Cursus
Microsoft Excel Essentials course
Duur
3 weken
Niveaus
Beginner
Formaat
Blended learning
Volgende groep start
May 28, 2026
Wil je je Excel-vaardigheden verbeteren en het volledige potentieel van dit krachtige spreadsheetprogramma benutten? Onze cursus is ontworpen om van jou een Excel-expert te maken en je productiviteit een boost te geven.
Amsterdam Data Academy biedt dit bootcamp al 7 jaar aan en heeft al meer dan 1.500 mensen getraind. We begrijpen dat iets nieuws leren niet altijd makkelijk is. Daarom hebben we onze blended learning methode ontwikkeld, waarbij een persoonlijke aanpak centraal staat. Leer meer over onze methode.
Geef je carrière een boost
- Betere carrièremogelijkheden en een hoger verdienpotentieel
- Optionele toegang tot topstages in de datawereld
- Directe toegang tot een online blended leeromgeving
- Een voorsprong om door te stromen naar expertposities
Wat kun je van deze cursus verwachten?
Na deze cursus
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Een goede beheersing hebben van de basisprincipes van Excel, waaronder het opmaken, sorteren en filteren van gegevens
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Bedreven zijn in geavanceerde Excel-functies zoals draaitabellen en formules zoals VLOOKUP
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Je vaardigheden kunnen toepassen op praktijkscenario's en praktische bedrijfsproblemen kunnen oplossen met Excel
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Excel met vertrouwen gebruiken als hulpmiddel voor gegevensanalyse en geïnformeerde besluitvorming
Ontdek onze unieke leeraanpak
Wat heeft Amsterdam Data Academy jou te bieden?
Amsterdam Data Academy biedt dit bootcamp al 7 jaar aan en heeft al meer dan 1.500 mensen getraind. We begrijpen dat iets nieuws leren niet altijd makkelijk is. Daarom hebben we onze blended learning methode ontwikkeld, waarbij een persoonlijke aanpak centraal staat.
Blended learning
We bieden online leren met zeer interactieve live sessies.
Persoonlijke benadering
We hebben een beperkt aantal studenten per cursus, zodat we je alle persoonlijke aandacht kunnen geven die je nodig hebt.
Officieel diploma
Ontvang het officiële diploma van de nummer 1 dataopleider ter wereld.
Docenten van deze cursus
Leer van de beste in de branche. Onze datacursus in Amsterdam wordt gegeven door doorgewinterde praktijkmensen met tientallen jaren ervaring. praktijkervaring bij internationale topbedrijven.
Why AI Ethics, AI & Data Literacy, and the EU AI Act must work together for Responsible AI
Why AI Ethics, AI & Data Literacy, and the EU AI Act must work together for Responsible AI
By Dr. Hanan ElNaghy Artificial Intelligence is no longer a distant technological concept. It is already embedded in our daily lives, helping us in filling common tasks such as writing emails to more advanced processes such as screening job applicants, detecting fraud, promoting products, and even assisting medical professionals in diagnosis. However, as AI becomes further integrated into our social and professional systems, an important question emerges upon which we must consciously reflect:How do we ensure that AI is used responsibly?
Frankly, there is no single answer or simple solution to this question. Responsible AI naturally asks us to combine AI literacy, data literacy, ethical awareness, and regulatory frameworks such as the EU AI Act. Amidst this rapid AI integration, these elements must work together to ensure AI systems will continue to support en aid our society rather than harm it. In this article, I will share key insights from our recent webinar at Amsterdam Data Academy on how organizations and individuals can move toward responsible AI adoption en ethically utilise AI tools to improve efficiency, organisation and simplify complex tasks, all while furthering their unique quality of work.AI Literacy is more than just using ChatGPT
When people first hear the term AI-geletterdheid, many immediately think about tools like ChatGPT, assuming this phrase simply refers to knowing how to effectively prompt AI. However, AI literacy is much deeper than that. True AI literacy includes forming a fundamental understanding of the basics behind how AI models function, delving into technicalities such as the difference between automation en learning systems as well as the way in which AI systems learn patterns vanaf historical data. Moreover, a critical component of learning about AI literacy is being able to actively evaluate these models and their use, understanding the risks of bias, hallucinations en incorrect inputs, generally identifying the limitations of these AI models. Many people assume AI simply automates tasks. Quite the contrary, AI systems are fundamentally different: they learn vanaf past data naar predict future outcomes. Once we understand this principle, it becomes easier to explain phenomena such as bias or hallucinations. For example, if an AI system is trained on biased or incomplete data, the output will also be biased. This is why the famous principle “Garbage In, Garbage Out” still applies strongly in AI systems. This is why AI-geletterdheid must be built on top of data literacy: Before people can responsibly use AI, they must first be able to answer the following questions:- What does high-quality data look like?
- What does fair representation in data mean?
- How do we identify bias in datasets?
- How does outdated or incomplete data lead to unreliable predictions?
The EU AI Act: A Risk-based Approach to AI
To address the growing social, societal, and technological impact of AI systems, the European Union introduced the EU AI-wet, which builds on the foundation of earlier regulations such as GDPR. This AI Act introduces a risk-based regulatory framework, categorizing AI systems based on their potential risk and general impact. The main categories include:-
Unacceptable Risk
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High Risk
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Limited Risk
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Minimal Risk
To help professionals and organizations prepare for these new obligations, Amsterdam Data Academy offers an AI Literacy & Ethics course focused on responsible AI use, governance, and EU AI Act compliance.
AI Governance: Moving beyond “tick-the-box” compliance
Many organizations interpret the aforementioned AI literacy requirements as a simple training exercise: “Let’s send all employees to any course to get this over with and consider ourselves legally compliant.” However, responsible AI requires more than a single training session. This is where AI governance becomes essential, a term that refers to the rules, policies, processes, and responsibilities that guide how AI systems are developed, deployed, en monitored within an organization. Key governance components include:- Fairness & Ethics
- Transparency & Accountability
- Data Privacy
- Risk Monitoring
- Redress mechanisms
The challenge: Regulation vs Innovation
Furthermore, one of the biggest challenges in AI regulation is speed. Legislation is inherently slow while AI innovation is faster than ever. New AI developments and innovations appear almost every week, sometimes even daily. Meanwhile, laws may take years to develop and implement internationally. This creates a difficult balance: If regulation is too slow, it becomes obsolete while if regulation is too strict, companies may simply avoid regulated markets altogether. We have already seen this happen with GDPR: Some companies decided not to enter the European market as they considered their compliance would be too complex or costly. This is why regulators must find the “regulatory sweet spot”: A framework that protects society while still encouraging innovation, also applying within organizations. Internal AI governance should be flexible, regularly reviewed, en responsive to change to keep up with modern AI ethical and technological developments alike.Keeping Humans in the Loop
Another important principle in responsible AI is Human-in-the-Loop (HITL). AI systems should not operate entirely without human oversight, especially when decisions affect people’s lives. A useful distinction here is between two modes of AI use:-
Generative AI
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Assistive AI
AI Literacy Must Be Context-Specific
Another misconception is that AI literacy can be taught as a single universal course. In reality, AI literacy must be customized for different fields. Bijvoorbeeld:- Doctors need to understand AI in medical diagnosis
- HR professionals need to understand AI in professional recruitment
- Engineers require deeper technical knowledge
- Managers need strategic and ethical understanding
Responsible AI in Talent Acquisition: A Practical Example
Recruitment and talent management are strong candidates for AI support because they involve large volumes of data en time-consuming processes, such as CV screening, interview scheduling, candidate assessments, and performance predictions. However, this also makes recruitment a high-risk application. If AI models are trained on biased previous data, they may reproduce those biases themselves. For example, if past hiring managers favored certain groups unfairly, the AI system might learn and replicate those discriminative and unjust patterns. Responsible AI use in recruitment should therefore include:- Bias detection in historical data
- Transparency with candidates about AI use
- Data minimization (collecting only necessary information)
- Consent from applicants
- Human oversight in final hiring decisions
- Continuous monitoring of model performance
Responsible AI Is a Shared Responsibility
Responsible AI is not only the responsibility of AI developers or regulators. It is a shared responsibility between organizations, developers, policymakers, educators, and end users alike. Even users interacting with AI systems should provide constructive feedback when outputs are incorrect. Responsibility goes both ways.The Future of Responsible AI
To conclude, the question is no longer whether AI will reshape our world, it already has. The real crux lies behind whether we are prepared to shape it responsibly. As AI will continue evolving rapidly, regulations will also evolve in turn, and organizations will need to continuously en quickly adapt to these unfamiliar changes. However, one thing is already clear: Responsible AI cannot exist without the active integration of AI literacy, ethical awareness, data literacy, en governance frameworks. By educating people, designing responsible systems, and building adaptive regulations, we can ensure that AI remains a tool that supports human decision-making rather than fully replacing it. Most importantly, to protect the unique thought and nuance behind human capabilities while still keeping up with and incorporating modern technological advancement in our favour, we as a society must always remember: AI should augment human intelligence, not replace it.
Lucia Riscado Cordas
Webinar: Navigating AI Responsibly — Ethics, AI Literacy & the AI Act
Navigating AI Responsibly
Hanan explored the EU AI Act and its implications for professionals and organisations.
Spreker
Hanan — AI Ethics Expert
Details
Date: March 16, 2026
Location: Online
Price: Free
Webinar: What is the SLIM Subsidy
Unlock €25K SLIM Subsidy for Data & AI Training
Olivier van Hees and Wout from Twin AI explained how to apply for the SLIM subsidy.
Luidsprekers
Olivier van Hees — Amsterdam Data Academy
Wout — Twin AI
Details
Date: February 25, 2026
Location: Online
Price: Free
Can you follow a training program at Amsterdam Data Academy that fits your work schedule?
Do you work full-time but want to boost your career with a data science or AI program? Then flexibility in your learning journey is probably one of your top priorities. At Amsterdam Data Academy, we understand that busy professionals still want to grow their skills. That’s exactly why our programs are designed to fit your life, not the other way around.
Blended learning: The best of both worlds
Amsterdam Data Academy offers a unique blended learning approach that combines online self-paced learning with interactive live sessions. This model is specifically created for working professionals who want flexibility while still benefiting from personal guidance and a strong learning community.
With blended learning, you can access course materials whenever it suits you. Study early in the morning before work, during your lunch break, or in the evening after a long day. Live sessions are carefully scheduled in the evenings or weekends to avoid conflicts with regular working hours.
Courses and bootcamps: Flexible options for every level
Amsterdam Data Academy provides both short courses and comprehensive bootcamps.
Short courses usually focus on one specific topic and last between 3 and 6 weeks. They’re perfect if you want to develop targeted skills without a large time commitment.
For those aiming for a full career switch or deep upskilling, bootcamps run between 3 and 6 months. These programs combine multiple courses and can optionally include an internship and career coaching. Thanks to the modular structure, you can also follow courses individually and combine them at your own pace around busy work periods.
Small class sizes for personal attention
One of the key strengths of Amsterdam Data Academy is its intentionally small class sizes. Instead of getting lost in a crowd, you receive real personal guidance from instructors.
You can ask questions, receive feedback on your projects, and connect with fellow professionals. This is especially valuable when balancing learning alongside a full-time job.
Financing options for working professionals
Amsterdam Data Academy offers several funding options to make training accessible:
- UWV support for job seekers
- Municipality vouchers for specific groups
- 0% rente TechMeUp leningen
- Flexible installment plans
These options allow you to invest in your future without financial pressure.
Official certification and recognition
After completing a course or bootcamp, you receive an official certificate from Amsterdam Data Academy. The academy is certified by NRTO and CRKBO, meaning its programs meet high educational quality standards in the Netherlands.
These certificates strengthen your CV and LinkedIn profile and help demonstrate your new skills to employers, whether you’re aiming for a promotion or a new career opportunity.
Free study consultation: Which program fits you best?
Not sure which program suits your work schedule and career goals? Amsterdam Data Academy offers free study consultation sessions where you can discuss:
- Time commitment per course or bootcamp
- Live session scheduling
- Course content and skill level
- Financing options
- Career prospects after completion
Conclusion: Flexible learning for ambitious professionals
So, can you follow a training program at Amsterdam Data Academy that fits your work schedule? Absolutely.
With blended learning, evening and weekend sessions, modular course structures, small class sizes, and flexible payment options, Amsterdam Data Academy is built specifically for working professionals who want to develop data and AI skills without putting their lives on hold.
Whether you’re looking for a one-week introduction course or a multi-month intensive bootcamp, there’s a learning path that fits your goals and availability.
Invest in yourself, at your own pace, and take the next step in your career with confidence.
Can you fit Data Science Training into your busy work schedule?
Many professionals who consider transitioning into data science share the same concern:
“I work full-time, often into the evening. How can I realistically fit a comprehensive data science bootcamp into my schedule?”
This is one of the most common and completely valid questions aspiring data professionals face today.
The reality is that career development shouldn’t require you to press pause on your life. Whether you’re working nine-to-five (or more realistically, nine-to-seven), juggling family responsibilities, or managing other commitments, the right educational program should adapt to you, not the other way around.
The Flexibility Challenge in Data Education
Traditional education models weren’t built for working professionals. The rigid schedules, mandatory attendance at specific times, and intensive daily commitments made sense in a different era. But today’s aspiring data professionals need something different. You need to upskill without downshifting your career or sacrificing your income.
This is precisely why blended learning has emerged as a game changer in data science education. Instead of choosing between your current job and your future career, blended learning approaches allow you to build new skills around your existing schedule.
What Makes Blended Learning Work for Busy Professionals
At Amsterdam Data Academy, the learning model is specifically designed to accommodate working professionals. Through their blended data science training programs, learners can combine self-paced online study with interactive live sessions. Here’s how it works in practice:
The online components allow you to study at your own pace, tackling coursework when it fits your schedule, early mornings before work, evenings after dinner, or weekend afternoons. This self-paced structure means you’re never racing to catch up or waiting for others to move forward.
However, flexibility doesn’t mean isolation. The highly interactive live sessions provide structured touchpoints where you engage directly with instructors and fellow learners. These sessions are scheduled to maximize accessibility, and because class sizes are intentionally limited, you receive personalized attention that larger programs simply cannot offer.
Real Flexibility Means Real Options
The beauty of a truly flexible program is that it meets you where you are. Some weeks, you might have more time to dedicate to learning and can push ahead with multiple modules. Other weeks, when work demands peak or personal commitments intensify, you can adjust your pace accordingly.
This adaptive approach is particularly valuable in data science education, where concepts build on one another. You need time to practice, experiment, and truly internalize skills like Python programming, statistical analysis, or machine learning algorithms. Rushing through material just to meet arbitrary deadlines doesn’t serve anyone.
The Power of Community and Support
One concern many professionals have about flexible learning is the fear of feeling disconnected or lacking accountability. This is where the structured support system becomes crucial. Regular live sessions create natural milestones and accountability checkpoints. You’re not just watching pre-recorded videos in isolation, you’re part of a learning community.
The personal approach facilitated by limited class sizes ensures that instructors understand your individual challenges and progress. They can provide targeted guidance, answer specific questions, and help you navigate tricky concepts. This personalized attention is something you’d never get in massive online courses with thousands of students.
Making the Most of Your Flexible Schedule
To truly benefit from a flexible learning structure, consider these practical approaches:
Create a realistic weekly schedule. Block out specific times for coursework, even if those times vary week to week. Treat these blocks as seriously as you would work meetings.
Leverage your commute. If you travel to work, use that time to review concepts, watch shorter lecture segments, or plan your practice exercises.
Communicate with your instructors. If you’re facing an exceptionally busy period at work, let them know. The personal approach means they can help you strategize how to maintain momentum without becoming overwhelmed.
The Investment That Fits Your Life
Many professionals who follow a blended learning path successfully complete their training while maintaining full-time jobs. They build strong project portfolios, gain practical experience, and transition into data roles without disrupting their income or burning out.
Their success is not about working extreme hours or sacrificing personal life. It’s about choosing an educational approach designed for working professionals who need real flexibility without compromising quality.
Your Next Step
If you’ve been hesitating to start your data science journey because of schedule concerns, it’s worth exploring programs specifically designed for working professionals. The combination of self-paced online learning with interactive live sessions, backed by personalized instructor support and official certification, creates a learning experience that adapts to your work schedule rather than competing with it.
Amsterdam Data Academy’s blended learning approach offers exactly this balance. With flexible online coursework, limited class sizes for personalized attention, and interactive sessions scheduled for maximum accessibility, you can develop in-demand data skills without putting your career or personal life on hold.
Ready to explore how data science training can fit into your schedule? The first step is often simpler than you think, and it doesn’t require rearranging your entire life.
Tetiana Levytska
Flexibele financieringsopties
We bieden verschillende manieren om je leerreis toegankelijker te maken:
UWV ondersteuning, gemeente vouchers, 0% rente TechMeUp leningen en flexibele afbetalingsregelingen.
Score
Nu inschrijven
Klaar om over te stappen 1450+ afgestudeerden?
Duur
3 weken
Niveaus
Alle niveaus
Formaat
Blended learning
Volgende groep start
May 28, 2026
Microsoft Excel Essentials course
In deze cursus leer je eerst de basis van Excel, inclusief draaitabellen en essentiële formules zoals VLOOKUP, met veel tijd om te oefenen. Je bouwt geleidelijk aan zelfvertrouwen op door middel van praktische oefeningen en een eindopdracht om alles wat je hebt geleerd in een praktische context toe te passen.
De cursus omvat
- Interactieve live sessies
- Toegang tot leerplatform
- Persoonlijke hulp van je docent
- Toegang tot gemeenschapsgroep
- Officieel gecertificeerd diploma
Wat je leert
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Met vertrouwen door de essentiële functies van Excel navigeren en deze gebruiken
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Spreadsheets maken en opmaken, gegevens efficiënt sorteren en filteren
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Werken met geavanceerde functies zoals draaitabellen en VLOOKUP voor diepere analyses
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Excel-vaardigheden toepassen op zakelijke problemen en besluitvorming op basis van gegevens
Frank Mulders
Nationale golfbond
De docent nam ons mee op een speelse, educatieve en effectieve leerreis
Marjolijn van 't Oost
Reisbureau
We kunnen de training zeker aanbevelen aan anderen
Marcel van Brienen
Marketeer
We zijn meteen aan de slag gegaan met ML-modellen voor onze klanten
David Roose
Gegevensanalist
Duik in datawetenschap
Wilbert Groenewoud
Ingenieursbureau
Deze cursus is perfect toegankelijk, zelfs zonder voorkennis van data
Hanna de Gruijter
Gegevenswetenschapper
Vooral de opdrachten waren erg leuk om aan te werken
Remko de Jongste
Gegevenswetenschapper
Dankzij de cursus werd de promotie snel werkelijkheid voor mij
Pascal Bovy
Freelancer
Een geweldige inleidende cursus om de wereld van data science te verkennen
Navneeta Sahal
Gegevensanalist
