Zertifikatsstudium „Data Science and Big Data“ 2021 an der TU Dortmund

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Komplexe Daten aufbereiten und analysieren, um daraus zukünftige Entwicklungen abzulesen: das lernen Sie im berufsbegleitenden Zertifikatsstudium „Data Science and Big Data“ an der TU Dortmund.

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Data Science & Big Data 2021

Die Zielgruppe sind Fachkräfte, die sich in ihrer Berufspraxis mit Fragestellungen zum Thema Datenanalyse und Big Data befassen, jedoch nun tiefergehende Kenntnisse in dem Themenfeld erhalten möchten. Von der Analyse über das Management bis zur zielgerichteten Darstellung der Ergebnisse lernen die Teilnehmenden dabei Methoden der Disziplinen Statistik, Informatik und Journalistik kennen.

Renommierte Wissenschaftlerinnen und Wissenschaftler vermitteln den Teilnehmerinnen und Teilnehmern die neuesten datenwissenschaftlichen Erkenntnisse und zeigen, wie dieses Wissen praxisnah im eigenen Big-Data Projekt umgesetzt werden kann.

Die nächste Studiengruppe startet im Februar 2021, der Bewerbungsschluss ist am 2. November 2020. Die Anzahl der verfügbaren Plätze ist begrenzt, eine rechtzeitige Bewerbung lohnt sich daher.

Nähere Informationen finden Sie unter: http://www.zhb.tu-dortmund.de/datascience

Einführung und Vertiefung in R Statistics mit den Dortmunder R-Kursen!

Im Rahmen der Dortmunder R Kurse bieten wir unsere Expertise in Schulungen für die Programmiersprache R an. Zielgruppe unserer Fortbildungen sind nicht nur Statistiker, sondern auch Anwender jeder Fachrichtung aus Industrie und Forschungseinrichtungen, die mit R ihre Daten analysieren wollen. Die Dortmunder R-Kurse werden ausschließlich von Statistikern mit langjähriger Erfahrung angeboten. Die Referenten gehören zum engsten Kreis der internationalen R-Gemeinschaft. Die angebotenen Kurse haben sich vielfach national und international bewährt.

Unsere Termine für die Online-Durchführung in diesem Jahr:

8., 9. und 10. Juni: R-Basiskurs (jeweils 9:00 – 14:00 Uhr)

22., 23., 24. und 25. Juni: R-Vertiefungskurs (jeweils 9:00 – 13:00 Uhr)

Kosten jeweils 750.00€, bei Buchung beider Kurse im Juni erhalten Sie einen Preisnachlass von 200€.

Zur Anmeldung gelangen Sie über den nachfolgenden Link:
https://www.zhb.tu-dortmund.de/zhb/wb/de/home/Seminare/Andere_Veranst/index.html

R Basiskurs

Das Seminar R Basiskurs für Anfänger findet am 8., 9. und 10. Juni 2020 statt. Den Teilnehmern wird der praxisrelevante Part der Programmiersprache näher gebracht, um so die Grundlagen zur ersten Datenanalyse — von Datensatz zu statistischen Kennzahlen und ersten Visualisierungen — zu schaffen. Anmeldeschluss ist der 25. Mai 2020.

Programm:

  • Installation von R und zugehöriger Entwicklungsumgebung
  • Grundlagen von R: Syntax, Datentypen, Operatoren, Funktionen, Indizierung
  • R-Hilfe effektiv nutzen
  • Ein- und Ausgabe von Daten
  • Behandlung fehlender Werte
  • Statistische Kennzahlen
  • Visualisierung

R Vertiefungskurs

Das Seminar R-Vertiefungskurs für Fortgeschrittene findet am 22., 23., 24. und 25. Juni (jeweils von 9:00 – 13:00 Uhr) statt. Die Veranstaltung ist ideal für Teilnehmende mit ersten Vorkenntnissen, die ihre Analysen effizient mit R durchführen möchten. Anmeldeschluss ist der 11. Juni 2020.

Der Vertiefungskurs baut inhaltlich auf dem Basiskurs auf. Es besteht aber keine Verpflichtung, bei Besuch des Vertiefungskurses zuvor den Basiskurs zu absolvieren, wenn bereits entsprechende Vorkenntnisse in R vorhanden sind.

Programm:

  • Eigene Funktionen, Schleifen vermeiden durch *apply
  • Einführung in ggplot2 und dplyr
  • Statistische Tests und Lineare Regression
  • Dynamische Berichterstellung
  • Angewandte Datenanalyse anhand von Fallbeispielen

Links zur Veranstaltung direkt:

R-Basiskurs: https://dortmunder-r-kurse.de/kurse/r-basiskurs/

R-Vertiefungskurs: https://dortmunder-r-kurse.de/kurse/r-vertiefungskurs/

Image Source: Pixabay (https://pixabay.com/photos/classroom-school-education-learning-2093744/)

The Data Surrounding Higher Education and COVID-19

Just a few short weeks ago, it would have seemed impossible for some microscopic pathogen to upend our lives as we knew it, but the novel Coronavirus has proven us breathtakingly wrong.

It has suddenly and unexpectedly changed everything we had thought was most stable and predictable in our lives, from the ways that we work to the ways we interact with one another. It’s even changed the way we learn, as colleges and universities across the nation shutter their doors.

But what is the real impact of COVID-19 on higher education? How are college students really faring in the face of the pandemic, and what can we do to support them now and in the post-pandemic life to come?

The Scramble is On

Probably the most significant challenge that schools, educators, and students alike are facing is that no one really saw this coming, so now we’re trying to figure out how to protect students’ education while also protecting their physical health. We’re having to make decisions that impact millions of students and faculty and do that with no preparation whatsoever.

To make matters worse, faculties are having to convert their classes to a forum the majority have never even used before. Before the lockdown, more than 70% of faculty in higher education had zero experience with online teaching. Now they’re being asked to convert their entire semester’s course schedule from an in-class to an online format, and they’re having to do it in a matter of weeks if not days.

For students who’ve never taken a distance learning course before, these impromptu, online, cobbled-together courses are hardly the recipe for academic success. The challenge is even greater for lab-based courses, where content mastery depends on hands-on work and laboratory applications. To solve this problem, some of the newly-minted distance ed instructors are turning to online lab simulations to help students make do until the real thing is open to them again.

Making Do

It’s not just the schools and the faculty that have been caught off guard by the sudden need to learn while under lockdown. Students are also having to hustle to make sure they have the technology they need to move their college experience online. Unfortunately, for many students, that’s not always easy, and for some, it’s downright impossible.

Studies show that large swaths of the student population: first-generation college students, community college students, immigrants, and lower-income students, typically rely on on-campus facilities to access the technology they need to do their work. When physical campuses close and the community libraries and hotspots with them, so too does the chance for many students to take their learning online.

Students in urban environments face particular risks. Even if they are able to access the technology they need to engage in distance learning, they may find it impossible to socially isolate. The need to access a hotspot or wi-fi connection might put them in unsafe proximity to other students, not to mention the millions of workers now forced to telecommute.

The Good News

America’s millions of new online learners and teachers may have a tough row to hoe, but the news isn’t all bad. Online education is by no means a new thing. By 2017, nearly 7 million students were enrolled in at least one distance education course according to a recent survey by the National Center for Education Statistics.

It isn’t as though the technology to provide a secure, user-friendly learning experience doesn’t exist. The financial industry, for example, has played a leading role in developing private, responsive, and highly-customizable technology solutions to meet practically any need a client or stakeholder may have.

The solutions used for the financial sector can be built on and modified for the online learning experience to ensure the privacy of students, educators, and institutions while providing real-time access to learning tools and content to classmates and teachers.

A New Path?

As challenging as it may be, transitioning to online learning not only offers opportunities for the present, but it may well open up new paths for the future. While our world may finally be approaching the downward slope of the curve and while we may be seeing the light at the end of the tunnel, until there’s a vaccine, we haven’t likely seen the last of COVID-19.

And even when we lay the COVID beast to rest, infectious disease, unfortunately, is a fact of human life. For students just starting to think about their career paths, this lockdown may well be the push they need to find a career that’s well-suited to this “new normal.”

For instance, careers in data science transition perfectly from onsite to at-home work, and as epidemiological superheroes like Dr. Fauci and Dr. Birx have shown, they are often involved in important, life-saving work. These are also careers that can be pursued largely, if not exclusively, online. Whether you’re a complete newbie or a veteran to the field, there is a large range of degree and certification programs available online to launch or advance your data science career.

It might be that your college-with-corona experience is pointing your life in a different direction, toward education rather than data science. With a doctorate in education, your future career path is virtually unlimited. You might find yourself teaching, researching, leading universities or developing education policy.

What matters most is that with an EdD, you can make a difference in the lives of students and teachers, just as your teachers and administrators are making a difference in your life. You can be the guiding and comforting force for students in a time of crisis and you can use your experiences today to pay it forward tomorrow.

Optimize AI Talent: Perception from Across the Globe

Despite the AI hype, the AI skill gap is turning into some pariah while businesses are accelerating to become demigods.

Reports from the “Global Talent Competitiveness Index (GTCI) 2020” cover multiple parameters both national and organizational to generate insight for further action. This report compiles 70 variables including 132 national economies across the globe – based on all groups of income and at every developmental level.

The sole purpose of the GTCI report is to narrow down the skill gap by delivering the right data inputs. The figures mentioned in the report could be of value to private and public organizations.

GTCI report covered multiple themes that need to be addressed: –

As the race to embrace AI spurs, it is evident to address the challenges faced due to AI and how best these problems can be solved.

The pace at which AI is developing is transforming the way we work, forcing a technology shift, change in the corporate structure, changing the innovation system for AI professionals in every possible way.

There’s more that is needed to be done as AI and automation continue to affect the way we work.

  • Reskilling in workplaces to eliminate dearth of talent

As the role in AI keeps evolving, organizations need a larger workforce, especially to play technology roles such as AI engineers and AI specialists. Looking closely at the statistics you may not fail to notice that the number of AI job roles is on the rise, but there’s scarce talent.

Employers must take on reskilling as a critical measure. Else how will the technology market keep up with changing trends? Reskilling in the form of training or AI certifications should be emphasized. Having an in-house AI talent is an added advantage to the company.

  • Skill gap between growing countries (low performing and high performing) are widening

Based on the GTCI report, it is seen there is a skill gap happening not only across industries but between nations. The report also highlights which country lacks basic digital skills, and this highly gets contributed toward a digital divide between nations.

  • High-level of cooperation needed to embrace AI benefits

As much as the world shows concern toward embracing AI, not much has been done to achieve these transformations. And AI has huge potential to transform society and make it a better place to live. However, to embrace these benefits, corporations must engage in AI regulation.

From a talent acquisition perspective, this simply means employers will need more training and reskilling opportunities.

  • AI to allow nations to skip generations

On a technological front, AI makes it possible to skip generations in developed nations. Although, not common due to structural obstruction.

  • Cities are now competing to become talent magnets and AI hubs

As AI continues to hit the market, organizations are aggressively coming up with newer policies to attract and retain AI professionals.

No doubt, cities are striving to attract the right kind of talent as competition keeps increasing. As such many cities are competing in becoming core AI engines in transforming energy grids, transportation, and many other multiple segments. Cities are now becoming the main test beds for AI-based tools i.e. self-driven vehicles, tele-surveillance, and facial recognition.

  • Sustainable AI comes when the society is equally up for it

With certain communities not adopting and accepting the advent of AI, it is difficult to say whether these communities will not try to distort AI narratives. As a result, it is crucial for multiple stakeholders to embrace AI and developed the AI workforce in parallel.

Not to forget, regulators and policy-makers have an equal role to play to ensure there’s a smooth transition in jobs. As AI-induced transformation skyrockets, educators and leaders need to move quickly as the new generations’ complete focus is entirely based on doing their bit to the society.

Two decades passed ever since McKinsey declared the war for talent – particularly for high-performing employees. As organizations are extensively looking to hire the right talent, it is imperative to retain and attract talent at large.

Despite the unprecedented growth in AI technologies, it is near to being unanimous regarding having hold of organizations to master in AI, forget about retaining talent. They’re not even getting better at it.

Even top tech companies such as Google and Amazon, the demand for top talent outstrips the supply. Although you may find thousands of candidates applying for the same job role, the competition just gets tougher since such employers are tough nuts and pleasing them is not an easy task.

If these tech giants are finding it difficult to hire the right talent, you could imagine the plight of other companies.

Given the optimistic view regarding the technology future, it is much more challenging to convince that the war for talent truly resembles the war on talent.

The good news is organizations that look forward to adopting new technology and reskill their employees will most likely thrive in the competitive edge.

Top 7 MBA Programs to Target for Business Analytics 

Business Analytics refers to the science of collecting, analysing, sorting, processing and compiling various available data pertaining to different areas and facets of business. It also includes studying and scrutinising the information for useful and deep insights into the functioning of a business which can be used smartly for making important business-related decisions and changes to the existing system of operations. This is especially helpful in identifying all loopholes and correcting them.

The job of a business analyst is spread across every domain and industry. It is one of the highest paying jobs in the present world due to the sheer shortage of people with great analytical minds and abilities. According to a report published by Ernst & Young in 2019, there is a 50% rise in how firms and enterprises use analytics to drive decision making at a broad level. Another reason behind the high demand is the fact that nowadays a huge amount of data is generated by all companies, large or small and it usually requires a big team of analysts to reach any successful conclusion. Also, the nature and high importance of the role compels every organisation and firm to look for highly qualified and educated professionals whose prestigious degrees usually speak for them.

An MBA in Business Analytics, which happens to be a branch of Business Intelligence, also prepares one for a successful career as a management, data or market research analyst among many others. Below, we list the top 7 graduate school programs in Business Analytics in the world that would make any candidate ideal for this high paying job.

1 New York University – Stern School of Business

Location: New York City, United States

Tuition Fees: $74,184 per year

Duration:  2 years (full time)

With a graduate acceptance rate of 23%, the NYU Stern School makes it to this list due to the diversity of the course structure that it offers in its MBA program in Business Analytics. One can specialise and learn the science behind econometrics, data mining, forecasting, risk management and trading strategies by being a part of this program. The School prepares its students and offers employability in fields of investment banking, marketing, consulting, public finance and strategic planning. Along with opportunities to study abroad for small durations, the school also offers its students ample chances to network with industry leaders by means of summer internships and career workshops. It is a STEM designated two-year, full time degree program.

2 University of Pennsylvania – Wharton School Business 

Location: Philadelphia, United States

Tuition fees: $81,378 per year

Duration: 20 months (full time, including internship)

The only Ivy-League school in the list with one of the best Business Analytics MBA programs in the world, Wharton has an acceptance rate of 19% only. The tough competition here is also characterised by the high range of GMAT scores that most successful applicants have – it lies between 540 and 790, averaging at a very high threshold of 732. Most of Wharton’s graduating class finds employment in a wide range of sectors including consulting, financial services, technology, real estate and health care among many others. The long list of Wharton’s alumni includes some of the biggest business entities in the world, them being – Warren Buffet, Elon Musk, Sundar Pichai, Ronald Perelman and John Scully.

The best part about Wharton’s program structure is its focus on building leadership and a strong sense of teamwork in every student.

3 Carnegie Mellon University – Tepper School of Business

Location: Pittsburgh, United States

Tuition Fees: $67,575

Duration: 18 months (online)

The Tepper School of Business in Carnegie Mellon University is the only graduate school in the list that offers an online Master of Science program in Business Analytics. The primary objectives of the program is to equip students with creative problem solving expertise and deep analytic skills. The highlights of the program include machine learning, programming in Python and R, corporate communication and the knowledge of various business domains like marketing, finance, accounting and operations.

The various sub courses offered within the program include statistics, data management, data analytics in finance, data exploration and optimization for prescriptive analytics. There are several special topics offered too, like Ethics in Artificial Intelligence and People Analytics among many others.

4 Massachusetts Institute of Technology – Sloan School of Management

Location: Cambridge, United States

Tuition Fees: $136,480

Duration: 12 months

The Master of Business Analytics program at MIT Sloan is a relatively new program but has made it to this list due to MIT’s promise and commitment of academic and all-rounder excellence. The program is offered in association with MIT’s Operations Research Centre and is customised for students who wish to pursue a career in the industry of data sciences. The program is easily comprehensible for students from any educational background. It is a STEM designated program and the curriculum includes several modules like machine learning, usage of analytics software tools like Python, R, SQL and Julia. It also includes courses on ethics, data privacy and a capstone project.

5 University of Chicago – Graham School

Location: Chicago, United States

Tuition Fees: $4,640 per course

Duration: 12 months (full time) or 4 years (part time)

The Graham School in the University of Chicago is mainly interested in candidates who show love and passion for analytics. An incoming class at Graham usually consists of graduates in science or social science, professionals in an early career who wish to climb higher in the job ladder and mid-career professionals who wish to better their analytical skills and enhance their decision-making prowess.

The curriculum at Graham includes introduction to statistics, basic levels of programming in analytics, linear and matrix algebra, machine learning, time series analysis and a compulsory core course in leadership skills. The acceptance rate of the program is relatively higher than the previous listed universities at 34%.

6 University of Warwick – Warwick Business School

Location: Coventry, United Kingdom

Tuition Fees: $34,500

Duration: 12 months (full time)

The only school to make it to this list from the United Kingdom and the only one outside of the United States, the Warwick Business School is ranked 7th in the world by the QS World Rankings for their Master of Science degree in Business Analytics. The course aims to build strong and impeccable quantitative consultancy skills in its candidates. One can also look forward to improving their business acumen, communication skills and commercial research experience after graduating out of this program.

The school has links with big corporates like British Airways, IBM, Proctor and Gamble, Tesco, Virgin Media and Capgemini among others where it offers employment for its students.

7 Columbia University – School of Professional Studies

Location: New York City, United States 

Tuition Fees: $2,182 per point

Duration: 1.5 years full time (three terms)

The Master of Sciences program in Applied Analytics at Columbia University is aimed for all decision makers and also favours candidates with strong critical thinking and logical reasoning abilities. The curriculum is not very heavy on pure stats and data sciences but it allows students to learn from extremely practical and real-life experiences and examples. The program is a blend of several online and on-campus classes with several week-long courses also. A large number of industry experts and guest lectures take regular classes, conduct workshops and seminars for exposing the students to the real-world scenario of Business Analytics. This also gives the students a solid platform to network and broaden their perspective.

Several interesting courses within the paradigm of the program includes storytelling with data, research design, data management and a capstone project.

The admission to every school listed above is extremely competitive and with very limited intake. However, as it is rightly said, hard work is the key to success, one can rest guaranteed that their career will never be the same if they make it into any of these programs.

Wie der C++-Programmierer bei der Analyse großer Datenmengen helfen kann

Die Programmiersprache C wurde von Dennis Ritchie in den Bell Labs in einer Zeit (1969-1973) entwickelt, als jeder CPU-Zyklus und jeder Byte Speicher sehr teuer war. Aus diesem Grund wurde C (und später C++) so konzipiert, dass die maximale Leistung der Hardware mit der Sprachkomplexität erzielt werden konnte. Derzeit ist der C++ Programmierer besonders begehrt auf dem Arbeitsmarkt, für ganz bestimmte Abläufe, die wir später genauer beschreiben werden.

Warum sollten Sie einen C++ Entwickler mieten, wenn es um große Daten geht?

C++ ermöglicht, als Sprache auf einem niedrigen Level, eine Feinabstimmung der Leistung der Anwendung in einer Weise, die bei der Verwendung von Sprachen auf einem hohen Level nicht möglich ist. Warum sollten Sie einen C++ Entwickler mieten? C++ bietet den Entwicklern eine viel bessere Kontrolle über den Systemspeicher und die Ressourcen, als die der C Programmierer oder Anderer.

C++ ist die einzige Sprache, in der man Daten mit mehr als 1 GB pro Sekunde knacken, die prädiktive Analyse in Echtzeit neu trainieren und anwenden und vierstellige QPS einer REST-ful API in der Produktion bedienen kann, während die [eventuelle] Konsistenz des Aufzeichnungssystems ständig erhalten bleibt. Auf einem einzigen Server, natürlich aus Gründen der Zuverlässigkeit dupliziert, aber das, ohne in Repliken, Sharding und das Auffüllen und Wiederholen von persistenten Nachrichtenwarteschlangen investieren zu. Für ein groß angelegtes Werbesystem, dynamischen Lastausgleich oder eine hocheffiziente adaptive Caching-Schicht ist C++ die klügste Wahl.

Die allgemeine Vorstellung ist, dass R und Python schneller sind, aber das ist weit von der Wahrheit entfernt. Ein gut optimierter C++-Code könnte hundertmal schneller laufen, als das gleiche Stück Code, das in Python oder R geschrieben wurde. Die einzige Herausforderung bei C++ ist die Menge an Arbeit, die Sie bewältigen müssen, um die fertigen Funktionen zum Laufen zu bringen. Sie müssen wissen, wie man Zeiger verteilt und verwaltet – was ehrlich gesagt ein wenig kompliziert sein kann. Die C# Programmierer Ausbildung ist aus diesem Grunde z.Z. sehr begehrt.

R und Python

Akademiker und Statistiker haben R über zwei Jahrzehnte entwickelt. R verfügt nun über eines der reichsten Ökosysteme, um Datenanalysen durchzuführen. Es sind etwa 12000 Pakete in CRAN (Open-Source-Repository) verfügbar. Es ist möglich, eine Bibliothek zu finden, für was auch immer für eine Analyse Sie durchführen möchten. Die reiche Vielfalt der Bibliothek macht R zur ersten Wahl für statistische Analysen, insbesondere für spezialisierte analytische Arbeiten.

Python kann so ziemlich die gleichen Aufgaben wie R erledigen: Data Wrangling, Engineering, Feature Selection Web Scrapping, App und so weiter. Python ist ein Werkzeug, um maschinelles Lernen in großem Maßstab einzusetzen und zu implementieren. Python-Codes sind einfacher zu warten und robuster als R. Vor Jahren hatte Python nicht viele Bibliotheken für Datenanalyse und maschinelles Lernen. In letzter Zeit holt Python auf und bietet eine hochmoderne API für maschinelles Lernen oder künstliche Intelligenz. Der größte Teil der datenwissenschaftlichen Arbeit kann mit fünf Python-Bibliotheken erledigt werden: Numpy, Pandas, Scipy, Scikit-Learning und Seaborn.

Aber das Wissen, mit Zeigern zu arbeiten oder den Code in C++ zu verwalten, ist mit einem hohen Preis verbunden. Aus diesem Grunde werden C++ Programmierer gesucht, für die Bewältigung von großen Datenpaketen. Ein tiefer Einblick in das Innenleben der Anwendung ermöglicht es ihnen, die Anwendung im Falle von Fehlern besser zu debuggen und sogar Funktionen zu erstellen, die eine Kontrolle des Systems auf Mikroebene erfordern. Schauen Sie sich doch nach C# Entwickler in Berlin um, denn sie haben einen besonders guten Ruf unter den neuen Entwicklern.

Das Erlernen der Programmierung ist eine wesentliche Fähigkeit im Arsenal der Analysten von Big Data. Analysten müssen kodieren, um numerische und statistische Analysen mit großen Datensätzen durchzuführen. Einige der Sprachen, in deren Erlernen auch die C Entwickler Zeit und Geld investieren sollten, sind unter anderem Python, R, Java und C++. Je mehr sie wissen, desto besser – Programmierer sollten immer daran denken, dass sie nicht nur eine einzelne Sprache lernen sollten. C für Java Programmierer sollte ein MUSS sein.

Wo wird das C++ Programmieren eingesetzt?

Die Programmiersprache C++ ist eine etablierte Sprache mit einem großen Satz von Bibliotheken und Tools, die bereit ist, große Datenanwendungen und verteilte Systeme zu betreiben. In den meisten Fällen wird C++ zum Schreiben von Frameworks und Paketen für große Daten verwendet. Diese Programmiersprache bietet auch eine Reihe von Bibliotheken, die beim Schreiben von Algorithmen für das tiefe Lernen helfen. Mit ausreichenden C++-Kenntnissen ist es möglich, praktisch unbegrenzte Funktionen auszuführen. Dennoch ist C++ nicht die Sprache, die man leicht erlernen kann, da man die über 1000 Seiten Spezifikation und fast 100 Schlüsselwörter beherrschen muss.

Die Verwendung von C++ ermöglicht die prozedurale Programmierung für intensive Funktionen der CPU und die Kontrolle über die Hardware, und diese Sprache ist sehr schnell, weshalb sie bei der Entwicklung verschiedener Spiele oder in Spielmaschinen weit verbreitet ist.

C++ bietet viele Funktionen, die anderen Sprachen fehlen. Darüber hinaus bietet die Sprache auch Zugang zu umfangreichen Vorlagen, die es Ihnen ermöglichen, generische Codes zu schreiben. Als betroffenes Unternehmen sollten Sie sich deshalb tatsächlich überlegen, einen C++ Programmierer zu suchen oder in einen Kurs von C++ für Ihren C Programmierer zu investieren. Am Ende lohnen sich bestimmt diese Kosten.

Und vergessen Sie nicht: C++ ist die einzige Sprache, die in der Lage ist, 1 GB+ Daten in weniger als einer Sekunde zu verarbeiten. Darüber hinaus können Sie Ihr Modell neu trainieren und prädiktive Analysen in Echtzeit und sogar die Konsistenz der Systemaufzeichnung anwenden. Diese Gründe machen C++ zu einer bevorzugten Wahl für Sie, wenn Sie einen Datenwissenschaftler für Ihr Unternehmen suchen.

Beispiele für die Verwendung von C++

Die Verwendung von C++ zur Entwicklung von Anwendungen und vielen produktbasierten Programmen, die in dieser Sprache entwickelt wurden, hat mehrere Vorteile, die nur auf ihren Eigenschaften und ihrer Sicherheit beruhen. Unten finden Sie eine Liste der häufigsten Anwendungen von C++.

  • Google-Anwendungen – Einige der Google-Anwendungen sind auch in C++ geschrieben, darunter das Google-Dateisystem und der Google-Chromium-Browser sowie MapReduce für die Verarbeitung großer Clusterdaten. Die Open-Source-Gemeinschaft von Google hat über 2000 Projekte, von denen viele in den Programmiersprachen C oder C++ geschrieben und bei GitHub frei verfügbar sind.
  • Mozilla Firefox und Thunderbird – Der Mozilla-Internetbrowser Firefox und der E-Mail-Client Thunderbird sind beide in der Programmiersprache C++ geschrieben, und sie sind ebenfalls Open-Source-Projekte. Der C++-Quellcode dieser Anwendungen ist in den MDN-Webdokumenten zu finden.
  • Adobe-Systeme – Die meisten der wichtigsten Anwendungen von Adobe-Systemen werden in der Programmiersprache C++ entwickelt. Zu diesen Anwendungen gehören Adobe Photoshop und Image Ready, Illustrator und Adobe Premier. Sie haben in der Vergangenheit eine Menge Open-Source-Codes veröffentlicht, immer in C++, und ihre Entwickler waren in der C++-Community aktiv.
  • 12D-Lösungen – 12D Solutions Pty Ltd ist ein australischer Softwareentwickler, der sich auf Anwendungen im Bereich Bauwesen und Vermessung spezialisiert hat. Computer Aided Design-System für Vermessung, Bauwesen und mehr. Zu den Kunden von 12D Solutions gehören Umweltberater, Berater für Bau- und Wasserbau, lokale, staatliche und nationale Regierungsabteilungen und -behörden, Vermessungsingenieure, Forschungsinstitute, Bauunternehmen und Bergbau-Berater.
  • In C/C++ geschriebene Betriebssysteme

Apple – Betriebssystem OS XApple – Betriebssystem OS X

Einige Teile von Apple OS X sind in der Programmiersprache C++ geschrieben. Auch einige Anwendungen für den iPod sind in C++ geschrieben.

Microsoft-BetriebssystemeMicrosoft-Betriebssysteme

Der Großteil der Software wird buchstäblich mit verschiedenen Varianten von Visual C++ oder einfach C++ entwickelt. Die meisten der großen Anwendungen wie Windows 95, 98, Me, 200 und XP sind ebenfalls in C++ geschrieben. Auch Microsoft Office, Internet Explorer und Visual Studio sind in Visual C++ geschrieben.

  • Betriebssystem Symbian – Auch Symbian OS wird mit C++ entwickelt. Dies war eines der am weitesten verbreiteten Betriebssysteme für Mobiltelefone.

Die Einstellung eines C- oder C++-Entwicklers kann eine gute Investition in Ihr Projekt-Upgrade sein

Normalerweise benötigen C- und C++-Anwendungen weniger Strom, Speicher und Platz als die Sprachen der virtuellen Maschinen auf hoher Ebene. Dies trägt dazu bei, den Kapitalaufwand, die Betriebskosten und sogar die Kosten für die Serverfarm zu reduzieren. Hier zeigt sich, dass C++ die Gesamtentwicklungskosten erheblich reduziert.

Trotz der Tatsache, dass wir eine Reihe von Tools und Frameworks nur für die Verwaltung großer Daten und die Arbeit an der Datenwissenschaft haben, ist es wichtig zu beachten, dass auf all diesen modernen Frameworks eine Schicht einer niedrigen Programmiersprache – wie C++ – aufgesetzt ist. Die Niedrigsprachen sind für die tatsächliche Ausführung des dem Framework zugeführten Hochsprachencodes verantwortlich. Es ist also ratsam in ein C-Entwickler-Gehalt zu investieren.

Der Grund dafür, dass C++ ein so unverzichtbares Werkzeug ist, liegt darin, dass es nicht nur einfach, sondern auch extrem leistungsfähig ist und zu den schnellsten Sprachen auf dem Markt gehört. Darüber hinaus verfügt ein gut geschriebenes Programm in C++ über ein komplexes Wissen und Verständnis der Architektur der Maschine, sowie der Speicherzugriffsmuster und kann schneller laufen als andere Programme. Es wird Ihrem Unternehmen Zeit- und Stromkosten sparen.

Zum Abschluss eine Grafik, die Sie als Unternehmer interessieren wird und die das Verhältnis von der Performance and der Sicherheit diverser Sprachen darstellt:

Aus diesen und weiteren Gründen neigen viele Unternehmensentwickler und Datenwissenschaftler mit massiven Anforderungen an Skalierbarkeit und Leistung zu dem guten alten C++. Viele Organisationen, die Python oder andere Hochsprachen für die Datenanalyse und Erkundungsaufgaben verwenden, verlassen sich auf C++, um Programme zu entwickeln, die diese Daten an die Kunden weiterleiten – in Echtzeit.

Data Science Certifications to Excel in Your Career: A Holistic Approach

Personal and professional growth for an individual depends on the investment one puts in continued education. Continued education is necessary for leadership positions and industries such as human resources, manufacturing, marketing, operations, information technology, etc. Staying updated with the relevant profession is essential to move up the career ladder, and in certain cases, it is essential to save the job. 

It showcases your knowledge, education, and relevant skills necessary to perform the job for the current and future employers. ‘Career growth’ is not defined by the higher salaries but the effort made to earn those ‘higher salaries.’ Higher salaries do not mean the appreciable yearly increment in a well-established firm but earning the competent salaries by staying with the trend. We will discuss the learning opportunities for the most in-demand data science professionals here. 

 

Data Science certifications for the Newbies

When you understand the basic principles of data science, it would help you use the tools productively. If you are looking to develop data analytic skills, then you can opt for certain free online courses. It helps you learn the basics of data science at your own pace and get acquainted with the field knowledge.  

Most of the data science certification program or courses mentioned below are available free online. Though, a few may charge for gaining the certification once you finish the course. Whatever the case may be, you get destined to gain knowledge in the field which would be a good kick start for your career. 

To mention a few, they are:

  • Coursera – Data Science Specialization
  • Edx – Data Science Essentials
  • Udacity – Introduction to Machine Learning
  • IBM – Data Science Fundamentals
  • Data Quest – Become a Data Scientist
  • Kdnuggets – Data Mining Course

Most of these courses are available free online and are self-paced. You can get the basic hold of the subject and afterward, you may go for premium courses to advance learning or earn certifications.  

 

Data Science Certifications for Professionals

To stay competitive in the industry, you should get certified from industry-renowned global certification bodies. Mention not to say, there is a lot of difference between courses and certifications. Though a course gives you the relevant subject knowledge or skill, a certification program is vendor-neutral and increases your employability factor. It equips you with the latest tools and techniques and assures your prospective recruiter that you are their shot to hire. 

To mention a few of the best data science certifications, they are:

  • SAS Academy for Data Science – SAS Certified Data Scientist
  • Data Science Council of America (DASCA) – Senior Data Scientist 
  • Google- Google Certified Professional Data Engineer
  • Dell EMC Education ServicesData Science Associate v2 (DCS-DS) certification and the Data Science Specialist (DCS-DS) certification

These certifications equip you with the latest tools and techniques and assure your prospective recruiter that you are their shot to hire. 

 

Industry-specific Certifications

Industry-specific certifications, as the name itself indicates, these are specific to the industries. These certifications provide you specific training with use cases in the industry you are interested in or working. It helps you solve industrial problems at a faster rate with deep insight.  

To mention a few:

  • Agriculture Industry- Certificate in Agricultural Data Science
  • Fintech industry- Certification course for financial professions
  • Business Analytics – Harvard Business School’s Certification Program 

The data collected by an education department is entirely different from the e-commerce industry. These certifications give you a clear-cut idea about data mining and deriving insights by using the right and specific tools as required.

 

Cross-functional Certifications

A data science job is an end-to-end job. Data insights are used to improve business productivity, marketing strategy, and business value. So, it is good to know other fields also like business analytics, marketing, manufacturing. Though these certifications do not directly deal with the subject, it structures your knowledge base in the industry. It gives a holistic approach to your work and widens your organizational value. 

To mention a few, they are:

  • Project Management Institute- Project Management Professional Certification
  • Springboard – Certified UX Designer
  • Business Analyst Professional Program – Institute of Business Analyst Training

These certifications give you complete knowledge of the system and help you derive data with a holistic approach and gain business benefits. 

 

Wrapping Up:

In addition to certifications, it is necessary to complete a few independent projects to showcase your skills. It increases practical knowledge and provides hand-on-experience in technology. Ultimately, the knowledge we impart for the organization that can increase value matters. 

So, rather than choosing certifications or learnings merely for job or salary purposes, it is recommended to choose for learning purposes. When you develop interest and dedication for the subject, it helps you go a long way in the career path. 

Be strategic in your learnings and increase the knowledge base.

 

 

Zertifikatsstudium „Data Science and Big Data“ 2020 an der TU Dortmund

Jetzt bewerben!

Komplexe Daten aufbereiten und analysieren, um daraus zukünftige Entwicklungen abzulesen: das lernen Sie im berufsbegleitenden Zertifikatsstudium „Data Science and Big Data“ an der TU Dortmund.

Die Zielgruppe sind Fachkräfte, die sich in ihrer Berufspraxis mit Fragestellungen zum Thema Datenanalyse und Big Data befassen, jedoch nun tiefergehende Kenntnisse in dem Themenfeld erhalten möchten. Von der Analyse über das Management bis zur zielgerichteten Darstellung der Ergebnisse lernen die Teilnehmenden dabei Methoden der Disziplinen Statistik, Informatik und Journalistik kennen.

Renommierte Wissenschaftlerinnen und Wissenschaftler vermitteln den Teilnehmerinnen und Teilnehmern die neuesten datenwissenschaftlichen Erkenntnisse und zeigen, wie dieses Wissen praxisnah im eigenen Big-Data Projekt umgesetzt werden kann.

Die nächste Studiengruppe startet im Februar 2020, der Bewerbungsschluss ist am 4. November 2019. Die Anzahl der verfügbaren Plätze ist begrenzt, eine rechtzeitige Bewerbung lohnt sich daher.

Nähere Informationen finden Sie unter: http://www.zhb.tu-dortmund.de/datascience

Dortmunder R-Kurse | Neue Termine im Herbst 2019

Erweitern Sie Ihre Fähigkeiten in der Anwendung der Open Source Statistiksoftware R: In der Tagesseminarreihe „Dortmunder R-Kurse“ an der Technischen Universität Dortmund geben erfahrene Wissenschaftler der Fakultät Statistik ihre Expertise an Sie weiter.

Sie erwerben dadurch Qualifikationen zur selbstständigen Analyse eigener Daten sowie Schlüsselkompetenzen im Umgang mit Big Data. Die Kurse richten sich an Anwenderinnen und Anwender jeder Fachrichtung aus Industrie und Forschungseinrichtungen, die ihre Daten mit R auswerten möchten.

Das Angebot umfasst Kurse für Einsteiger und Fortgeschrittene, wo Sie Ihre Kenntnisse in R erlernen und vertiefen können.

  • R Basiskurs
    Inhalte: Grundlagen zur ersten Datenanalyse
    Termine: 5. & 6. November 2019
  • R Vertiefungskurs
    Inhalt: Effiziente Analysen mit R
    Termine: 21. & 22. November 2019
  • Weitere Inhouse Themen auf Anfrage: Machine Learning in R, Shiny Apps mit R

Weitere Informationen zu den R-Kursen finden Sie unter:
http://dortmunder-r-kurse.de/

 

Accelerate your AI Skills Today: A Million Dollar Job!

The skyrocketing salaries ($1m per year) of AI engineers is not a hype. It is the fact of current corporate world, where you will witness a shift that is inevitable.

We’ve already set our feet at the edge of the technological revolution. A revolution that is at the verge of altering the way we live and work. As the fact suggests, humanity has fundamentally developed human production in three revolutions, and we’re now entering the fourth revolution. In its scope, the fourth revolution projects a transformation that is unlike anything we humans have ever experienced.

  • The first revolution had the world transformed from rural to urban
  • the emergence of mass production in the second revolution
  • third introduced the digital revolution
  • The fourth industrial revolution is anxious to integrate technologies into our lives.

And all thanks to artificial intelligence (AI). An advanced technology that surrounds us, from virtual assistants to software that translates to self-driving cars.

The rise of AI at an exponential rate has disrupted almost every industry. So much so that AI is being rated as one-million-dollar profession.

Did this grab your attention? It did?

Now, what if we were to tell you that the salary compensation for AI experts has grown dramatically. AI and machine learning are fields that have a mountain of demand in the tech industry today but has sparse supply.

AI field is growing at a quicker pace and salaries are skyrocketing! Read it for yourself to know what AI experts, AI researchers and any other AI talent are commanding today.

  • A top-class AI research laboratory, OpenAI says that techies in the AI field are projected to earn a salary compensation ranging between $300 to $500k for fresh graduates. However, expert professionals could earn anywhere up to $1m.
  • Whopping salary package of above 100 million yen that amounts to $1m is being offered to AI geniuses by a Japanese firm, Start Today. A firm that operates a fashion shopping website named Zozotown.

Does this leave you with a question – Is this a right opportunity for you to jump in the field and make hay while the sun is shining? 

And the answer to this question is – yes, it is the right opportunity for any developer seeking a role in the AI industry. It can be your chance to bridge the skill shortage in the AI field either by upskilling or reskilling yourself in the field of AI.

There are a wide varieties of roles available for an AI enthusiast like you. And certain areas are like AI Engineers and AI Researchers are high in demand, as there are not many professionals who have robust AI knowledge.

According to a job report, “The Future of Jobs 2018,” a prediction was made suggesting that machines and algorithms will create around 133 million new job roles by 2022.

AI and machine learning will dominate the tech world. The World Economic Forum says that several sectors have started embracing AI and machine learning to tackle challenges in certain fields such as advertising, supply chain, manufacturing, smart cities, drones, and cybersecurity.

Unraveling the AI realm

From chatbots to financial planners, AI is impacting the way businesses function on a day-today basis. AI makes the work simpler, as it provides variables, which makes the work more streamlined.

Alright! You know that

  • the demand for AI professionals is rising exponentially and that there is just a trickle of supply
  • the AI professionals are demanding skyrocketing salaries

However, beyond that how much more do you know about AI?

Considering the fact that our lives have already been touched by AI (think Alexa, and Siri), it is just a matter of time when AI will become an indispensable part of our lives.

As Gartner predicts that 2020 will be an important year for business growth in AI. Thus, it is possible to witness significant sparks for employment growth. Though AI predicts to diminish 1.8 million jobs, it is also said to replace it with 2.3 million jobs that will be created. As we look forward to stepping into 2020, AI-related job roles are set to make positive progress of achieving 2 million net-new employments by 2025.

With AI promising to score fat paychecks that would reach millions, AI experts are struggling to find new ways to pick up nouveau skills. However, one of the biggest impacts that affect the job market today is the scarcity of talent in this field.

The best way to stay relevant and employable in AI is probably by “reskilling,” and “upskilling.” And  AI certifications is considered ideal for those in the current workforce.

Looking to upskill yourself – here’s how you can become an AI engineer today.

Top three ways to enhance your artificial intelligence career:

  1. Acquire skills in Statistics and Machine Learning: If you’re getting into the field of machine learning, it is crucial that you have in-depth knowledge of statistics. Statistics is considered a prerequisite to the ML field. Both the fields are tightly related. Machine learning models are created to make accurate predictions while statistical models do the job of interpreting the relationship between variables. Many ML techniques heavily rely on the theory obtained through statistics. Thus, having extensive knowledge in statistics help initiate the first step towards an AI career.
  2. Online certification programs in AI skills: Opting for AI certifications will boost your credibility amongst potential employers. Certifications will also enhance your earning potential and increase your marketability. If you’re looking for a change and to be a part of something impactful; join the AI bandwagon. The IT industry is growing at breakneck speed; it is now that businesses are realizing how important it is to hire professionals with certain skillsets. Specifically, those who are certified in AI are becoming sought after in the job market.
  3. Hands-on experience: There’s a vast difference in theory and practical knowledge. One needs to familiarize themselves with the latest tools and technologies used by the industry. This is possible only if the individual is willing to work on projects and build things from scratch.

Despite all the promises, AI does prove to be a threat to job holders, if they don’t upskill or reskill themselves. The upcoming AI revolution will definitely disrupt the way we work, however, it will leave room for humans to perform more creative jobs in the future corporate world.

So a word of advice is to be prepared and stay future ready.