Tag Archive for: Career

Benjamin Aunkofer

Jobprofil des Data Engineers

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Warum Data Engineering der Data Science in Bedeutung und Berufschancen längst die Show stiehlt, dabei selbst ebenso einem stetigen Wandel unterliegt.

Was ein Data Engineer wirklich können muss

Der Data Scientist als sexiest Job des 21. Jahrhunderts? Mag sein, denn der Job hat seinen ganz speziellen Reiz, auch auf Grund seiner Schnittstellenfunktion zwischen Technik und Fachexpertise. Doch das Spotlight der kommenden Jahre gehört längst einem anderen Berufsbild aus der Datenwertschöpfungskette – das zeigt sich auch bei den Gehältern.

Viele Unternehmen sind gerade auf dem Weg zum Data-Driven Business, einer Unternehmensführung, die für ihre Entscheidungen auf transparente Datengrundlagen setzt und unter Einsatz von Business Intelligence, Data Science sowie der Automatisierung mit Deep Learning und RPA operative Prozesse so weit wie möglich automatisiert. Die Lösung für diese Aufgabenstellungen werden oft vor allem bei den Experten für Prozessautomatisierung und Data Science gesucht, dabei hängt der Erfolg jedoch gerade viel eher von der Beschaffung valider Datengrundlagen ab, und damit von einer ganz anderen entscheidenden Position im Workflow datengetriebener Entscheidungsprozesse, dem Data Engineer.

Data Engineer, der gefragteste Job des 21sten Jahrhunderts?

Der Job des Data Scientists hingegen ist nach wie vor unter Studenten und Absolventen der MINT-Fächer gerade so gefragt wie nie, das beweist der tägliche Ansturm der vielen Absolventen aus Studiengängen rund um die Data Science auf derartige Stellenausschreibungen. Auch mangelt es gerade gar nicht mehr so sehr an internationalen Bewerben mit Schwerpunkt auf Statistik und Machine Learning. Der solide ausgebildete und bestenfalls noch deutschsprachige Data Scientist findet sich zwar nach wie vor kaum im Angebot, doch insgesamt gute Kandidaten sind nicht mehr allzu schwer zu finden. Seit Jahren sind viele Qualifizierungsangebote für Studenten sowie Arbeitskräfte am Markt auch günstig und ganz flexibel online verfügbar, ohne dabei Abstriche bei beim Ansehen dieser Aus- und Fortbildungsmaßnahmen in Kauf nehmen zu müssen.

Was ein Data Scientist fachlich in Sachen Expertise alles abdecken muss, hatten wir ganz ausführlich über Betrachtung des Data Science Knowledge Stack besprochen.

Doch was bringt ein Data Scientist, wenn dieser gar nicht über die Daten verfügt, die für seine Aufgaben benötigt werden? Sicherlich ist die Aufgabe eines jeden Data Scientists auch die Vorbereitung und Präsentation seiner Vorhaben. Die Heranschaffung und Verwaltung großer Datenmengen in einer Enterprise-fähigen Architektur ist jedoch grundsätzlich nicht sein Schwerpunkt und oft fehlen ihm dafür auch die Berechtigungen in einer Enterprise-IT. Noch konkreter wird der Bedarf an Datenbeschaffung und -aufbereitung in der Business Intelligence, denn diese benötigt für nachhaltiges Reporting feste Strukturen wie etwa ein Data Warehouse.

Das Profil des Data Engineers: Big Data High-Tech

Auch wenn Data Engineering von Hochschulen und Fortbildungsanbietern gerade noch etwas stiefmütterlich behandelt werden, werden der Einsatz und das daraus resultierende Anforderungsprofil eines Data Engineers am Markt recht eindeutig skizziert. Einsatzszenarien für diese Dateningenieure – auch auf Deutsch eine annehmbare Benennung – sind im Kern die Erstellung von Data Warehouse und Data Lake Systeme, mittlerweile vor allem auf Cloud-Plattformen. Sie entwickeln diese für das Anzapfen von unternehmensinternen sowie -externen Datenquellen und bereiten die gewonnenen Datenmengen strukturell und inhaltlich so auf, dass diese von anderen Mitarbeitern des Unternehmens zweckmäßig genutzt werden können.

Enabler für Business Intelligence, Process Mining und Data Science

Kein Data Engineer darf den eigentlichen Verbraucher der Daten aus den Augen verlieren, für den die Daten nach allen Regeln der Kunst zusammengeführt, bereinigt und in das Zielformat gebracht werden sollen. Klassischerweise arbeiten die Engineers am Data Warehousing für Business Intelligence oder Process Mining, wofür immer mehr Event Logs benötigt werden. Ein Data Warehouse ist der unter Wasser liegende, viel größere Teil des Eisbergs der Business Intelligence (BI), der die Reports mit qualifizierten Daten versorgt. Diese Eisberg-Analogie lässt sich auch insgesamt auf das Data Engineering übertragen, der für die Endanwender am oberen Ende der Daten-Nahrungskette meistens kaum sichtbar ist, denn diese sehen nur die fertigen Analysen und nicht die dafür vorbereiteten Datentöpfe.

Abbildung 1 - Data Engineering ist der Mittelpunkt einer jeden Datenplattform. Egal ob für Data Science, BI, Process Mining oder sogar RPA, die Datenanlieferung bedingt gute Dateningenieure, die bis hin zur Cloud Infrastructure abtauchen können.

Abbildung 1 – Data Engineering ist der Mittelpunkt einer jeden Datenplattform. Egal ob für Data Science, BI, Process Mining oder sogar RPA, die Datenanlieferung bedingt gute Dateningenieure, die bis hin zur Cloud Infrastructure abtauchen können.

Datenbanken sind Quelle und Ziel der Data Engineers

Daten liegen selten direkt in einer einzigen CSV-Datei strukturiert vor, sondern entstammen einer oder mehreren Datenbanken, die ihren eigenen Regeln unterliegen. Geschäftsdaten, beispielsweise aus ERP- oder CRM-Systemen, liegen in relationalen Datenbanken vor, oftmals von Microsoft, Oracle, SAP oder als eine Open-Source-Alternative. Besonders im Trend liegen derzeitig die Cloud-nativen Datenbanken BigQuery von Google, Redshift von Amazon und Synapse von Microsoft sowie die cloud-unabhängige Datenbank snowflake. Dazu gesellen sich Datenbanken wie der PostgreSQL, Maria DB oder Microsoft SQL Server sowie CosmosDB oder einfachere Cloud-Speicher wie der Microsoft Blobstorage, Amazon S3 oder Google Cloud Storage. Welche Datenbank auch immer die passende Wahl für das Unternehmen sein mag, ohne SQL und Verständnis für normalisierte Daten läuft im Data Engineering nichts.

Andere Arten von Datenbanken, sogenannte NoSQL-Datenbanken beruhen auf Dateiformaten, einer Spalten- oder einer Graphenorientiertheit. Beispiele für verbreitete NoSQL-Datenbanken sind MongoDB, CouchDB, Cassandra oder Neo4J. Diese Datenbanken exisiteren nicht nur als Unterhaltungswert gelangweilter Nerds, sondern haben ganz konkrete Einsatzgebiete, in denen sie jeweils die beste Performance im Lesen oder Schreiben der Daten bieten.

Ein Data Engineer muss demnach mit unterschiedlichen Datenbanksystemen zurechtkommen, die teilweise auf unterschiedlichen Cloud Plattformen heimisch sind.

Data Engineers brauchen Hacker-Qualitäten

Liegen Daten in einer Datenbank vor, können Analysten mit Zugriff einfache Analysen bereits direkt auf der Datenbank ausführen. Doch wie bekommen wir die Daten in unsere speziellen Analyse-Tools? Hier muss der Engineer seinen Dienst leisten und die Daten aus der Datenbank exportieren können. Bei direkten Datenanbindungen kommen APIs, also Schnittstellen wie REST, ODBC oder JDBC ins Spiel und ein guter Data Engineer benötigt Programmierkenntnisse, bevorzugt in Python, diese APIs ansprechen zu können. Etwas Kenntnis über Socket-Verbindungen und Client-Server-Architekturen zahlt sich dabei manchmal aus. Ferner sollte jeder Data Engineer mit synchronen und asynchronen Verschlüsselungsverfahren vertraut sein, denn in der Regel wird mit vertraulichen Daten gearbeitet. Ein Mindeststandard an Sicherheit gehört zum Data Engineering und darf keinesfalls nur Datensicherheitsexperten überlassen werden, eine Affinität zu Netzwerksicherheit oder gar Penetration-Testing ist positiv zu bewerten, mindestens aber ein sauberes Berechtigungsmanagement gehört zu den Grundfähigkeiten. Viele Daten liegen nicht strukturiert in einer Datenbank vor, sondern sind sogenannte unstrukturierte oder semi-strukturierte Daten aus Dokumenten oder aus Internetquellen. Mit Methoden wie Data Web Scrapping und Data Crawling sowie der Automatisierung von Datenabrufen beweisen herausragende Data Engineers sogar echte Hacker-Qualitäten.

Dirigent der Daten: Orchestrierung von Datenflüssen

Eine der Kernaufgaben des Data Engineers ist die Entwicklung von ETL-Strecken, um Daten aus Quellen zu Extrahieren, zu in das gewünschte Zielformat zu Transformieren und schließlich in die Zieldatenbank zu Laden. Dies mag erstmal einfach klingen, wird jedoch zur echten Herausforderung, wenn viele ETL-Prozesse sich zu ganzen ETL-Ketten und -Netzwerken zusammenfügen, diese dabei trotz hochfrequentierter Datenabfrage performant laufen müssen. Die Orchestrierung der Datenflüsse kann in der Regel in mehrere Etappen unterschieden werden, von der Quelle ins Data Warehouse, zwischen den Ebenen im Data Warehouse sowie vom Data Warehouse in weiterführende Systeme, bis hin zum Zurückfließen verarbeiteter Daten in das Data Warehouse (Reverse ETL).

Hart an der Grenze zu DevOp: Automatisierung in Cloud-Architekturen

In den letzten Jahren sind Anforderungen an Data Engineers deutlich gestiegen, denn neben dem eigentlichen Verwalten von Datenbeständen und -strömen für Analysezwecke wird zunehmend erwartet, dass ein Data Engineer auch Ressourcen in der Cloud managen, mindestens jedoch die Datenbanken und ETL-Ressourcen. Darüber hinaus wird zunehmend jedoch verlangt, IT-Netzwerke zu verstehen und das ganze Zusammenspiel der Ressourcen auch als Infrastructure as Code zu automatisieren. Auch das automatisierte Deployment von Datenarchitekturen über CI/CD-Pipelines macht einen Data Engineer immer mehr zum DevOp.

Zukunfts- und Gehaltsaussichten

Im Vergleich zum Data Scientist, der besonders viel Methodenverständnis für Datenanalyse, Statistik und auch für das zu untersuchende Fachgebiet benötigt, sind Data Engineers mehr an Tools und Plattformen orientiert. Ein Data Scientist, der Deep Learning verstanden hat, kann sein Wissen zügig sowohl mit TensorFlow als auch mit PyTorch anwenden. Ein Data Engineer hingegen arbeitet intensiver mit den Tools, die sich über die Jahre viel zügiger weiterentwickeln. Ein Data Engineer für die Google Cloud wird mehr Einarbeitung benötigen, sollte er plötzlich auf AWS oder Azure arbeiten müssen.

Ein Data Engineer kann in Deutschland als Einsteiger mit guten Vorkenntnissen und erster Erfahrung mit einem Bruttojahresgehalt zwischen 45.000 und 55.000 EUR rechnen. Mehr als zwei Jahre konkrete Erfahrung im Data Engineering wird von Unternehmen gerne mit Gehältern zwischen 50.000 und 80.000 EUR revanchiert. Darüber liegen in der Regel nur die Data Architects / Datenarchitekten, die eher in großen Unternehmen zu finden sind und besonders viel Erfahrung voraussetzen. Weitere Aufstiegschancen für Data Engineers sind Berater-Karrieren oder Führungspositionen.

Wer einen Data Engineer in Festanstellung gebracht hat, darf sich jedoch nicht all zu sicher fühlen, denn Personalvermittler lauern diesen qualifizierten Fachkräften an jeder Ecke des Social Media auf. Gerade in den Metropolen wie Berlin schaffen es längst nicht alle Unternehmen, jeden Data Engineer über Jahre hinweg zu beschäftigen. Bei der großen Auswahl an Jobs und Herausforderungen fällt diesen Datenexperten nicht schwer, seine Gehaltssteigerungen durch Jobwechsel proaktiv voranzutreiben.

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Arianna Lupi

How To Perform High-Quality Data Science Job Assessments in 4 Steps

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In 2009, Google Chief Economist Hal Varian said to the McKinsey Quarterly that “the sexy job in the next 10 years will be statisticians.” At the time, it was hard to believe. But more than a decade later, we can’t get around the importance of data. Where once oil ruled the world, data is now catching up—quickly. That calls for more and better data scientists. In this article, we’ll explain to you how to find them.

Why is it so hard to find good data scientists?

The demand for data scientist roles has increased by 650 percent since 2012, and that number will continue to grow as the amount of data—and power it holds—grows steadily, too.

But unsurprisingly, there hasn’t been an increase of 650 percent in available data scientists on the job market. Even though the job is a lot sexier—and better paid—than ten years ago, many employers are still struggling to fill their empty seats with talented data scientists.  McKinsey predicted that there would be a shortage of between 140,000 and 190,000 people with analytical skills in the U.S. alone in 2018, and even in 2022 good data scientists, data analysts, forecasting analysts, modelling analysts, machine learning scientists, are hard to find.  Add to that another 1.5 million managers who will also need to at least understand how data analysis drives decision-making, and you can see how employers can be in a bit of a pickle.

Why thoroughly screening data scientists is still crucial

Even though demand is growing much faster than the number of data scientists, companies can’t simply settle for the first data lover who’s available from Monday to Friday. It’s no longer the company with the most data that wins the game. The ones who are taking the lead are the ones that are able to get the most out of data. They can pull valuable information that helps with decision-making and innovation out of even the smallest pieces of data—and they’re right, over and over again. This is why it’s vital to check if applicants have the skills you need to derive valuable input out of data. You’ll be basing a lot of business decisions on what these data scientists tell you, so best make sure they’re right.

But what makes someone a great data scientist? Some people turn their life around and go from being a maths teacher to following a 12-week data science boot camp or online data science course and quickly get the hang of it—others are top of their class, but aren’t confident enough data scientists to inform your business on its next big move. The truth is that the skills a valuable data scientist has, will have to develop over the years. It’s not just the data literacy, hard skills and the brain for maths—they’ll also need to be able to present and communicate their findings the right way.

Finding the right data scientists using a data science job assessment

So, you’ll want to choose your data scientists carefully, but how do you do that? Resumes and portfolios might seem impressive, but how do you actually find out if someone has the skills you’re looking for—especially if you don’t have anyone on board yet that knows what to ask. The easiest and most effective thing to do, is to screen candidates early in the process, using a data science test that’s been created by a real-life expert. This will ensure that relevant questions are being asked, and you get a clear idea of who’s worth going through the hiring process with — and who isn’t. In this article, we’ll walk you through four steps that will help you set up a data science job assessment that is of real value to your hiring managers. Let’s get started.

Step 1: Choose the right platform

You could, of course, draw up an online survey and create a test in there to send out to all applicants, but these might be hard to ‘grade’—although you’ll develop a tremendous respect for teachers along the way. In many cases, it’s better to choose a dedicated platform that has tests available, and will help you swift through the results effortlessly.

Before you start looking for platforms, make a list of absolute needs that you won’t compromise on. Ask yourself at least the following questions:

  • What types of tests are you looking for? Only hard skills, or also soft skills? If you need both, look for a platform that offers both—mixing and matching can be time-consuming.
  • Will there be tests readily available, or are you looking for a platform that allows you to create your own tests?
  • Does the platform have experience with companies like yours?
  • How are the tests presented to candidates, and how do you want the test results presented to your hiring managers?
  • And last but not least: what are you willing to spend on a job assessment platform? Do they charge per candidate, a flat fee, or would you prefer an annual subscription?

Once you’ve chosen a platform that is right for you, the fun can begin.

Step 2: Start with a hard skills assessment

For roles like data scientists, you’ll be initially focusing on whether they possess the right hard skills. Depending on the specific role, you can test core data science topics such as:

Statistics

You’re expecting your future data scientist to be fluent in statistics. Depending on the level you’re hiring at, you might want to throw in a few questions that quickly test how fast someone can see through the woods in a mess of statistics, and if they can interpret them the right way.

Machine learning

For some more senior roles, machine learning is becoming increasingly important in the world of data science. If this is the case for the role you’re hiring for, test to see if someone knows how to use data to feed it to machine learning and build awesome products.

Neural networks

A big part of data science is knowing how to work with neural networks. Neural networks are a way to solve problems through trial and error, based on human and animal brains. It’s incredibly helpful if your data scientist’s brain can use them.

Deep learning

Deep learning is a subfield of machine learning that can be necessary in specific data science roles. It works more closely to the way the human brain makes decisions, so this will require a specific set of test questions.

Collecting data

All that data has to come from somewhere, right? Your data scientists should not only be able to read and process data, but also know where and how to get the most valuable input. For this, include some questions about data extraction, data transformation, and data loading. This can also include tests on Excel and querying languages like SQL.

Storing data

Databases should look nothing like the average teenage bedroom. Meaning that they should be nice and tidy, making it easier to extract valuable information from them. Since data isn’t just numbers, but can be anything from video to reviews, it’s crucial that you hire a data scientist who knows how to store this correctly.

Analyzing and modeling data

Data wrangling, data exploration, analysis, and modeling need in-depth understanding of math and programming, but luckily, even data scientists get some help.

Data scientists use analytical tools like Apache Spark, D3.js Python, and many, many more to analyze all that data. If you’re using a specific one in your company and want your data scientists to be able to hit the ground running, quickly test if they’re actually able to use the tools they list on their resume.

Visualizing and presenting data

At the end of the day, data scientists will have to be able to communicate their findings to other departments with people who are less data-savvy. For this, they often use tools that help them visualize data to explain it in a more easy-to-grasp way.

Test if your next data scientist is able to do that with a quick check on their skills in tools like Tableau, PowerBI, Plotly, Bokeh, or whichever one you use.

Step 3: Continue with a soft skill assessment

Your friendly neighborhood data scientist should not only be a math genius, they should possess the right soft skills too. If they’re impossible to work with, you won’t reap the benefits of their skill set. Productivity will suffer, and team morale might also take a hit. Here are some soft skills to test your candidates on:

  • Business-oriented: ultimately, your data scientist will be fueling your decision-making process. This means they’ll have to have a good head for business, on top of simply understanding the numbers.
  • Communication skills: sure, everyone in your company preferably has some of these, but since data scientists play such an important role in decision-making, you’ll want them to be able to express themselves well—and listen to what you’re asking from them.
  • Teamwork: your data scientists shouldn’t be on a little island somewhere in the company. The more they integrate with other departments, the easier it is for them to determine what your business needs from them.
  • Critical thinking skills: this one’s pretty self-explanatory, but the more critical your data scientist, the more reassurance you’ll have that data is correctly interpreted.
  • Creativity: data is less dry than it seems. From data storage to finding connections and problem-solving: it all requires some form of creative thinking.

Step 4: Follow up on the test results

If you want to make the most of your data science job assessment, it shouldn’t just be a test to see who goes through to the next round. For the candidates that ‘pass’, you can customize the questions in their follow-up interview based on the strengths and weaknesses they showed in their test. Because the test they took says a lot, but at the same time—it’s just a snapshot. Did they score remarkably high on certain skills? Ask them how they got to be so experienced in that, and what projects contributed most to that.

Did you notice that they struggled with questions about X? Ask how they are planning to improve on that and how they make sure this doesn’t impact the quality of their work for the time being—are they calling in help from a peer, or do they simply take more time to figure things out?

These types of follow-up questions steer a job interview in a much more real-life direction: it’s not a generic set of questions that any company could ask any employee, but a real conversation between you and the candidate, in which you can evaluate if they fit in the future of the company—and if your company fits in theirs.

Ready to start the hiring process?

With these tips, we’re sure you’ll get some extra reassurance that your next hire will be a great fit—not just based on their previous experience and a couple of interviews. If you want, you can keep reading about data science jobs—or simply start hiring. Good luck!

Benjamin Aunkofer

Data Science Knowledge Stack – Abstraction of the Data Science Skillset

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What must a Data Scientist be able to do? Which skills does as Data Scientist need to have? This question has often been asked and frequently answered by several Data Science Experts. In fact, it is now quite clear what kind of problems a Data Scientist should be able to solve and which skills are necessary for that. I would like to try to bring this consensus into a visual graph: a layer model, similar to the OSI layer model (which any data scientist should know too, by the way).
I’m giving introductory seminars in Data Science for merchants and engineers and in those seminars I always start explaining what we need to work out together in theory and practice-oriented exercises. Against this background, I came up with the idea for this layer model. Because with my seminars the problem already starts: I am giving seminars for Data Science for Business Analytics with Python. So not for medical analyzes and not with R or Julia. So I do not give a general knowledge of Data Science, but a very specific direction.

A Data Scientist must deal with problems at different levels in any Data Science project, for example, the data access does not work as planned or the data has a different structure than expected. A Data Scientist can spend hours debating its own source code or learning the ropes of new DataScience packages for its chosen programming language. Also, the right algorithms for data evaluation must be selected, properly parameterized and tested, sometimes it turns out that the selected methods were not the optimal ones. Ultimately, we are not doing Data Science all day for fun, but for generating value for a department and a data scientist is also faced with special challenges at this level, at least a basic knowledge of the expertise of that department is a must have.

Read this article in German:
“Data Science Knowledge Stack – Was ein Data Scientist können muss“

Data Science Knowledge Stack

With the Data Science Knowledge Stack, I would like to provide a structured insight into the tasks and challenges a Data Scientist has to face. The layers of the stack also represent a bidirectional flow from top to bottom and from bottom to top, because Data Science as a discipline is also bidirectional: we try to answer questions with data, or we look at the potentials in the data to answer previously unsolicited questions.

The DataScience Knowledge Stack consists of six layers:

Database Technology Knowledge

A Data Scientist works with data which is rarely directly structured in a CSV file, but usually in one or more databases that are subject to their own rules. In particular, business data, for example from the ERP or CRM system, are available in relational databases, often from Microsoft, Oracle, SAP or an open source alternative. A good Data Scientist is not only familiar with Structured Query Language (SQL), but is also aware of the importance of relational linked data models, so he also knows the principle of data table normalization.

Other types of databases, so-called NoSQL databases (Not only SQL) are based on file formats, column or graph orientation, such as MongoDB, Cassandra or GraphDB. Some of these databases use their own programming languages ​​(for example JavaScript at MongoDB or the graph-oriented database Neo4J has its own language called Cypher). Some of these databases provide alternative access via SQL (such as Hive for Hadoop).

A data scientist has to cope with different database systems and has to master at least SQL – the quasi-standard for data processing.

Data Access & Transformation Knowledge

If data are given in a database, Data Scientists can perform simple (and not so simple) analyzes directly on the database. But how do we get the data into our special analysis tools? To do this, a Data Scientist must know how to export data from the database. For one-time actions, an export can be a CSV file, but which separators and text qualifiers should be used? Possibly, the export is too large, so the file must be split.
If there is a direct and synchronous data connection between the analysis tool and the database, interfaces like REST, ODBC or JDBC come into play. Sometimes a socket connection must also be established and the principle of a client-server architecture should be known. Synchronous and asynchronous encryption methods should also be familiar to a Data Scientist, as confidential data are often used, and a minimum level of security is most important for business applications.

Many datasets are not structured in a database but are so-called unstructured or semi-structured data from documents or from Internet sources. And again we have interfaces, a frequent entry point for Data Scientists is, for example, the Twitter API. Sometimes we want to stream data in near real-time, let it be machine data or social media messages. This can be quite demanding, so the data streaming is almost a discipline with which a Data Scientist can come into contact quickly.

Programming Language Knowledge

Programming languages ​​are tools for Data Scientists to process data and automate processing. Data Scientists are usually no real software developers and they do not have to worry about software security or economy. However, a certain basic knowledge about software architectures often helps because some Data Science programs can be going to be integrated into an IT landscape of the company. The understanding of object-oriented programming and the good knowledge of the syntax of the selected programming languages ​​are essential, especially since not every programming language is the most useful for all projects.

At the level of the programming language, there is already a lot of snares in the programming language that are based on the programming language itself, as each has its own faults and details determine whether an analysis is done correctly or incorrectly: for example, whether data objects are copied or linked as reference, or how NULL/NaN values ​​are treated.

Data Science Tool & Library Knowledge

Once a data scientist has loaded the data into his favorite tool, for example, one of IBM, SAS or an open source alternative such as Octave, the core work just began. However, these tools are not self-explanatory and therefore there is a wide range of certification options for various Data Science tools. Many (if not most) Data Scientists work mostly directly with a programming language, but this alone is not enough to effectively perform statistical data analysis or machine learning: We use Data Science libraries (packages) that provide data structures and methods as a groundwork and thus extend the programming language to a real Data Science toolset. Such a library, for example Scikit-Learn for Python, is a collection of methods implemented in the programming language. The use of such libraries, however, is intended to be learned and therefore requires familiarization and practical experience for reliable application.

When it comes to Big Data Analytics, the analysis of particularly large data, we enter the field of Distributed Computing. Tools (frameworks) such as Apache Hadoop, Apache Spark or Apache Flink allows us to process and analyze data in parallel on multiple servers. These tools also provide their own libraries for machine learning, such as Mahout, MLlib and FlinkML.

Data Science Method Knowledge

A Data Scientist is not simply an operator of tools, he uses the tools to apply his analysis methods to data he has selected for to reach the project targets. These analysis methods are, for example, descriptive statistics, estimation methods or hypothesis tests. Somewhat more mathematical are methods of machine learning for data mining, such as clustering or dimensional reduction, or more toward automated decision making through classification or regression.

Machine learning methods generally do not work immediately, they have to be improved using optimization methods like the gradient method. A Data Scientist must be able to detect under- and overfitting, and he must prove that the prediction results for the planned deployment are accurate enough.

Special applications require special knowledge, which applies, for example, to the fields of image recognition (Visual Computing) or the processing of human language (Natural Language Processiong). At this point, we open the door to deep learning.

Expertise

Data Science is not an end in itself, but a discipline that would like to answer questions from other expertise fields with data. For this reason, Data Science is very diverse. Business economists need data scientists to analyze financial transactions, for example, to identify fraud scenarios or to better understand customer needs, or to optimize supply chains. Natural scientists such as geologists, biologists or experimental physicists also use Data Science to make their observations with the aim of gaining knowledge. Engineers want to better understand the situation and relationships between machinery or vehicles, and medical professionals are interested in better diagnostics and medication for their patients.

In order to support a specific department with his / her knowledge of data, tools and analysis methods, every data scientist needs a minimum of the appropriate skills. Anyone who wants to make analyzes for buyers, engineers, natural scientists, physicians, lawyers or other interested parties must also be able to understand the people’s profession.

Engere Data Science Definition

While the Data Science pioneers have long established and highly specialized teams, smaller companies are still looking for the Data Science Allrounder, which can take over the full range of tasks from the access to the database to the implementation of the analytical application. However, companies with specialized data experts have long since distinguished Data Scientists, Data Engineers and Business Analysts. Therefore, the definition of Data Science and the delineation of the abilities that a data scientist should have, varies between a broader and a more narrow demarcation.


A closer look at the more narrow definition shows, that a Data Engineer takes over the data allocation, the Data Scientist loads it into his tools and runs the data analysis together with the colleagues from the department. According to this, a Data Scientist would need no knowledge of databases or APIs, neither an expertise would be necessary …

In my experience, DataScience is not that narrow, the task spectrum covers more than just the core area. This misunderstanding comes from Data Science courses and – for me – I should point to the overall picture of Data Science again and again. In courses and seminars, which want to teach Data Science as a discipline, the focus will of course be on the core area: programming, tools and methods from mathematics & statistics.

Marta Fajlhauer

Is Data Science the new Statistics?

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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
Benjamin Aunkofer

What makes a good Data Scientist? Answered by leading Data Officers!

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What makes a good Data Scientist? A question I got asked recently a lot by data science newbies as well as long-established CIOs and my answer ist probably not what you think:
In my opinion is a good Data Scientist somebody with, at least, a good knowledge of computer programming, statistics and the ability of understanding the customer´s business. Above all stands a strong interest in finding value in distributed data sources.

Debatable? Maybe. That’s why I forwarded this question to five other leading Data Scientists and Chief Data Officers in Germany, let’s have a look on their answers to this question and create your own idea of what a good Data Scientist might be:

Dr. Andreas Braun – Head of Global Data & Analytics @ Allianz SE

A data scientist connects thorough analytical and methodological understanding  with a technical hands-on/ engineering mentality.
Data scientists bridge between analytics, tech, and business. “New methods”, such as machine learning, AI, deep learning etc. are crucial and are continuously challenged and improved. (14 February 2017)

Dr. Helmut Linde – Head of Data Science @ SAP SE

The ideal data scientist is a thought leader who creates value from analytics, starting from a vision for improved business processes and an algorithmic concept, down to the technical realization in productive software. (09 February 2017)

Klaas Bollhoefer – Chief Data Scientist @ The unbelievable Machine Company

For me a data scientist thinks ahead, thinks about and thinks in-between. He/she is a motivated, open-minded, enthusiastic and unconventional problem solver and tinkerer. Being a team player and a lone wolf are two sides of the same coin and he/she definitely hates unicorns and nerd shirts. (27 March 2017)

 

Wolfgang Hauner – Chief Data Officer @ Munich Re

A data scientist is, from their very nature, interested in data and its underlying relationship and has the cognitive, methodical and technical skills to find these relationships, even in unstructured data. The essential prerequisites to achieve this are curiosity, a logical mind-set and a passion for learning, as well as an affinity for team interaction in the work place. (08 February 2017)

 

Dr. Florian Neukart – Principal Data Scientist @ Volkswagen Group of America

In my opinion, the most important trait seems to be driven by an irresistible urge to understand fundamental relations and things, whereby I summarize both an atom and a complex machine among “things”. People with this trait are usually persistent, can solve a new problem even with little practical experience, and strive for the necessary training or appropriate quantitative knowledge autodidactically. (08 February 2017)

Background idea:
That I am writing about atoms and complex machines has to do with the fact that I have been able to analyze the most varied data through my second job at the university, and that I am given a chance to making significant contributions to both machine learning and physics, is primarily rooted in curiosity. Mathematics, physics, neuroscience, computer science, etc. are the fundamentals that someone will acquire if she wants to understand. In the beginning, there is only curiosity… I hope this is not too out of the way, but I’ve done a lot of job interviews and worked with lots of smart people, and it has turned out that quantitative knowledge alone is not enough. If someone is not burning for understanding, she may be able to program a Convolutional Network from the ground but will not come up with new ideas.