3-day Big Data Analytics Training

At the beginning of the first day, we’ll discuss the conceptual framework of Big Data and AI. What’s hype and what’s reality? What are examples of successful implementations? We’ll explore the characteristics of a data-driven organization and the role Big Data and AI play within it. What are the four defining characteristics of Big Data, and what do they mean for your project? And how do they impact the tools and people you’ll need to involve? You’ll also get clear definitions of machine learning, data science, and AI – and learn how they differ from traditional Business Intelligence.

We’ll then explore how to best position Big Data Analytics and Data Science within your organization. What level of statistical knowledge is needed for your project? Applying the right techniques is essential. The instructor will also highlight key trends in Big Data and AI, such as quantum computing, generative AI, ChatGPT, deep learning, and Auto Machine Learning (AutoML). The use of tools like ChatGPT has especially surged in recent times. All of these trends will be interpreted and put into context by the instructor.

The majority of organizations get stuck at the stage of creating reports, dashboards, and experimenting with machine learning – often overlooking the need to design and implement a truly AI-first strategy. In this section, you’ll learn the essential elements of an effective Big Data and AI strategy.

Big Data initiatives – such as AI, machine learning, Big Data tools, and analytics – involve significant financial investments. According to Statista, the global market is expected to exceed $650 billion by 2029. However, the potential returns are also substantial, as demonstrated by success stories like House of Cards on Netflix. The instructor will also share several other best practices that offer valuable insights.

What is the link between Big Data, analytics, innovation, and process improvement? What should a strong business case include? How should you approach experiments and living labs? How do you gain buy-in from the board, management, and other stakeholders for Big Data and AI initiatives? What kind of leadership is needed within your organization? You’ll also explore how other companies have tackled these challenges – and what you can learn from them. Finally, we’ll cover the key steps for running a Big Data project successfully, along with the common risks and pitfalls when developing and deploying algorithms in production.

Big Data involves large volumes of (often unstructured) data that no longer fit within a traditional data warehouse. In this Big Data course, you’ll learn how to design a sustainable architecture that integrates with your existing systems. How does a Big Data architecture relate to classic BI and data warehouse (DWH) architecture? And how can you connect AI tools to your current infrastructure?

Key questions include: what is the impact of Big Data on your IT and BI architecture? And how can you link Big Data streams to your existing systems? How should you handle your current data warehouse?

The instructor will present a reference architecture for Data Science and Big Data, guiding you through the core principles it’s built on. You’ll also explore the latest tools and developments, such as Hadoop, NoSQL, (hybrid) cloud, Docker containers, automated machine learning, specialized hardware like GPUs, SPARK, and REST APIs.

What does a data scientist or Big Data engineer actually do, and what are the key steps in the Big Data process? What role does machine learning play, and how can you implement each step within your organization? Who is responsible for what in the data science process, in building data lakes, and what skills are required? Where should the Big Data Analytics and Data Science function be positioned within your organization? And how do you build a high-performing team that can lead your organization into the next phase of data-driven transformation? You’ll also explore the different Big Data roles and competencies involved in this process.

Big Data and AI have evolved into professional disciplines, and the required skills and knowledge are advancing quickly. How do you stay up to date and maintain control? How can you continuously elevate the performance of your Big Data team? This includes developing personal skills, strengthening team capabilities, and managing models effectively. You’ll learn how to structure this management and where to place these responsibilities within your organization.

Algorithms are the essential building blocks of Big Data and machine learning. But what exactly are algorithms, what types exist, and what can you do with them? Which type best fits your specific problem? The instructor will guide you through the main categories of algorithms – such as classification, regression, clustering, decision trees, neural networks, and more – and explain when and how to use them.

In this section, you’ll also explore key AI concepts like supervised learning, unsupervised learning, and reinforcement learning, including how to apply each method effectively. You’ll learn what deep learning is, how it can be used in practice, how to build a deep learning model, and what that process entails. The course also covers how to validate algorithms and assess machine learning model performance. You’ll gain insights into avoiding issues like model drift and learn how to manage overfitting and underfitting.

In this module of the Big Data course, you’ll work with a variety of Big Data tools that data scientists use in real-world projects. The instructor will provide an overview of the most commonly used (open source) tools. What tools are available in the open-source ecosystem? You’ll get an introduction to popular tools such as Python, R, Julia, Scikit-learn, Pandas, and SCALA. The instructor will also introduce you to widely used commercial tools like RapidMiner, SAS, IBM Cloud Pak for Data, IBM SPSS Modeler, Dataiku, and more.

When it comes to selecting and purchasing tools, what should you look out for? Can all these tools truly handle large volumes of Big Data or unstructured data like images, videos, and emails? Or do some vendors overpromise? You’ll also learn how to integrate various Big Data and AI tools so that end users can easily interpret the results of algorithms or trigger Auto Machine Learning processes themselves.

The abundance of data brings with it a number of complex challenges. What should you do with it – and what can you do? How do you avoid the constant risk of information overload? And how do you identify and implement the most valuable, practical, and profitable applications? You’ll explore all of this and more in our hands-on Big Data analytics training.

We’ll look at real-world examples such as the City of Dublin, the Province of South Holland, Social Analytics at KLM, and the Police and Fire Department of Amsterdam-Amstelland – winner of the Dutch BI & Data Science Award. What were the key success factors in these cases – and what led to failure? What are your own experiences with Big Data and AI, and what lessons can be drawn from them?

In this Big Data workshop, you’ll learn how to design and build a basic machine learning model. You’ll work through the full process: importing data, conducting Exploratory Data Analysis (EDA), cleaning and preparing the data, creating training and test datasets, training the algorithm, and finally, visualizing and validating the Big Data model.

Collecting and linking Big Data automatically raises important ethical and legal questions. Practicing Big Data without addressing ethics and privacy is no longer an option. The instructor will guide you through the most widely used frameworks, relevant laws and regulations, and introduce techniques for protecting individual privacy – without significantly impacting the business case.

Together with the instructor and fellow participants, you’ll explore key questions such as: Which ethical frameworks and principles help foster responsible decision-making? Which types of data can be used, and which are strictly off-limits? What do laws like the GDPR and the upcoming AI Act say, and how should you comply with them? How can you anticipate public concerns and avoid reputational damage? You’ll also learn about creative methods for collecting data ethically, including techniques like anonymization and pseudonymization of personal information.