Big Data Analytics Training

Photo Jack Esselink MA
Author: Jack Esselink MA
Big Data & AI trainer
Table of Contents
9 / 10

In our unique in-company Big Data training, you’ll learn that Big Data Analytics and algorithms are inextricably linked to process improvement, organizational renewal, and innovation. As a consultant, manager, Big Data architect, or project leader, you want to understand how that works. In this 3-day Big Data course, you will learn the fundamentals of (Big) Data Science & AI so that you will not only become a better interlocutor but also be able to separate sense from nonsense. You will learn what opportunities there are for your organization, how to get returns from Big Data analytics and how to effectively manage these types of projects. During this interactive Big Data course, you will learn how to process large amounts of (un)structured data into new insights, process improvements, and use a radically different business model. For more information contact us.

Big Data Analytics course: the questions & answers

You want to make your organization data-driven with Big Data Analytics. How do you do that? In the process, you’ll encounter data lakes, data science, data management, machine learning, artificial intelligence (AI), and algorithms. That one simple question raises new questions for you, such as:

  • How do you convince your colleagues of the usefulness and necessity of big data analytics?
  • What does the conceptual framework of Big Data, Data Science, and AI look like?
  • How can you effectively establish a business case for Big Data & AI?
  • What exactly does a Big Data project look like and what are the main pitfalls and risks?
  • What algorithms and techniques are at your disposal and how do you select the right ones?
  • What do you need to know about statistics in the field of Big data?
  • How do you use text mining for learning purposes?
  • What tools and platforms (Hadoop, R, SPSS, Spark, Python, BI tools) are available?
  • How do you deal with Big Data, ethics, and all the legislation around privacy?
  • What place do these issues take in the architecture and how do you keep a grip on it all?

But more importantly: how do you make your Big Data project a success and how will you embed it in your organization? And what hard and soft skills do you need in your team? And how do you deploy the Internet of Things (IoT) and AI for innovation and new business models? If you or your team are struggling with one or more of the above questions, then our in-company Big Data training is strongly recommended.

Mirror the best practices from the Big Data course

In this Big Data course, we don’t just teach you the theory. Practice is also amply covered during the training. You will be introduced to numerous recent best practices. Examples of the various vendor solutions will also be discussed, such as R, Dataiku, IBM, and Python. In addition, the instructor will elaborate on various big data algorithms such as decision trees, cluster analysis, and neural networks.

Achieving success with Big Data and analytics

In three intensive days, you will be prepared to start working with Big Data, machine learning, & algorithms directly within your organization. Once you have completed our Big Data training, you will be in an excellent position to start a big data project and then achieve success step by step with machine learning, AI, and Big Data analytics.

Big Data training & applications

Big Data trainingBig Data and Data Science are very different from regular Business Intelligence tracks where reports, KPI management, and dashboarding are central. The size and complexity of the data also differ greatly. The specialized field of Big Data Analytics therefore requires entirely different skills and tools. But the higher goal of this Big Data Analytics training always remains the same: to work on the intelligence of your organization and make it data-driven.

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Guard against disappointing results

Without the use of the right skills and tools, big data and machine learning often produce results that make no sense at all. Failure is always lurking. Only a small percentage of Big Data initiatives make it to the finish line. Our practical model covering all Big Data tools and methods creates the right conditions and points the right route to even better results. This practical Big Data course will save you from disappointing results. Contact us to know more.

Content of the 3-day Big Data training

During this Big Data training you will learn in three days how to manage a Big Data & AI project from A to Z. Not only will you learn what you need to know about the Internet of Things (IoT), data lakes, and data architectures, you’re also going to learn how to cash in on a business case, and put together and implement an AI-first strategy, in addition to how to deal with privacy issues, regulations, and ethics.

Day 1: Introduction, AI-first strategy, business case, projects & architecture

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 such as quantum computing, generative AI, ChatGPT, deep learning, and Auto Machine Learning (Auto ML). ChatGPT in particular has recently taken off, the lecturer will explore all the trends mentioned, 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.

DAY 2: Artificial Intelligence & Big Data process, algorithms, tools and techniques

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?

DAY 3: Building Big Data applications, workshop, skills, ethics, privacy and legislation

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.

Meet the success factors of this interactive Big Data course

During each training day of this Big Data course, there is ample opportunity to exchange experiences. You’ll participate in discussions and work in groups on concrete assignments. This creates a perfect mix between practice and theoretical frameworks and models. After completing this unique Big Data training course, participants will receive a certificate from Passionned Academy, a digital badge and a copy of our Data Science book.

Additional information on the Big Data Analytics training

This Big Data Analytics training is done in-company. Some of its features are listed below:

high education level and thinking level
VAT exempt
no study load
interactive & practical
certified digital certificate
from 9:00 to 17:00

This course is also offered in Dutch and it is part of our 10-day Data Science training.

Become a Big Data professional

Target group for the Big Data Analytics training

The Big Data Analytics training is intended for anyone interested in the possibilities and impossibilities of Big Data & AI for their own organization. This unique Big Data training is particularly attended by prospective project managers, BI managers, IT managers, marketing managers, data scientists, big data analysts, data engineers, consultants, program managers, BI consultants, data analysts, business analysts, and controllers.

Learning objectives achieved at the end

  • You will know how to determine and monetize the value of big data for your business
  • You will recognize the different types of algorithms and know how they work
  • You will know the distinction between unsupervised, supervised and reinforcement learning
  • You will have practiced creating a predictive model in Python
  • You will learn the basics of machine learning and deep learning
  • You will be able to validate a Big Data model to acquire a reliable analysis
  • You will recognize the ethical, legal and privacy issues surrounding Big Data
  • In short: you will know how to manage a Big Data project from A to Z

Join our unique Big Data course

Through our contact form you can directly request a proposal for an in-company training course. Contact us if you have any questions about the training.

About the lecturer

Jack EsselinkEvery organization collects mountains of data and is eagerly looking to do smarter things with it. Jack Esselink is a very experienced lecturer who speaks, teaches and advises on the subject of Big Data & AI with great enthusiasm and passion. He introduced this Big Data Analytics training on the market a number of years ago and is continuously refining it.

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Reviews about Big Data Analytics Training

Number of reviews: 4Average rating: 9 / 10
8 / 10
Marco Jongen | Vebego B.V. | December 22, 2023

The course provides good insight into the content and potential applications of big data analytics. In addition, you will gain an initial insight into how data models are created by getting started with programming yourself, but in a way that is extensively prescribed and explained. What I found particularly valuable is that now I understand better what big data analytics is. This allows me to translate the problems in my organization and think about how big data analytics can contribute to improvement.

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10 / 10
Zinabu Melese | In3 | October 2, 2023

The entire organization of the program is good and informative for people with intermediate to advanced knowledge.

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10 / 10
Vincent Recappé | Wigo4it | September 13, 2023

You'll get a good idea of what a Data Scientist does that will help you better enter the conversation.

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8 / 10
Bram Schreuder | In3 | August 21, 2023

This training was good in outline terms regarding big data. The Python part was less interesting to me.

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