initial implementation, compliance violations, weak or even ineffective controls, and even cases of fraud, are detected reliably. The findings can be used in the aftermath to stop the weaknesses. A further implementation of the analysis after a waiting period makes it possible to assess the effectiveness of the measures taken.

In some application scenarios, the seamless integration of the process analysis with the visual dashboard to the IT system landscape is recommended so that processes can be monitored in near real-time. This connection can also be supplemented by notification systems, so that decision makers and auditors are automatically informed about the latest process bottlenecks or violations via SMS or e-mail.

Fazit

Process Analytics is, in the course of the digitalization, the highly effective methodology from the area of ​​Big Data Analysis for detecting compliance-relevant events throughout the company and also providing visual support for forensic data analysis. Since this is a method, and not a software, an expansion of the IT system landscape, especially for entry, is not absolutely necessary, but can be carried out by internal or external employees at regular intervals.

My Desk for Data Science

In my last post I anounced a blog parade about what a data scientist’s workplace might look like.

Here are some photos of my desk and my answers to the questions:

How many monitors do you use (or wish to have)?

I am mostly working at my desk in my office with a tower PC and three monitors.
I definitely need at least three monitors to work productively as a data scientist. Who does not know this: On the left monitor the data model is displayed, on the right monitor the data mapping and in the middle I do my work: programming the analysis scripts.

What hardware do you use? Apple? Dell? Lenovo? Others?

I am note an Apple guy. When I need to work mobile, I like to use ThinkPad notebooks. The ThinkPads are (in my experience) very robust and are therefore particularly good for mobile work. Besides, those notebooks look conservative and so I’m not sad if there comes a scratch on the notebook. However, I do not solve particularly challenging analysis tasks on a notebook, because I need my monitors for that.

Which OS do you use (or prefer)? MacOS, Linux, Windows? Virtual Machines?

As a data scientist, I have to be able to communicate well with my clients and they usually use Microsoft Windows as their operating system. I also use Windows as my main operating system. Of course, all our servers run on Linux Debian, but most of my tasks are done directly on Windows.
For some notebooks, I have set up a dual boot, because sometimes I need to start native Linux, for all other cases I work with virtual machines (Linux Ubuntu or Linux Mint).

What are your favorite databases, programming languages and tools?

I prefer the Microsoft SQL Server (T-SQL), C# and Python (pandas, numpy, scikit-learn). This is my world. But my customers are kings, therefore I am working with Postgre SQL, MongoDB, Neo4J, Tableau, Qlik Sense, Celonis and a lot more. I like to get used to new tools and technologies again and again. This is one of the benefits of being a data scientist.

Which data dou you analyze on your local hardware? Which in server clusters or clouds?

There have been few cases yet, where I analyzed really big data. In cases of analyzing big data we use horizontally scalable systems like Hadoop and Spark. But we also have customers analyzing middle-sized data (more than 10 TB but less than 100 TB) on one big server which is vertically scalable. Most of my customers just want to gather data to answer questions on not so big amounts of data. Everything less than 10TB we can do on a highend workstation.

If you use clouds, do you prefer Azure, AWS, Google oder others?

Microsoft Azure! I am used to tools provided by Microsoft and I think Azure is a well preconfigured cloud solution.

Where do you make your notes/memos/sketches. On paper or digital?

My calender is managed digital, because I just need to know everywhere what appointments I have. But my I prefer to wirte down my thoughts on paper and that´s why I have several paper-notebooks.

Now it is your turn: Join our Blog Parade!

So what does your workplace look like? Show your desk on your blog until 31/12/2017 and we will show a short introduction of your post here on the Data Science Blog!

 

Show your Data Science Workplace!

The job of a data scientist is often a mystery to outsiders. Of course, you do not really need much more than a medium-sized notebook to use data science methods for finding value in data. Nevertheless, data science workplaces can look so different and, let’s say, interesting. And that’s why I want to launch a blog parade – which I want to start with this article – where you as a Data Scientist or Data Engineer can show your workplace and explain what tools a data scientist in your opinion really needs.

I am very curious how many monitors you prefer, whether you use Apple, Dell, HP or Lenovo, MacOS, Linux or Windows, etc., etc. And of course, do you like a clean or messy desk?

What is a Blog Parade?

A blog parade is a call to blog owners to report on a specific topic. Everyone who participates in the blog parade, write on their blog a contribution to the topic. The organizer of the blog parade collects all the articles and will recap those articles in a short form together, of course with links to the articles.

How can I participate?

Write an article on your blog! Mention this blog parade here, show and explain your workplace (your desk with your technical equipment) in an article. If you’re missing your own blog, articles can also be posted directly to LinkedIn (LinkedIn has its own blogging feature that every LinkedIn member can use). Alternative – as a last resort – it would also be possible to send me your article with a photo about your workplace directly to: redaktion@data-science-blog.com.
Please make me aware of an article, via e-mail or with a comment (below) on this article.

Who can participate?

Any data scientist or anyone close to Data Science: Everyone concerned with topics such as data analytics, data engineering or data security. Please do not over-define data science here, but keep it in a nutshell, so that all professionals who manage and analyze data can join in with a clear conscience.

And yes, I will participate too. I will propably be the first who write an article about my workplace (I just need a new photo of my desk).

When does the article have to be finished?

By 31/12/2017, the article must have been published on your blog (or LinkedIn or wherever) and the release has to be reported to me.
But beware: Anyone who has previously written an article will also be linked earlier. After all, reporting on your article will take place immediately after I hear about it.
If you publish an artcile tomorrow, it will be shown the day after tomorrow here on the Data Science Blog.

What is in it for me to join?

Nothing! Except perhaps the fun factor of sharing your idea of ​​a nice desk for a data expert with others, so as to share creativity or a certain belief in what a data scientist needs.
Well and for bloggers: There is a great backlink from this data science blog for you 🙂

What should I write? What are the minimum requirements of content?

The article does not have to (but may be) particularly long. Anyway, here on this data science blog only a shortened version of your article will appear (with a link, of course).

Minimum requirments:

  • Show a photo (at least one!) of your workplace desk!
  • And tell us something about:
    • How many monitors do you use (or wish to have)?
    • What hardware do you use? Apple? Dell? Lenovo? Others?
    • Which OS do you use (or prefer)? MacOS, Linux, Windows? Virtual Machines?
    • What are your favorite databases, programming languages and tools? (e.g. Python, R, SAS, Postgre, Neo4J,…)
    • Which data dou you analyze on your local hardware? Which in server clusters or clouds?
    • If you use clouds, do you prefer Azure, AWS, Google oder others?
    • Where do you make your notes/memos/sketches. On paper or digital?

Not allowed:
Of course, please do not provide any information, which could endanger your company`s IT security.

Absolutly allowed:
Bringing some joke into the matter 🙂 We are happy to vote in the comments on the best or funniest desk for election, there may be also a winner later!


The resulting Blog Posts: https://data-science-blog.com/data-science-insights/show-your-desk/


 

The importance of domain knowledge – A healthcare data science perspective

Data scientists have (and need) many skills. They are frequently either former academic researchers or software engineers, with knowledge and skills in statistics, programming, machine learning, and many other domains of mathematics and computer science. These skills are general and allow data scientists to offer valuable services to almost any field. However, data scientists in some cases find themselves in industries they have relatively little knowledge of.

This is especially true in the healthcare field. In healthcare, there is an enormous amount of important clinical knowledge that might be relevant to a data scientist. It is unreasonable to expect a data scientist to not only have all of the skills typically required of a data scientist, but to also have all of the knowledge a medical professional may have.

Why is domain knowledge necessary?

This lack of domain knowledge, while perfectly understandable, can be a major barrier to healthcare data scientists. For one thing, it’s difficult to come up with project ideas in a domain that you don’t know much about. It can also be difficult to determine the type of data that may be helpful for a project – if you want to build a model to predict a health outcome (for example, whether a patient has or is likely to develop a gastrointestinal bleed), you need to know what types of variables might be related to this outcome so you can make sure to gather the right data.

Knowing the domain is useful not only for figuring out projects and how to approach them, but also for having rules of thumb for sanity checks on the data. Knowing how data is captured (is it hand-entered? Is it from machines that can give false readings for any number of reasons?) can help a data scientist with data cleaning and from going too far down the wrong path. It can also inform what true outliers are and which values might just be due to measurement error.

Often the most challenging part of building a machine learning model is feature engineering. Understanding clinical variables and how they relate to a health outcome is extremely important for this. Is a long history of high blood pressure important for predicting heart problems, or is only very recent history? How long a time horizon is considered ‘long’ or ‘short’ in this context? What other variables might be related to this health outcome? Knowing the domain can help direct the data exploration and greatly speed (and enhance) the feature engineering process.

Once features are generated, knowing what relationships between variables are plausible helps for basic sanity checks. If you’re finding the best predictor of hospitalization is the patient’s eye color, this might indicate an issue with your code. Being able to glance at the outcome of a model and determine if they make sense goes a long way for quality assurance of any analytical work.

Finally, one of the biggest reasons a strong understanding of the data is important is because you have to interpret the results of analyses and modeling work. Knowing what results are important and which are trivial is important for the presentation and communication of results. An analysis that determines there is a strong relationship between age and mortality is probably well-known to clinicians, while weaker but more surprising associations may be of more use. It’s also important to know what results are actionable. An analysis that finds that patients who are elderly are likely to end up hospitalized is less useful for trying to determine the best way to reduce hospitalizations (at least, without further context).

How do you get domain knowledge?

In some industries, such as tech, it’s fairly easy and straightforward to see an end-user’s prospective. By simply viewing a website or piece of software from the user’s point of view, a data scientist can gain a lot of the needed context and background knowledge needed to understand where their data is coming from and how their model output is being used. In the healthcare industry, it’s more difficult. A data scientist can’t easily choose to go through med school or the experience of being treated for a chronic illness. This means there is no easy single answer to where to gain domain knowledge. However, there are many avenues available.

Reading literature and attending presentations can boost one’s domain knowledge. However, it’s often difficult to find resources that are penetrable for someone who is not already a clinician. To gain deep knowledge, one needs to be steeped in the topic. One important avenue to doing this is through the establishment of good relationships with clinicians. Clinicians can be powerful allies that can help point you in the right direction for understanding your data, and simply by chatting with them you can gain important insights. They can also help you visit the clinics or practices to interact with the people that perform the procedures or even watch the procedures being done. At Fresenius Medical Care, where I work, members of my team regularly visit clinics. I have in the last year visited one of our dialysis clinics, a nephrology practice, and a vascular care unit. These experiences have been invaluable to me in developing my knowledge of the treatment of chronic illnesses.

In conclusion, it is crucial for data scientists to acquire basic familiarity in the field they are working in and in being part of collaborative teams that include people who are technically knowledgeable in the field they work in. This said, acquiring even an essential understanding (such as “Medicine 101”) may go a long way for the data scientists in being able to become self-sufficient in essential feature selection and design.

 

Is Data Science the new Statistics?

Table of Contents

1 Introduction

2 Emerging of Data Science

3 Big data technologies

4 Two data worlds: Predictive vs inferential statistics

5 How to study data science

6 Conclusions

7 References

Introduction

As a student of Statistics and the winner of Data Science Scholarship I am often surrounded by computer scientists, mathematicians, physicists and of course statisticians. During conversation, I was asked questions such as “So what actually do I do? What is Data Science?”. These are some very difficult questions and as like you will see during reading this document many before me tried to answer those questions. There is a dispute between statisticians and computer scientists what is the origin of data science and who should teach it. According to the Institute of Mathematical Statistics in the: “The IMS presidential address: let us own data science” we can find a simple recipe for data scientist. [1]

“Putting the traits of Turner and Carver together gives a good portrait of a data scientist:

  • Statistics (S)
  • Domain/Science knowledge (D)
  • Computing (C)
  • Collaboration/teamwork (C)
  • Communication to outsiders (C)

That is, data science = SDCCC = S DC3

However, despite all the challenges that I will need to overcome in answering those questions I will try to do it. I will refer to ideas from several reputable sources, in which I will also tell you: what is in the data science that I am really fascinated about? What is magical in this creation of statistics and computer science that I am drawn to?

Emerging of Data Science

On Tuesday, the 8th of September 2015, University of Michigan announced the 100 million dollars “Data Science Initiative” (DSI), hired 35 new faculty members. On the DSI website we can read about this initiative:

“This coupling of scientific discovery and practice involves the collection, management, processing, analysis, visualisation, and interpretation of vast amounts of heterogeneous data associated with a diverse array of scientific, translational and interdisciplinary applications”2

But that sounds like a bread and butter for statisticians. So, is it really a new creation or is it something that exists for many years but it didn’t sound so sexy as data science? In the article written by Karl Broman, (the University of Wisconsin) we can read:

“When physicists do mathematics, they’re don’t say they’re doing “number science”. They’re doing math. If you’re analyzing data, you’re doing statistics. You can call it data science or informatics or analytics or whatever, but it ‘s still statistics. If you say that one kind of data analysis is statistics and another kind is not, you’re not allowing innovation. We need to define the field broadly. You may not like what some statisticians do. You may feel they don’t share your values. They may embarrass you. But that shouldn’t lead us to abandon the term “statistics”.

Reading the definition of data science on the Data Science Association’s “Professional Code of Conduct”:

“Data scientist means a professional who uses scientific methods to liberate and create meaning from raw data”

These sound like K. Browman maybe right. Maybe I should go on MSc Statistics like many before me did. Maybe Data Science is simply a new sexy name for statistician only data is big, technology more advanced rather than it used to be so you need to have programming skills to handle the data. Maybe let say loudly data science is a modern version of statistics? But maybe not? Because we can also find statements like the following:

“Statistics is the least important part of data science”. [3]

Further, we can read:

“There ‘s so, much that goes on with data that is about computing, not statistics. I do think it would be fair to consider statistics (which includes sampling, experimental design, and data collection as well as data analysis (which itself includes model building, visualization, and model checking as well as inference)) as a subset of data science. . . .”.[3]

So maybe people from computer science are right. Maybe I should go and study programming and forget about expanding my knowledge in statistics? After all, we all know that computer science always had much bigger funding and having MSc computer science was always like a magic star for employers. What should I do? Let me research further.

Big data technologies

Is the data size important to distinguish between data science and statistics? Going back to the “Let us own data science” article we can read that a statistician, Hollerith, invented the punched card reader to allow e cient compilation of a US census, the first elements of machine learning. So, no, machine learning is not an invention of computer scientists. It was well known for statistician for decades already. What about different techniques used in DOE (Design of Experiments) or sampling methods to decrease the sample size. If the data used by statisticians would be only small they wouldn’t have to discover methods such PCA (Principle component analysis) or dimensionality reduction techniques. So, no, data can be big and/or small for statisticians, so what is the difference between data science and statistics and what department should I choose?

When I spoke to computer scientists they try to convince me to choose computer science department. Their reasons being that there are many different programmes that I need to know to deal with large datasets. For instance: Java, Hadoop, SQL, Python, and much more. Moreover, programming can only be taught to the best standard through computer science courses Is it true? Can’t we do the same calculations using statistical software such as R, SAS or even Matlab? But on the other hand, doesn’t the newest technology always work faster? And if so, wouldn’t be better to use the newest technology when we program and write loops?

But, I don’t want to underestimate the effort made by statisticians and data analyst over last 50 years in developing statistical programmes. Their efforts have resulted in the emergence of today’s technology. Early statistical packages such as SPSS or Minitab (from 1960’s) allowed to develop more advanced programmes having roots in mini computer era such as STATA or my favourite R which in turn allowed progress to advanced technology even further and create Python, Hadoop, SQL and so on. Becker and Chambers (with S) and later Ihaka, Gentleman, and members of the R Core team (with R) worked on developing the statistical software. These names should be convincing about how powerful statistical programming languages can be. Many operations that we can do in Hadoop or SQL we can also do easily in R.

Two data worlds: Predictive vs inferential statistics

So maybe Data Science is a creature merged by statisticians working on computer science department? Maybe there are two different approaches to statistics: mathematical statistics and computer science statistics and the computer science statisticians are data scientists because according to Yanir Seroussi in his blog:

“A successful data scientist needs to be able to “become one with the data” by exploring it and applying rigorous statistical analysis (right-hand side of the continuum). But good data scientists also understand what it takes to deploy production systems, and are ready to get their hands dirty by writing code that cleans up the data or performs core system functionality (lefthand side of the continuum). Gaining all these skills takes time.”[4]

Okay, so my reasoning that some statisticians work on computer science department is right, as well as there exists subject like computational statistics, so maybe I should go for computer science department but study statistics.

In fact, I am not the first one to arrive at the conclusion. Everything started from a confession made by John Tukey in “The Future of Data Analysis” article published in “The Annals of Mathematical Statistics” :

For a long time, I have thought I was a statistician, interested in inferences from the particular to the general. But as I have watched mathematical statistics evolve, I have had cause to wonder and to doubt. … All in all I have come to feel that my central interest is in data analysis, which I take to include, among other things: procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data

If I am right then above confession was a critical moment. The time when mathematical statistics become more inferential and computational statistics concentrated more on predictive statistics. Applied statisticians working on predictive analytics that are more interested in applying the knowledge rather than developing long proofs decided to move on computer science department.

Additionally, the following is crucial discussion made by Leo Biermann in his paper published in Statistical Science titled “Statistical modelling: the two cultures”. It enables us to understand and differentiate views from both types of statistician, namely mathematical and statistical.

Statistics starts with data. Think of the data as being generated by a black box in which a vector of input variables x (independent variables) go in one side, and on the other side the response variables y come out. Inside the black box, nature functions to associate the predictor variables with the response variables … There are two goals in analyzing the data:

  • Prediction. To be able to predict what the responses are going to be to future input variables
  • InferenceTo [infer] how nature is associating the response variables to the input variables.”

Furthermore, in the same dispute we can read:

“The statistical community has been committed to the almost exclusive use of [generative] models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. [Predictive] modeling, both in theory and practice, has developed rapidly in fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on [generative] models …”

So, we can say that Data Science evolved from Predictive Analytics which in turn evolved from Statistics but it becomes separate science. Tukey and Wilk 1969 compared this new science to established sciences and further circumscribed the role of Statistics within it:

“ … data analysis is a very di cult field. It must adapt itself to what people can and need to do with data. In the sense that biology is more complex than physics, and the behavioural sciences are more complex than either, it is likely that the general problems of data analysis are more complex than those of all three. It is too much to ask for close and effective guidance for data analysis from any highly formalized structure, either now or in the near future. Data analysis can gain much from formal statistics, but only if the connection is kept adequately loose”

How to study data science

So, what is exactly predictive analytics culture? I think that everyone who used Kaggle competition before can agree with me that description of common task framework (CTF) formulated by Marc Liberman in 2009 is a perfect description of Kaggle competitions, and hackathons events; where latter has worked as training sessions for newbies in the data world. An instance of the CTF has these ingredients:

  1. A publicly available training data set involving, for each observation, a list of (possibly many) feature measurements, and a class label for that observation.
  2. A set of enrolled competitors whose common task is to infer a class prediction rule from the training data.
  3. A scoring referee, to which competitors can submit their prediction rule. The referee runs the prediction rule against a testing dataset which is sequestered behind a Chinese wall. The referee objectively and automatically reports the score (prediction accuracy) achieved by the submitted rule

Kaggle competitions are not only training platforms for newbies like me but also very challenging statistical competitions where experienced statisticians can win “pocket money”. A famous example is the Netflix Challenge where the common task was to predict Netflix user movie selection. The winning team (which included ATT Statistician Bob Bell) won 1 mln dollars.

Comparing modules that are available on master in data science at University of Berkley[6]:

  1. Both
  • Applied machine learning
  • Experiments and causality
  1. Statistics
  • Research design and application for data and analysis
  • Statistics for Data Science
  • Behind the data: humans and values
  • Statistical methods for discrete response, Time Series and panel data
  • Data visualisation
  1. Computer Science
  • Python for Data Science
  • Storing and Retrieving Data
  • Scalling up! Really Big Data
  • Machine Learning at scale
  • Natural Language Processing with Deep Learning

We can really see that data science is a subject that demands skills from both computer science and statistics. So, it is another confirmation for me that it is the best time to change department for my postgraduate study, that is, to study statistics on computer science department.

In the 50 Years of Data Science article we can read: “The activities of Greater Data Science are classified into 6 divisions:

  1. Data exploration and preparation
  2. Data representation and transformation
  3. Computing with data
  4. Data visualization and presentation
  5. Data Modelling
  6. Science about data science [5]

I will quickly go through all of them using my Ebola research example, this required using machine learning on time series data.

  1. The most demanding part. Many people told me before starting this project that: collecting, cleaning, wrangling and preparing data take 60% of all the time that you need to spend on data science project. I didn’t realise how much this 60% means in real time. I didn ‘t realise that the 60 percent will take so much time and that after this I will be exhausted. Exhausted but ready for the next step.
  2. This point is actually part of the first one, or maybe just like many other things in statistics: everything is one huge connected bunch.Data that you can find can be very nice, well behaving, written in CSV or JSON or any other format file that you can quickly download and use, but what if not? What if your data is ‘dirty’and not stored as a file (e.g. only appear on a website)? What if data is coded? Do you need to decode it?
  3. The even bigger challenge, but what a fun? You need to know a few different programming languages or least as I do know a little bit of R, a little bit of Python, quite well Tableau and Excel. So you can use different program in different scenarios or for different tasks. For example, using Panda to do EDA and ggplot 2 to do data vis.
  4. Graphs are pretty, right? If you are still reading my article, I bet you know what is heat map, spatial vis in big cities or different infographics. Surely, I would like to highlight, that we respect only the ones that are not only pretty but also valid. Nevertheless, time that is required to create these visualisations is another matter.
  5. The data modelling, finally? I don’t need to say a lot about this. All forms of inferential and predictive analytic are allowed and accepted.
  6. My favourite part, not the end yet. All the conferences and meetups that I can attend on. All the seminars where we all present our current projects.

Conclusions

After graduation, I will be graduated Statistician. Even more, I will be a mathematical statistician whom mostly during degree dealt with inferential statistics. On the other hand, winning data science scholarship gave me exposure to predictive analytic which I highly enjoyed. Therefore, for my next stage, I will just change my department and concentrate more on predictive analytic. There are many statisticians working on computer science department. They possess both statistical knowledge and advanced software engineering skills, they are called data scientists. It would be a pleasure for me to join them. I don’t mind if it will be MSc. Computer Science, MSc. Data Science, MSc. Big Data or whatever the name will be. I do mind to have sufficient exposure to deal with “dirty” data using statistical modelling and machine learning using modern technology. This is what data science is for me. Maybe for you, it will be something else. Maybe you will be more satisfied with expanding massively programming skills. But for me, programming is a tool, modern technology is my friend and my bread and butter will be predictive analytic.

References

  1. IMS Presidential Address: Let us own data science
  2. Data science is statistics
  3. A Gelman, Columbia University
  4. Yanir Seroussi: What is data Science?
  5. 50 Years Data Science
  6. Curriculum: data science@Berkley