Big Data Analytics Training

During the first day of this Big Data training, you will learn exactly what Big Data is and more importantly what it is not. You will be introduced to the characteristics of Big Data, Artificial Intelligence, data science, Business Intelligence, and machine learning. Specifically, you will learn how to determine and capitalize on the value that Big Data has for your organization.

• Big Data, hypes, and the data-driven organization: what’s hype, what’ s the reality and what are examples of successful implementations? What are the characteristics of a data-driven organization and what place does Big Data Science & AI have in it?
• Definitions of Big Data & AI: what are the four characteristics of Big Data? And what does that insight mean for your project? And for the tools and people to be deployed? What are the definitions of machine learning, data science, and AI, and how do they differ from regular Business Intelligence?
• Positioning & knowledge: how can you best position Big Data Analytics & Data Science within your organization? What statistical knowledge do you need for your project? After all, applying the right statistics is crucial.
• Key Big Data & AI trends: the instructor will present the key trends for Big Data & artificial intelligence and explore them, case by case.

Develop a Big Data & AI-first strategy

The majority of organizations get stuck in creating reports and dashboards and experimenting with machine learning, but forget to devise and implement an AI-first strategy. In this section, you will learn the key aspects of a Big Data & AI strategy.

The business case of Big Data, AI, machine learning, and project management

There is a lot of money involved in big data-related initiatives such as AI, machine learning, big data tools, and big data analytics. The total global market for big data analytics is estimated by Statista to exceed $650 billion by 2029. But the revenues can also be huge, as evidenced by the success of House of Cards (Netflix), for example. The lecturer also presents many other best practices you can learn from.

• Integral thinking & business cases: what is the relationship between Big Data, Analytics and innovation, and process improvement? What components should a business case consist of? How do you deal with experiments and living labs?
• The case for the necessity of these tools: how do you convince the board, management and other stakeholders of the usefulness and necessity of Big Data and AI? What kind of leadership do you need to do this in your organization?
• Steps and pitfalls: how have other organizations tackled these types of projects and what can be learned from them? What steps should you take in your Big Data project and what risks and pitfalls should you recognize when developing and putting algorithms into production?

How do you build a clear Big Data architecture?

Big Data involves large amounts of (unstructured) data that no longer fit into a data warehouse. In this Big Data course, you will learn how to set up an architecture in a sustainable way and make it connect to existing architectures. How does Big Data architecture relate to classic BI and DWH architecture? How do you make AI tools connect to your existing architecture and infrastructure?

• Big Data architecture: what is the impact of Big Data on IT and BI architecture? And how do you make Big Data flows connect to existing architectures? How to deal with an already existing data warehouse?
• Reference architecture for Data Science: the instructor will show a reference architecture for Data Science & Big Data and take you through the most important principles it contains.
• New developments & tools: what devices, tools and platforms are available to generate, store, and analyze Big Data? Think Hadoop, NoSQL, (hybrid) cloud, Docker containers, automated machine learning, specialized hardware (such as GPUs), SPARK, Hadoop, and REST APIs.

The Big Data process, algorithms, tools, and techniques must fit seamlessly with the specific organizational issue. But how do you make the right choice? This is critical. During the second day of this Big Data training, you will also dive into the technology without getting too technical. We will review various statistical models, algorithms and machine learning techniques such as image processing, neural networks, and text mining. But it all starts with the process.

Big Data process

What does a data scientist do and what steps make up the big data science process? What role does machine learning play in it and how can you implement these steps in your organization?

• From data to prediction: what are the steps of the big data process and what do machine learning and AI contribute to it?
• Data science team: who plays what role in the data science process, in setting up big data lakes, and what competencies are needed?
• Embedding in the organization: where do you embed the big data analytics & data science function in the organization?

What big data algorithms can you use?

Algorithms are essential building blocks for big data and machine learning. What are algorithms and what types are there and what can you do with them?

• Understanding Big Data algorithms: what is a big data algorithm and what do you need to create one?
• The different types of algorithms: what type of algorithms are available and which best fits your specific issue? The instructor will give you insight into the different types of algorithms such as classification, regression, clustering, decision trees, neural networks, et cetera.
• Supervised learning, unsupervised learning, and reinforcement learning: during this section, you are going to understand these AI concepts and learn how best to apply them in which situation.
• What is deep learning: what is deep learning, in what ways can you apply it, and how do you build a deep learning model?
• How to validate algorithms: how to measure the performance of a machine learning model, how to prevent it from becoming unbalanced (model drifting), and how to keep control over overfitting and underfitting.

Big Data tools and techniques

In this module of the Big Data training you will get to work with several big data tools used in practice by data scientists. The instructor will provide an overview of the tools that are commonly used:

• Open-source Big Data tools: what tools are available in the open-source domain? Here the instructor will give you an overview of commonly used open-source solutions such as Python, R, Julia, Scikit-learn, Pandas, and SCALA, for example.
• Commercial tools: the instructor gives an overview of the most commonly used commercial tools. Think Rapidminer, SAS, IBM, Cloud Pak for Data, IBM SPSS, Modeler, Dataiku, et cetera.
• Tool selection and procurement: what should you look for when purchasing such tools? Can they all really handle large amounts of big data or (un)structured data such as photos, videos and emails? Or do some vendors promise too much?
• Integration: how can you “tie together” the various big data & AI tools so that end users can effortlessly use the outcomes of algorithms or activate Auto Machine learning themselves?

The last day of our Big Data training is dedicated to successful Big Data applications, a machine learning workshop, the skills and competencies of Big Data Scientists, and finally the ethical and legal side of Big Data & the use of algorithms.

Big Data applications

The abundance of data presents a number of complex challenges. What can and should you do with it? How do you avoid the information overload that always lurks? How can you devise and implement the right, most profitable, and most practical applications? You’ll learn all this and more about Big Data applications during our practical Big Data analytics training.

• Best practices: what can be learned from appealing cases such as: the City of Dublin, the Province of South Holland, Social Analytics at KLM, and the Police and Fire Department A’dam-Amstelland (all customer cases can be explored on the Dutch website).
• Success factors: what factors played a role in success or failure? What are your own experiences with Big Data & AI and what can be learned from them?

Workshop Big Data & machine learning model development

In this Big Data workshop, you will learn how to build a (simple) machine learning model. You will work on importing data, Exploratory Data Analysis (EDA), data cleaning and editing, building a training and test dataset, training the algorithm, and visualizing and validating the Big Data model.

Skills, knowledge, and competencies

Data Science is becoming a professional field and new knowledge is also rapidly becoming available in terms of skills and competencies. How do you keep track of that and keep a grip on it? And how do you take your Big Data or data science team to the next level?

• Personal skills: what are the three most commonly mentioned skills of a data scientist and how do you validate someone’s skills? How to deal with the shortage of big data scientists?
• Team skills: what skills (soft & hard skills) are needed in a successful big data analytics team? How do you continue to build a successful team that prepares your organization for a new phase of development? What Big Data roles and competencies can you then identify in the process?
• Management of models: you take effective machine learning models into production, of course, but you also have to manage them. How will you organize their management and how will you embed the tasks in the organization?

Ethics, privacy, and laws and regulations

Collecting and linking (large amounts of) data automatically raises important ethical and legal issues. Practicing Big Data Science without addressing ethics and privacy is out of the question these days. The instructor will take you through the most commonly used frameworks, the applicable laws and regulations, and finally present a number of techniques to thoroughly protect the privacy of individuals without having major consequences for the business case.

• Frameworks: which ethical frameworks and principles are important to create a climate in which people make the right decisions; which data are you allowed to use and which are you not allowed to use?
• Laws and regulations: what do the relevant laws and regulations, such as the AVG and the upcoming European AI Act, say about this? How to deal with them? How do you respond to public opinion and prevent possible damage to your image?
• Techniques and privacy solutions: what creative methods and techniques are available to still have access to the data without violating privacy? These include anonymizing and pseudonymizing personal data.