Experten-Training: Angewandte Künstliche Intelligenz

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Im Rahmen dieses praxisorientierten Kurses wird anhand eines konkreten Beispiels ein gesamter Prozess zur Mustererkennung nachvollzogen und selbst programmiert. Dabei werden die möglichen Methoden beleuchtet und angewandt.

Aufbaukurs: Angewandte Künstliche Intelligenz

Am 2.11. – 3.11.2022 oder 18.1. + 19.1.2023 in Gotha

Ziele:

–        Datenvorverarbeitung zur Nutzung von KI

–        Einsatz von Künstlichen Neuronalen Netzen für spezielle Anwendungen (Lernen mit Lehrer)

–        Nutzung von Anaconda, Tensorflow und Keras an konkreten Beispielen

–        Erarbeitung und Einsatz von KI-Methoden zur Datenverarbeitung

–        KI zur Mustererkennung (z. B. k-MEANS, Lernen ohne Lehrer)

 

Zielgruppe:

–        Erfahrene aus den Bereichen Programmierung, Entwicklung, Anwendung

 

Voraussetzungen:

–        Grundlegende Programmierkenntnisse empfehlenswert (aber nicht erforderlich)

 

Inhalte:

–        Datenverarbeitungsmethoden kennenlernen und nutzen

–        Programmierung und Nutzung von Klassifizierungsmethoden

–        Anwendung vom bestärkenden Lernen (Reinforcement Learning)

–        Einsatz kostenloser und kostenpflichtiger Tools zur Datenauswertung

–        Umfangreiche Darstellung der Ergebnisse

 

Ausweichtermin:

–        18.1. + 19.1.2023 in Gotha

 

Ein Schulungstag umfasst 6 Lehrveranstaltungsstunden (9.30 Uhr – 15.30 Uhr) und findet großenteils am PC statt. Die Verpflegung ist jeweils inklusive.

 

Preis pro Kurs (2 Tage): 980 Euro (netto)

Die Teilnehmerzahl pro Modul ist auf 6 begrenzt.

Rückfragen sowie Anmeldungen: schulung@cc-online.eu

Ansprechpartner: Prof. Dr.-Ing. Christian Döbel (Leiter Steinbeis Transferzentrum „Integrierte Systeme und Digitale Transformation“, ISD)

 

Anbieter-Informationen:
Steinbeis-Transferzentrum ISD (Zentrale: Steinbeis Transfer GmbH) – Ausfeldstr. 21 – 99880 Waltershausen – Tel. 03622 208334
E-Mail SU2209@stw.de
USt.-Ident-Nr. DE814628518 – Registergericht Stuttgart HRB 25312

Geschäftsführer: Dipl.-Ing. (FH) M. Eng. Erik Burchardt

Webinar zum Statistikprogramm R

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R – ein unverzichtbares Werkzeug für Data Scientists. Lassen Sie auch Ihre Mitarbeitenden auf den neusten Stand in der Open Source Statistiksoftware R aus der modernen Datenanalyse bringen. Zielgruppe unserer Fortbildungen sind nicht nur Statistikerinnen und Statistiker, sondern auch Anwenderinnen und Anwender jeder Fachrichtung aus Industrie und Forschungseinrichtungen, die mit R ihre Daten effektiv analysieren möchten. Die Teilnehmenden erwerben Qualifikationen zur selbstständigen Analyse eigener Daten sowie Schlüsselkompetenzen im Umgang mit Big Data.

Webinar zum Statistikprogramm R

Inhalte Basiskurs:

  • 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

Inhalte Vertiefungskurs:

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

Termine:

  • R-Basiskurs: 14. und 15. November 2022 (jeweils 9:00 – 17:30 Uhr)
  • R-Vertiefungskurs: 17. und 18. November 2022 (jeweils 9:00 – 16:30 Uhr)

Kosten: pro 2-tägigem Kurs 750 €; bei Buchung beider Kurse im November erhalten Sie einen Preisnachlass von 200€

Weitere Informationen zu den Inhalten und zur Anmeldung finden Sie unter: https://wb.zhb.tu-dortmund.de/seminare/dortmunder-r-kurse/

Bei Fragen können Sie sich an Daniel Neubauer (daniel.neubauer@tu-dortmund.de; Tel.: 0231 755 6632) wenden.

Zertifikatsstudium – Data Science & Big Data

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Datenanalyse, Datenmanagement und die zielgerichtete Darstellung der Ergebnisse – darum geht es im berufsbegleitenden Zertifikatsstudium ‚Data Science & Big Data‘ der TU Dortmund.

Technische Universität Dortmund: Anmeldungen für das Zertifikatsstudium ‚Data Science & Big Data‘ (Start: Februar 2023) möglich.

Datenanalyse, Datenmanagement und die zielgerichtete Darstellung der Ergebnisse – darum geht es im berufsbegleitenden Zertifikatsstudium ‚Data Science & Big Data‘ der TU Dortmund.

Der Kurs richtet sich an alle Berufsgruppen, die sich mit dem Management und der Analyse von Daten beschäftigen, wie z. B. Data Scientists, Business Analysten, Softwareentwickler, Consultants, wissenschaftliche Mitarbeitende (universitär oder außeruniversitär) o.ä.

Ziel ist der Erwerb moderner Kenntnisse in Theorie und Praxis von Data Science- und Big Data-Projekten. Die Übungen mit realen Datensätzen sowie die Option, die Abschlussarbeit auf Basis von eigenen Daten (‚bring your own data‘) zu verfassen, unterstützen den Transfer des Gelernten in die berufliche Praxis. Das Zertifikatsstudium umfasst zehn Termine und dauert neun Monate. Nach erfolgreicher Abschlussprüfung vergibt die Technische Universität Dortmund ein Zertifikat, mit dem der Kompetenzausbau nachgewiesen werden kann.

Näheres finden Sie unter: https://wb.zhb.tu-dortmund.de/datascience

Bei frühzeitiger Anmeldung oder wenn mehrere Personen aus Ihrem Unternehmen am Kurs teilnehmen, profitieren Sie zudem von unseren Rabattangeboten:

  • Early Bird: Sie erhalten 5% Preisnachlass auf das Teilnahmeentgelt bei Anmeldung bis zum 30. September 2022.
  • Weitersagen lohnt sich: Wenn Sie gemeinsam mit einer/einem Kollegin/Kollegen oder mehreren Personen aus Ihrem Unternehmen am Kurs teilnehmen, reduziert sich das Teilnahmeentgelt bei bis zu zwei angemeldeten Personen um 5 % pro Person, darüber hinausgehend zahlt jede weitere Person 10 % weniger.

Bei Fragen können Sie sich an Daniel Neubauer (daniel.neubauer@tu-dortmund.de; 0231 755 6632) wenden.

CCNA vs. CCNP vs. CCIE Security Certification

As more companies turn to cloud-based software and other advanced solutions, demand for expert IT professionals in the field increases. One popular vendor, Cisco Systems, Inc., makes underlying software and hardware businesses will use for their networks.

If you’re interested in pursuing a career in the data security industry, you may want to consider earning a Cisco security certification. However, there are many types of certificates available, and each one will deliver unique benefits to you and your job marketability.

Learn more about Cisco certifications and learn the difference between CCNA, CCNP and CCIE certifications to help you choose which path is right for you.

Why Earn Cisco Certifications?

The main reason why Cisco provides these security certifications is so IT professionals can fine-tune their skills and build upon their knowledge. When IT professionals earn a Cisco certification, they can use Cisco products and services more easily, help guide customers and troubleshoot customer problems.

A future employer may perceive candidates with certifications as more qualified, productive and someone with a “go-getter” attitude. According to Cisco’s website, 81% of employers associate certifications holders with higher quality and value of work contribution.

However, it’s important to research the various Cisco certifications to learn which ones are most suitable for you and what job you’re interested in. For example, Cisco offers different levels of certifications, ranging from entry-level to expert.

Below are three certifications from Cisco that may be a good fit for you.

CCNA — Cisco Certified Network Associate

A CCNA certification is highly sought after. This certification demonstrates a professional’s ability to install, configure, operate and troubleshoot networks, both routed and switched. No prerequisites are necessary for the CCNA certification. It’s considered an associate-level certification and is available in a few prominent areas, including:

  • Cloud
  • Collaboration
  • Industrial/IoT
  • Security
  • Routing and Switching
  • Service Provider
  • Wireless

One challenge in the data industry is the increased reliance on cloud environments. Using only one cloud provider is a business risk some companies are concerned about. Uptime Institute cites the concentration risk of cloud computing as a major challenge for data centers in 2022.

Earning a CCNA cloud certification may help you get hired for an entry-level position at a company and allow you to support a senior cloud engineer.

Common jobs that you can earn with a CCNA are an IT network engineer, associate networking engineer, network system administrator and cloud architecture and security professional.

CCNP — Cisco Certified Network Professional

The Cisco CCNP certification is a more advanced professional-level certification than the CCNA certification. With the CCNP, you should be able to implement higher-level networking solutions for a company. It will cover the fundamentals of LAN and WAN infrastructures. Here are some of the different areas you can earn a CCNP in:

  • Enterprise
  • Security
  • Service Provider
  • Collaboration
  • Data Center

You must pass some core exams before earning the CCNP certification. Someone looking for the CCNP certification must also qualify for Cisco’s IP switched network and IP routing technologies. This will help determine the candidate’s readiness for the CCNP certification.

Some jobs you may get with a CCNP certification are senior security/network engineer, network architecture, network manager and troubleshooting assistant.

CCIE — Cisco Certified Internetwork Expert

IT professionals who’ve secured the knowledge and technical skills to design, implement and configure security for Cisco solutions and IT resources would be ready to earn the CCIE certification. According to Cisco, an expert-level certification is accepted worldwide as the most prestigious certification in the tech industry. Here are some of the CCIE certifications:

  • Enterprise Infrastructure
  • Collaboration
  • Enterprise Wireless
  • Data Center
  • Security
  • Service Provider

CCIE certifications can open up a range of job opportunities, but it’s a challenging certification to earn. Earning a CCIE means that your end-to-end IT lifecycle skills are valid. You know exactly what you’re talking about regarding networking, LAN/WAN, IPv4 and IPv6 protocols, switches and routers, general information and installation and configuration of various network types.

Jobs you can earn with a CCIE certificate include network security architect, network security specialist, infrastructure consulting practitioner and cloud engineer/architect.

Where to Earn Cisco Certifications

Because Cisco certifications are in such high demand and can open up job opportunities, you may want to know how you can earn them. You earn certificates directly from Cisco’s website. Under Cisco’s Learn tab, there’s plenty of information about certifications, training, events, webinars, support and other services.

There are many online training programs that you can complete to help you prepare for the Cisco certification exams. Here are some websites that offer programs you may want to explore based on the certification you’d like to earn:

For CCNA

  • Udemy
  • ICOHS College
  • Pluralsight
  • Cybrary

For CCNP

  • Udemy
  • INE
  • Global Knowledge
  • Varsity Tutors

For CCIE

  • Udemy
  • Skillshare
  • PluralSight
  • Network Lessons
  • Koenig solutions

These examples are only a few, as other online training programs and resources can set you up for success.

Additionally, Cisco offers several resources on its website to help individuals prepare for certification exams. These include guided study groups and a free Cisco Networking Academy program.

Earning Cisco Certifications

Because many companies, especially large ones, will use Cisco products for their technology infrastructure. Potential IT candidates who list certifications on their resume or job application will have a competitive advantage in the hiring process.

Depending on your current skill level and knowledge, you should be able to determine which Cisco certification is right for you. Cisco’s website has extensive information on each certificate and what topics you’ll learn about. Consider earning a Cisco certification, whether it’s CCNA vs. CCNP vs. CCIE, to bolster your skills and improve your marketability.

Wie kann man sich zum/r Data Scientist ausbilden lassen?

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Das allgegenwärtige Internet und die Digitalisierung haben heutzutage viele Veränderungen in den Geschäften überall auf der Welt mit sich gebracht. Aus diesem Grund wird Data Science immer wichtiger.

In der Data Science werden große Datenmengen an Informationen aus allen Arten von Quellen gesammelt, sowohl aus strukturierten als auch aus unstrukturierten Daten. Dazu werden Techniken und Theorien aus verschiedenen Bereichen der Statistik, der Informationswissenschaft, der Mathematik und der Informatik verwendet.

Datenexperten und -expertinnen, d. h. Data Scientists, beschäftigen sich genau mit dieser Arbeit. Wenn Du Data Scientist werden möchten, kannst Du eine große Karriere in der Data Science beginnen, indem Du Dich für eine beliebige geeignete Weiterbildung einschreibst, der Deinem Talent, Deinen Interessen und Deinen Fähigkeiten in einigen der wichtigsten Data-Science-Kurse entspricht.

Was machen Data Scientists?

Zunächst einmal ist es wichtig zu verstehen, was man eigentlich unter dem Begriff „Data Scientist” versteht. Data Scientist ist lediglich ein neuer Beruf, der in vielen Artikeln häufig zusammen mit dem der Data Analysts beschrieben wird, weil die erforderlichen Grundfertigkeiten recht ähnlich sind. Vor allem müssen Data Scientists die Fähigkeit haben, Daten aus MySQL-Datenbanken zu extrahieren, Pivot-Tabellen in Excel zu verwalten, Datenbankansichten zu erstellen und Analytics zu verwalten.

Data Scientists werden viele Stellen in Unternehmen angeboten, die mit der zunehmenden Verfügbarkeit von Daten konfrontiert sind und Personen brauchen, die ihnen bei der Entwicklung der Infrastruktur helfen, die sie zur Verwaltung der Daten benötigen. Oft handelt es sich um Unternehmen, die ihre ersten Schritte in diesem Bereich machen. Dafür benötigen sie eine Person mit grundlegenden Fähigkeiten in der Softwaretechnik, um den gesamten Prozess voranzutreiben.

Dann gibt es stark datenorientierte Unternehmen, für diejenigen Daten sozusagen Rohprodukt und Rohstoff darstellen. In diesen Unternehmen werden Datenanalyse und maschinelles Lernen recht intensiv betrieben, wodurch Personen mit guten mathematischen, statistischen oder sogar physikalischen Fähigkeiten benötigt werden.

Es gibt auch Unternehmen, die keine Daten als Produkt haben, aber ihre Zukunft auf sie und ihre Sinne planen und abstimmen. Diese Unternehmen werden immer mehr und brauchen sowohl Data Scientists mit grundlegenden Fähigkeiten als auch Data Scientists mit speziellen Kenntnissen, von Visualisierung bis hin zu Machine Learning.

Kompetenzen der Data Scientists

Die Grundlagen sind zunächst für alle, die im Bereich der Data Science arbeiten, dieselben. Unabhängig von den Aufgaben, die Data Scientists zu erfüllen haben, muss man grundlegende Softwaretechnik beherrschen.

Selbstverständlich müssen Data Scientists mit Programmiersprachen wie R oder Python und mit Datenbanksprachen wie SQL umgehen können. Sie bedienen sich dann statistischer, grundlegender Fähigkeiten um zu bestimmen, welche Techniken für die zu erreichenden Ziele am besten geeignet sind.

Ebenso sind beim Umgang mit großen Datenmengen und in sogenannten „datengetriebenen” Kontexten Techniken und Methoden des maschinellen Lernens wichtig: KNN-Algorithmen (Nächste-Nachbarn-Klassifikation für Mustererkennung), Random Forests oder Ensemble Techniken kommen hier zum Einsatz.

Entscheidend ist, die für den jeweiligen Kontext am besten geeignete Technik unterscheiden zu können, und dies bevor man die verschiedenen Werkzeuge beherrscht.

Die lineare Algebra und die multivariate Berechnung sind auch unerlässlich. Sie bilden die Grundlage für viele der oben beschriebenen Fähigkeiten und können sich als nützlich erweisen, wenn das mit den Daten arbeitende Team beschließt, intern eigene Implementierungen zu entwickeln.

Eins ist noch entscheidend. In einer idealen Welt werden die Daten korrekt identifiziert, da sie vollständig und kohärent sind. In der realen Welt muss sich der Data Scientist mit unvollkommenen Daten auseinandersetzen, d. h. mit fehlenden Werten, Inkonsistenzen und unterschiedlichen Formatierungen. Hier kann man von Munging sprechen, d. h. von der Tätigkeit, die sogenannten Rohdaten in Daten umzuwandeln, die ein einheitliches Format haben und somit in den Prozess der Aufnahme und Analyse einbezogen werden können.

Wenn Daten als wesentlich für Geschäftsentscheidungen sind, reicht es nicht aus, eine Person zu haben, die sie verarbeiten, analysieren und aufnehmen kann. Die Visualisierung und Kommunikation von Daten ist ebenso zentral. Daten zu visualisieren und zu kommunizieren bedeutet, anderen die angewandten Techniken und die erzielten Ergebnisse zu beschreiben. Daher ist es wichtig zu wissen, wie man Visualisierungswerkzeuge wie ggplot oder D3.js verwendet.

Ausbildungsmöglichkeiten und Bootcamps, um Data Scientist zu werden

Kurz gesagt gibt es zwei gängige Wege, um Data Scientist zu werden.

  • Auf der einen Seite kann man einen Universitätslehrgang absolvieren. Diese Art von Studiengang führt zu einem spezialisierten Abschluss, der nach einem dreijährigen Bachelorabschluss in Informatik, Mathematik oder Statistik absolviert werden kann. In den letzten Jahren wurden diese neuen Studiengänge an den europäischen Universitäten immer häufiger angeboten.
  • Auf der anderen Seite kann man sich für eine Weiterbildung zum/r Data Scientist anmelden, zum Beispiel eine Weiterbildung von DataScientest. Als national und international anerkannte Ausbildungsorganisation bietet DataScientest eine Weiterbildung zum/r Data Scientist an, die sich an Personen mit einem Bachelorabschluss und Kenntnissen in Kommunikation wendet. Ihr großer Vorteil ist die persönliche Betreuung, die allen Teilnehmer und Teilnehmerinnen angeboten wird, sowie ein Fernstudium, das 85% individuelles Coaching und 15% Masterclasses umfasst. Alles läuft über eine sichere Plattform, damit jeder Teilnehmer und jede Teilnehmerin codieren, Daten erforschen usw. können.

Bei dieser DataScientest-Weiterbildung haben die Lernenden die Wahl zwischen einer weitgehenden Ausbildung (10 Stunden pro Woche) oder einer Bootcamp-Ausbildung (35 Stunden pro Woche). 

Das am Ende des Kurses erworbene Zertifikat wird von der Pariser Universität La Sorbonne anerkannt.   

10 Best Resources To Learn Data Science Online in 2022

Today, data science is more than a buzzword. To simply put it, data science is an interdisciplinary field of gathering data from various sources and channels such as databases, analysing and transforming them into visualization and graphs. This basically facilitates the readability and understanding of the data to aid in soft-skills like insightful decision-making for any organization or business. In short, data science is a combination of incorporating scientific methods, different technologies, algorithms, and more when it comes to data.

Apart from the certified courses, as a data scientist, it is expected to have experience in various domains of computer science, including knowledge of a few programming languages such as Python and R as well as statistics and mathematics. An individual should be able to comprehend the data provided and be able to transform it into graphs which help in extracting insight for a particular business.

Best Resources To Learn Data Science

For those pursuing a career in data science, it is not just technical skills that matter, in business settings an individual is tasked with communicating complex ideas and making data-driven insightful decisions. As a result, people in the field of data science are expected to be effective communicators, leaders, and team members as well as high-level analytical thinkers too.

If we talk about applications of data science, it is used in myriad fields, including image and speech recognition, the gaming world, logistics and supply chain, healthcare, and risk detection, among others. It remains a limitless world indeed. Data scientists will continue to remain in high demand, while at the same time there is a substantial skill gap that needs to be currently addressed in the industry.

Here’s the lowdown on a few of the online resources—in no particular order—which can be checked out to learn data science. While a few of these educational platforms have been launched a couple of years ago, they would continue to hold equal relevance when it comes to resources for seeking in-depth knowledge related to everything in the field of data science.

1. Udemy

Udemy is a site that offers hands-on exercises while extending comprehensive data courses. At last count, there were about 10,000 data courses and almost 500 of which are free of cost. An individual can discover specialisations, including Python, Tableau, R, and many more. While offering real-world examples, Udemy courses are quite well-defined when it comes to specific topics.
The courses are suitable for beginners as well as experts in the field of data science.

2. Coursera

Coursera is another online learning platform that offers massive open online courses (MOOC), specialisations, and degrees in a range of subjects, and this includes data science as well. Some of the courses hosted on the platform include top-notch names such as Harvard University, University of Toronto, Johns Hopkins University, University of Michigan, and MITx, among others. Coursera courses can be audited for free and certificates can be obtained by paying the mentioned amount. The courses from Coursera are part of a particular specialisation, which is a micro-credential offered by Coursera. These specialisations also include a capstone project.

3. Pluralsight

Pluralsight remains an educational platform for learners through insights from instructor-led courses or online courses, which lay stress on basics and some straightforward scenarios. Courses taken online will require you to exert more effort to gain detailed insights, thus helping you in the longer run. Pluralsight introduces one to several video training courses for Software developers and IT administrators.

By using the service of Pluralsight, an individual can look forward to learning a lot of solutions. An individual can even get the key business objectives and even close the skill gaps in critical areas like cloud, design, security, and mobile data.

4. FlowingData

The website, which is produced by Dr. Nathan Yau, Ph.D., offers insights from experts about how to present, analyse, and understand data. This comes with practical guides to illustrate the points with real-time examples. In addition, the site also offers book recommendations, as well as provides insights related to the field of data science.
There are also articles which an individual can browse related to gaining more in-depth insight into the correlation between data science and the world around.

5. edX

edX is an online platform, which has been created as a tie-up between Harvard University and the Massachusetts Institute of Technology. This website has been designed with the idea to highlight courses in a wide range of disciplines and deliver them to a larger audience across the world. edX extends courses that are offered by 140 top-notch universities at free or nominal charges to make learning easy. The website includes at least 3,000 courses and has programs available for learners to excel in the field of data science.

6. Kaggle

Kaggle is an online learning platform that would be quite beneficial for individuals who already have some knowledge related to data science. In addition, most of the micro-courses require the users to have some prior knowledge in data science languages such as Python or R and machine learning. It remains an ideal site for upgrading skills and enhancing the capabilities in the field of data science. It offers extensive insights related to the field from experts.

7. GitHub

GitHub remains a renowned platform that uses Git, which is a DevOps tool used for source code management, to apply version control to a code. With over 40 million developers on its users list, it also opens up a lot of opportunities for data scientists to collaborate and manage projects together, besides gaining insights about the industry that continues to remain high in demand at the moment.

 

 

8. Reddit

This is a platform that comprises sub-forums, or subreddits, each focused on a subject matter of interest. Under this, the R/datascience subreddit has been titled the data science community, which remains one of the larger subreddit pages related to data science. Various data science professionals discuss relevant topics in data science. The data science subreddit remains insightful for individuals seeking a community that can provide related technical advice in the field of data science.

9. Udacity

Udacity Data Science Nanodegree remains an ideal certification program for those who remain well-versed with languages such as Python, SQL, machine learning, and statistics. In terms of content, Udacity Data Science Nanodegree remains quite advanced and introduces hands-on practice in the form of real-world projects. While Udacity doesn’t offer an all-inclusive course, it introduces separate courses for becoming an expert in the field of data science. Professionals who aspire to become data scientists are advised to take Udacity’s three courses namely Intro to Data Analysis, Introduction to Inferential Statistics, and Data Scientist Nanodegree. These three courses extend real-world projects, which are provided by industry experts. In addition, technical mentor support, flexible learning program, and personal career coach and career services are also offered to aspirants in the domain.

10. KDnuggets

KDnuggets remains a resourceful site on business analytics, big data, data mining, data science, and machine learning. The site is edited by Gregory Piatetsky-Shapiro, a co-founder of Knowledge Discovery and Data Mining Conferences. KDnuggets boasts of more than 4,00,000 unique visitors and has about 1,90,000 subscribers. The site also provides information related to tutorials, certificates, webinars, courses, education, and curated news, among others.

 

Ending Note

Increasing technology and big data mean that organizations must leverage their data in order to deliver more powerful products and services to the world by analyzing that data and gaining insight, which is what the term “Data Science” means. You can jumpstart your career in Data Science by utilizing any of the resources listed above. Make sure you have the right resources and certifications. Now is the time to work in the data industry.

 

Zusatz-Studium „Data Science and Big Data“ an der TU Dortmund

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Jetzt anmelden für das weiterbildendes Studium „Data Science and Big Data“ an der Technischen Universität Dortmund!

Im Februar 2022 startet das berufsbegleitenden weiterbildende Studium „Data Science and Big Data“ an der Technischen Universität Dortmund zum 6. Mal.
Renommierte Wissenschaftlerinnen und Wissenschaftlern vermitteln Ihnen die neuesten datenwissenschaftlichen Erkenntnisse und zeigen, wie dieses Wissen praxisnah im eigenen Big-Data Projekt umgesetzt werden kann. Von der Analyse über das Management bis zur zielgerichteten Darstellung der Ergebnisse lernen Sie dabei Methoden der Disziplinen Statistik, Informatik und Journalistik kennen.

Das weiterbildende Studium richtet sich an alle Personen, die über einen natur-  oder ingenieurwissenschaftlich/ statistische Studienhintergrund verfügen oder aufgrund ihrer mehrjährigen Berufserfahrung mit Fragestellungen zum Thema Datenanalyse vertraut sind.

Mögliche Berufsgruppen sind:

  • Data Analyst
  • Consultant/ Unternehmensberater
  • Business Analyst
  • Software-Entwickler

Das weiterbildende Studium umfasst 10 Veranstaltungstage über eine Dauer von 10 Monaten (Kursabschluss: November 2022). Die Kosten betragen 6.900 € (zahlbar in 3 Raten). Bewerbungsschluss ist der 29. November 2021. Weitere Informationen und Hinweise zur Anmeldung finden Sie unter: https://wb.zhb.tu-dortmund.de/zertifikatskurse/data-science-and-big-data/

Bewerbungsformular für Zusatzstudium an der TU Dortmund

Bewerbungsformular (Download)

 

Bei Fragen können Sie sich gerne an den zuständigen Bildungsreferenten Daniel Neubauer wenden: daniel.neubauer@tu-dortmund.de oder 0231/755-6632

7 Ways To Advance Your Data Science Knowledge and Expertise

Image Source: Pexels

As a data scientist, your knowledge and expertise are what powers industries. Businesses of all sectors of the economy now rely on data to inform their business processes. As many as 53% of companies have already adopted big data analytics, highlighting the upward trend in data science within the private sector.

Businesses rely on data scientists to stay competitive facing in this market. But how can you advance your data science knowledge and expertise to bring the most value to your work?

These seven strategies will help you build your resources and improve your opportunities to grow.

1. Recognize the Need for Growth

It may seem disheartening at first to realize that there is no end to the progress you can make in honing your data science skills. There is simply too much to master in just a few years. However, what this really means is that there is no end to the progress and advancement you can make as a data scientist.

Consider the breadth of what there is to know. Skills to master include probability, new programming languages, data visualization, data intuition, and so much more. Recognize the scope of your field to open the door to learning opportunities in data science.

2. Brush Up on the Latest Trends

Your opportunities as a data scientist are largely dependent on how well you can utilize new software and data analytics trends. Modern data analytics relies on artificial intelligence and machine learning processes to drive insights with unprecedented detail. Meanwhile, data communication and storage platforms like blockchain are emerging to supplement data management infrastructures.

An awareness of these modern developments paired with basic general knowledge and qualifications will be key to getting hired as a data scientist in 2021 and beyond. As companies across industries look to pivot to new tech and competitive data strategies, it is more important than ever to keep abreast of the latest data science trends.

3. Enroll in Data Science Bootcamps

Data science is a constantly changing field, driven by technological innovation. At the same time, the breadth of opportunities that exist in a tech field invite career flexibility. Data scientists can make the most of these advancement and flexibility opportunities by enrolling in boot camps and training courses designed to fill in skills gaps.

These programs cover a range of topics within the field of data science. No matter your level of expertise and education, engaging in supplemental training can help you advance your expertise and bring value-building benefits to your role as a data scientist.

4. Look for Guidance Online

Because of the increasingly virtual nature of all kinds of work and education, opportunities for data science growth may be better sought out online. There are many ways you can go about increasing your data science expertise on a virtual platform. From finding a mentor through social media like LinkedIn to participating in training courses crafted by other data science professionals, you can expand your knowledge base.

First, however, ensure that you have a productive workspace at home that will allow you to learn and grow while staying motivated. This means setting up a home office to accommodate the virtual shift, complete with a comfortable chair and desk set up to avoid neck strain and health problems.

With virtual guidance in a productive environment, you can advance your expertise to secure the value of your position.

5. Expand Your Horizons

Data science is a multifaceted arena. The role of a data scientist typically consists of harnessing and categorizing raw data to draw out useful and predictive insights. Meanwhile, other positions in analytics and IT lend to more powerful data results.

Customer analytics, for example, is another subset of data science that involves harnessing information to describe and predict customer journeys. This entails focusing on customer demographics and behaviors to assemble more carefully targeted buyer personas, which can then be used to increase customer engagement and conversion rates.

Through broadening your data skills to account for areas like customer analytics, you can advance your professional opportunities.

6. Let Your Passions Inspire You

Every data scientist has a reason they got into their field. Your passions and inspirations can inform new avenues of exploration into the many designations surrounding data science. For example, big data analysts, machine learning specialists, and data visualization experts all play vital roles in modern business.

Finding your niche and specialization can come down to what drove you into data science in the first place. Perhaps you have a talent for creating comprehensive visuals that expertly summarize the point you want to be taken from your graphic. Alternatively, diving deep into the ins and outs of algorithmic functions may be what inspires you most.

Explore your passions and commit to a lifetime of learning and growing.

7. Never Stop Improving

With rapid technological change, data scientists must maintain their awareness of new systems and processes at all times. Innovations in AI, for example, have created a skills gap in the market. Eighty percent of business leaders say that lack of talent is the biggest obstacle in AI implementation.

For data scientists, closing this skills gap can be a simple matter of improving your technological training over time. Learning how machine learning functions, for example, can assist in your application of this tech to increase the value you add to your business.

Never stop improving through new courses and credentials that explore changing technology and how these changes affect the world of data science. With a commitment to lifelong learning, your skills as a data scientist will never go out of vogue.

These seven strategies can help you formulate a plan to expand your expertise into new territory, leading to new opportunities and a lucrative financial future.

Data Security for Data Scientists & Co. – Infographic

Data becomes information and information becomes knowledge. For this reason, companies are nowadays also evaluated with regard to their data and their data quality. Furthermore, data is also the material that is needed for management decisions and artificial intelligence. For this reason, IT Security is very important and special consulting and auditing companies offer their own services specifically for the security of IT systems.

However, every Data Scientist, Data Analyst and Data Engineer rarely only works with open data, but rather intensively with customer data. Therefore, every expert for the storage and analysis of data should at least have a basic knowledge of Data Security and work according to certain principles in order to guarantee the security of the data and the legality of the data processing.

There are a number of rules and principles for data security that must be observed. Some of them – in our opinion the most important ones – we from DATANOMIQ have summarized in an infographic for Data Scientists, Data Analysts and Data Engineers. You can download the infographic here: DataSecurity_Infographic

Data Security for Data Scientists, Data Analysts and Data Engineers

Data Security for Data Scientists, Data Analysts and Data Engineers

Download Infographic as PDF

Infographic - Data Security for Data Scientists, Data Analysts and Data Engineers

Infographic – Data Security for Data Scientists, Data Analysts and Data Engineers

Role of Data Science in Education

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Data science is a new science that appeared thanks to a lot of reasons. The first reason is that nowadays, we have enough capacity to gather data and later work with it. The second reason is that society accumulates a lot of information every minute, and gadgets can save and then send it to data centers without any communication between people. But saving data is just one step in the world of science. The main task is how to analyze and show the results and later make a conclusion and prognoses. A team of online essay experts from a professional academic writing company SmartWritingService.com say that requests for academic papers for data science research topics increase extremely. It happens because data science analysts’ knowledge is useful in a lot of spheres, and the demand for such specialists is very high. Data science is an important part of sociology, political forecasting, the theory of games, statistics and others. Students need to study it and use it for future research. That’s why the universities added the courses of big data to be modern and meet requirements. Let’s try to discover the role of data science in education to make your own conclusion about its importance.

Why is data science necessary and how to become good in it?

The improvement of the studying process.

All students are different. They use different skills for studying and perceive information differently according to a lot of nuances. For some of them, the best way of getting information from lectors is listening without interruption. Other students prefer discussion during lectures. Some prefer to make notes. Others like to listen carefully and make notes later using the audio version of the lecture. Every group is different but has the same goal, and this goal is to absorb as much information as possible. The best assistant for this goal is data science that will show the teacher the best ways of communicating with students.

Using big data for personal needs. 

Have you ever thought that every one of us is a data scientist? We all use data, analyze it, and act according to conclusions. For example, shopping. Every time you go to the grocery, you notice how many people are there and how long the line is. When you plan your next shopping, you make a prognosis according to that data — the time you need to spend in the market and make a decision if it is optimal to go right now or it is better to visit the shop later when it is almost empty. The same thing works when we talk about studying. According to your observation and experience (that are both data), you make a conclusion on how much time you need to spend on every task.

Learning data science as an additional course. 

To know data science as an additional profession nowadays is very helpful. For the employer, it will be a bonus that can be a decisive factor. The skill to analyze is essential for every profession and helps to understand the market now and necessary for sales. The hardest thing for a data scientist is to ask the right questions for collecting data, and if you are good at it, your salary will increase immensely.

How to become a data science specialist?

Big data and artificial intelligence (AI). The importance of development. We believe that AI is the future of studying. First of all, the machine can collect the information and repeat it as much as the student needs. It is studying with the group, and the quality of communication grows rapidly. The base for machine learning in AI is data science. The analysis of data gives the set of possible reactions and actions to AI that can be changed or improved according to new data that was processed by AI. But from the beginning, AI is a huge set of data. It consists of reactions to life situations, speed and timbre of the voice of the interlocutor, country, the hour of the day, and a lot of other data that finally lead to the reaction of AI to the situation. The development of AI is a question of time, and it will help us to move faster in all spheres of life.

Can we ignore data analytics and don’t take a part in it?

The only way to ignore data science is to throw away all gadgets and become citizens of the wood cabin. And even this step won’t help. Those interested in the amount of population of people who live in the woods will be happy to add you to their list and analyze your +1 using data science technology. Every time you buy bread or don’t buy something on the internet, you are counted. Later they will analyze why you became or not their customer and save for statistics you age, sex, country of request, and all other parameters they can catch. We can only accept this reality and try to use it to our needs.

Don’t be afraid to become a subject for data science. It doesn’t affect your privacy a lot because there are billions of us, and we are only one point for statistics. Thanks to such analytics, scientists can make better specific offers and content for you. On the one hand, it is a kind of manipulation, but it saves time and resources for research from another hand. Use it to make your life more comfortable but remember that there are no coincidences when we talk about data.

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