10-day Data Science training
Our 10-day Master of Data Science training course is meant for anyone who wants to implement Data Science into daily practice and boost their career. Data Scientist wasn’t called “the sexiest job of the 21st century” for nothing. There’s a big shortage of Data Scientists as every company is realizing how essential this position is.
Statistics, data blending, and data visualization belong to the core competencies, but good communicative skills, organizing BI & data governance, mounting a business case, genuine KPIs, data quality, privacy, ethics, and consultancy skills are also important.
A fusion of technology and business
The Master of Data Science training will naturally teach you all the essential technical aspects. We’ll cover advanced technology, machine learning, and algorithms. But our Data Science training course also emphasizes the business aspects:
- How do you put Data Science on the map in your organization? How do you make the business case?
- How can you develop an AI-first strategy and ensure that everyone in the organization contributes to it?
- How do you embed AI technology in the right Business Intelligence processes and frameworks?
- What AI tools can you use, and which ones best fit your specific problem?
- Which algorithms and methods for pattern recognition can you use?
- What do you need to know when implementing machine learning?
- How do you successfully implement a Big Data solution and a data lake, and what’s involved?
- What’s the relationship between innovation, new business models, machine learning, and Data Science, and how can you optimize it?
- How should you handle the internal politics and create a base of support and acceptance among users?
This complete, practical Data Science training course contains 24 modules and a challenging final assignment, and comes highly recommended to anyone who wants to become proficient in Data Science and Big Data. It’s especially recommended when you want to become a successful, data-driven organization.
Take the right steps in Data Science
Over the course of 10 intensive days, you’ll be immersed in the field and prepared for a leading role in your organization. Past participants in this Data Science training course proved to be well-equipped to take the right steps in their Data Science track.
Modules Data Science training
This complete Data Science training course consists of 24 compact, practical modules:
✪ KPIs, analytics & machine learning
✪ Data Science maturity and AI-first
✪ Project management & governance
✪ Data visualization & data story telling
✪ BI & Data Science success factors
✪ Introduction to data warehousing & big data
✪ Data warehouse architecture & data lakes
✪ The data warehouse & the ETL processes
✪ Business Intelligence & Data Analytics
✪ Master Data (MDM) & metadata
✪ Data warehouse & data lake administration
✪ Data quality & AI
✪ Continuous improvement of (big) data
✪ AI, big data science & machine learning
✪ The AI & data science business case
✪ AI architecture
✪ Algorithms and machine learning techniques
✪ Data science tools
✪ Introduction to Python, notebooks & R
✪ Developing machine learning models
✪ Privacy, ethics & legislation
✪ Skills and competencies
Face complex data challenges and become an AI-first organization with this training
Data-driven society and algorithmization is a fact of life, and various national and international think tanks all agree that using data correctly can make the difference between growth and stagnation. Our Master of Data Science course is meant for organizations that want to become more intelligent and have to deal with complex data challenges.
Beyond data analytics and machine learning
An organization’s intelligence can’t be described in terms of in data analytics and machine learning alone, it goes far beyond that. In our practical model, you’ll learn where and how Data Science and the intelligent organization overlap, and why an analytical company culture is of great importance to the success of Data Science and artificial intelligence.
10-day program of the Master of Data Science training course
The Master of Data Science training course covers every aspect of Data Science. You’ll learn how to turn data-related issues into profitable results. Besides the technical aspects (like the Internet of Things, data mining, genetic algorithms, Hadoop, etc.), you’ll also learn about all the relevant business aspects. Think about project management, business cases, KPIs, governance, data quality, Data Governance, privacy aspects, and ethics.
The Data Science training course is broken up into three blocks of three days:
- Block 1 (day 1-3): Business Analytics & the intelligent organization; see also our masterclass Business Analytics.
- Block 2 (day 4-6): Data warehousing, data governance & data lakes; see also the training course DWH & Data Governance.
- Block 3 (day 7-9): Big data & Predictive Analytics; see also our Big Data training.
The three blocks are seamlessly connected and have proven their value in practice. Day 10 will contain a discussion of the final assignment, which you’ll work on in groups.
Day 1: Introduction to data science, trends, KPIs & AI-first strategy
During the first day of the Data Science training course, you’ll become acquainted with the power of data-driven organizations and AI. You’ll come to understand the why of big data, KPIs, machine learning, the latest trends in data science, AI, and the advantages of analytics. You’ll learn which steps to take to implement an AI-first strategy in your organization. The following modules will be covered:
MODULE 1: Introduction to Data Science, BI & artificial intelligence
- Definitions and vision: what is Business Analytics? What are we talking about when we talk about data science, artificial intelligence, machine learning, and business intelligence, and how do they relate to each other? Which four essential matters do all organizations need to adapt to? How do you develop a vision on data science and machine learning? How can you make sure that managers and teams in your own organization embrace data-driven working and AI?
- Trends in Data Science and BI: what trends should you be aware of? You’ll learn more about Big Data, data lakes, self-service BI, deep learning, robotics, drones, self-driving cars, photography as the new universal language, cloud solutions, and block chain, and most important when these trends could be relevant in your situation.
MODULE 2: KPIs, analytics & machine learning
- Datafication: The abundance of data poses a large number of challenges. First of all: what can and should you do with it? How can you come up with and implement AI & Big Data applications? The trainer will present a number of interesting examples. What can you learn from case studies like:
- Province South-Holland: algorithms advise bridge operators
- Social Analytics (KLM): webcare, electronic word of mouth (eWoM)
- Predictive policing and the case of Amsterdam’s Fire Department (winner of the Dutch BI & Data Science Award)
- Determining KPIs and million-dollar insights: The four most important methods for finding KPIs will be covered: strategy, process, market, and data-driven. How do you determine the most essential data science content, how do you determine the genuine KPIs, and how can you identify the million-dollar insights? What’s the relationship between KPIs, big data, and machine learning? Under the trainer’s supervision, participants will work on KPIs in groups.
MODULE 3: Data Science maturity and AI-first strategy
- The business case for Data Science and BI: the most important aspects of a business case are covered, and you’ll learn how to make one (based on million-dollar insights, among others). How do you convince the stakeholders and get them involved, while ensuring a budget?
- Develop an AI-first strategy: the majority of organizations remain in the stage of making dashboards and reports and experimenting with algorithms, but neglect to develop and implement an AI-first strategy. You’ll learn the most important aspects of an AI strategy, and under the teacher’s supervision you’ll start developing one for your organization.
Day 2: Project management, data visualization & data science success
This day starts with data science project management and governance and is dedicated to the users of the algorithms, dashboards, and reports. The complete data science process (all 15 steps) will be covered extensively. You’ll learn how to improve the data’s User eXperience using data visualization, among other things. Finally, the 12 key success factors of data science and BI will be discussed. In detail, you’ll learn more about the following:
MODULE 4: Project management & Governance in data science
- Business Intelligence as the foundation: data science without a solid foundation is doomed to fail. The trainer will do a deep dive into the foundation of data science: enterprise business intelligence & analytics.
- Project management & Governance: project management of BI and Data Science. The various forms are all discussed: from waterfall to agile scrum. We’ll also discuss the 10 most important project risks and pitfalls, delivery models for Analytics, and why and how to set up a BICC (Business Intelligence Competency Center). You’ll learn the most important differences between a BI and a data science project.
MODULE 5: Process steps and data visualization
- BI and data science is a process: learn about the 15 steps of processing, analyzing, distributing, and using information effectively to make better decisions. The trainer will also discuss how the data science process diverges from the BI process.
- Data visualization & User eXperience (UX): become acquainted with the most powerful data visualization and story telling techniques, and how they can increase the ease of use of information. You also have to take into account the psychological effects and cognitive frameworks. What does an effective big data visualization look like and which factors should you consider? Includes a discussion of the BBC documentary How to Make Better Decisions. In groups, practice visualizing various effective data visualizations.
MODULE 6: BI & Data Science success factors
Day 3: Introduction to DWH, goals, alternatives & Big Data architecture
Day 3 of the Master of Data Science workshop is completely dedicated to establishing a solid data infrastructure and ETL processes of both the required data warehouse environment and future-proof big data storage methods. A data warehouse can make or break a Data Science project. What should you keep in mind?
MODULE 7: Introduction to data warehousing & big data
How can you set up a scalable central data hub that will ensure the integration of data, reliable information, and one version of the truth? Which other goals can you achieve with a data warehouse and a data lake? Files from various data sources (business processes) and external data will be cleaned and linked so that end users can make easy connections. It’s becoming more and more important to combine these with all the present unstructured data that the organization has access to.
- Knowledge as the end point: how do you turn data into actionable information? What is the importance of adequate information maintenance?
- Goals of a data warehouse: what are the most important goals of a data warehouse, such as history-building and performance improvement? How can a data warehouse contribute to higher data quality, greater recognition, and making information easier to find? Can all this serve as a basis for unlocking and combining all the structured and unstructured data?
- Four characteristics of big data: big data is characterized by the four Vs, but what does that mean for your own project? And for the algorithms you have to use, the solutions, and the required knowledge and skills of your people when it comes to data storage?
- Data lakes, virtualization, and cloud: there are various alternatives to data warehousing these days. We’ll introduce you to data warehouse appliances, in-memory BI, data lakes, data virtualization, and cloud solutions. Which are useful and attainable in your situation? What can you learn from other case studies? What are your own experiences, and what can others learn from it?
MODULE 8: Data warehouse architecture & data lakes
- Data warehouse architecture: what’s the importance of good data warehouse architecture? How does a data warehouse architecture fit within the enterprise architecture of your organization?
- Data modeling: which types of data modeling can you employ? For example: Inmnon, Kimball, and Lindstedt (DataVault). What are the similarities and major differences? The trainer will discuss the pros and cons of the various methods: should you choose one method, or a combination?
- Big Data and data lakes: sometimes, data is too voluminous or unstructured to fit inside a traditional data warehouse. How can you handle this from an architectural standpoint? Can the cloud help, and what are the specific demands when you use AI and big data in your organization?
- Sensor data: when do you apply sensors to gather data and where do you place them? How can you keep costs as low as possible and cause as few disruptions as possible?
- Tooling overview: how do you choose a specific data modeling tool, storage platform, and database software? Which criteria are most important?
MODULE 9: The data warehouse & ETL processes
Filling up the data warehouse consists of three steps: extraction, transformation, and loading (ETL). This can be done using the powerful analytical language SQL or using ETL software and/or data warehouse automation tools. The latter are an essential component of a mature data warehouse architecture.
- Applying data techniques: which data techniques are most suitable at every stage of the ETL process? AI is making big strides in automating this process and improving data quality. How can you support AI in this process and how should the process steps be defined?
- Anonymization and pseudonymization: How can you ensure that you stay within the boundaries of the law when processing, analyzing, and using data when developing algorithms? The trainer will present the two most important methods in this process: anonymization and pseudonymization.
- ETL & DWH automation tools: which data integration tools are available on the market? Do you go with one vendor, or best-of-breed? We’ll introduce you to our 100% independent ETL Tools & Data Integration Survey.
- How to improve data quality: the teacher will provide an overview of the most important methods and tools to improve data quality. Should “bad data” be loaded into the DWH? Or is that a bad idea? What is data profiling, and how can that help?
- ELT or ETL: vendors also talk about ELT. What’s the difference between that and ETL? What impact does bigger and bigger decentralized data collection (ie. open data, Big Data, data lakes) inside and outside of the organization have on your Data Science project?
Day 4: Analytics, Master Data Management, metadata & administration
Day 4 of the Data Science course is dedicated to using the data warehouse in line with the higher goals of BI Analytics, the business intelligence tools and DWH administration, master data, metadata, and open data. Big Data, and especially AI, is taking an increasingly large role in this.
MODULE 10: Business Intelligence & Data Analytics
End users expect a data warehouse to deliver relevant data of excellent quality. They use Business Intelligence software to request data sets, to report on it and analyze it.
- Business Intelligence software: what Business Intelligence software is available, and how does it integrate with your data warehouse, data lake, or AI application? Become acquainted with our 100% vendor-independent BI tool guide.
- The latest trends: what are the latest trends in Business Analytics and Data Science, especially when it comes to Big Data (for example, image processing or text mining)? What does open source mean in this area?
- Practical use: direct access to the data warehouse can be necessary or recommended, but when? Not all users know what they’re doing. The teacher will show you an overview of the various types of users. Which functional needs to they have, and how can you play into this? What role does self-service BI play in this?
MODULE 11: Master Data Management (MDM) and metadata
Aside from clean data, a sound metadata and master data structure is extremely important for further growth in Data Science. Important data groups such as customers, products, locations, and employees have to be well-maintained. Insight into their history and the accompanying business rules are essential in this. The volume of available data is increasing exponentially and AI is becoming increasingly important in this domain.
- Definitions and master processes: what do we really mean by master data and metadata? How will it help you produce and consume reliable, high-quality information (one version of the truth)? Which processes play a role in this?
- Smart learning loops: is master data and metadata management purely analytical, or can it also impact operational systems? Become acquainted with the various maturity levels of metadata and learn how to design a smart learning loop using an enterprise portal and mobile access.
MODULE 12: Managing the data warehouse and data lakes and achieving success
Data warehouses have to be maintained and managed. During this portion of the course, you’ll be introduced to the most important technical and functional management processes, patterns, and competencies. The rise of AI applications within this domain requires new competencies.
- Management processes: which technical and functional management processes can be distinguished in data warehousing? Which tools can be used for this?
- Business & IT: what role does the BICC play in these processes, and what are its pros and cons? What are the roles of the business and IT?
- Project approach: what’s the right project approach, keeping in mind the success and risk factors? How does the use of algorithms change the approach? What are the most important success factors for a well-functioning data warehouse?
- Support and maintenance: how do you develop a data warehouse and data lake, keeping in mind support and maintenance? AI requires a different kind of support and maintenance. How can you support AI effectively?
- Competencies and skills: how is DWH maintenance different (or the same) from “traditional” maintenance? Which competencies and skills are required for successful DWH and data lake maintenance? Especially given the growing importance of algorithms in this domain.
Day 5: Data governance, data quality, and continuous improvement
Data governance is essential to organizations that want to work data-driven. The total life cycle of data – from inception to deletion – has to be implemented as a learning process and feedback loop. During day 5 of our Data Science training course you’ll learn how to set up a robust Data Governance structure.
MODULE 13: Data governance
The value of Data Governance: how can your organization extract more value thanks to Data Governance and use it to achieve an advantage? Do you have to completely change the Data Governance structure when working with big data and AI?
- The Data Governance framework: the teacher will cover every aspect of Data Governance. Consider the various roles like data stewards and data custodians, but also consider the integrity of the data, its quality, and making (big) data, metadata, and master data easily accessible. Learn how to use a Data Governance framework in your own organization. What should you pay attention to in the context of AI becoming an important pillar of the organization’s business?
- From Governance to data perfection: learn about the ideal route to data perfection, naturally based on a thorough cost/benefit analysis. What steps should you take, who do you involve, and how do you lift your own organization to a high level of data maturity? What are the possibilities of AI in this process?
MODULE 14: Data quality
No one will call the importance of data quality into question (openly), but how do you go about it? How do you implement solutions and achieve success? Learn about everything involved in taking your data quality to a higher level.
- Data quality: data can’t be half-right, it’s either right or wrong.
- Key terms: what are the key terms around data quality? For example: completeness, correctness, integrity, metadata, linked data, aggregated data, big data, etc. What should you pay attention to in your situation?
- Unstructured data: learn how to improve the data quality of unstructured (sensor) data. What can you learn from success stories? How do you ensure the quality is good enough to use in AI applications?
- Solutions for data quality: what solutions are on the market to monitor data quality, analyze it, clean up the data, mark it, or reject it? Are data quality systems with AI the golden grail or do they cause more problems than they solve?
- Success factors: what pitfalls and success factors play a role? What are your own experiences, and what can you learn from them?
MODULE 15: Continuous improvement of (big) data
During this part of the training, the process of arriving at structurally higher data quality in your organization will being to take shape. Learn how to set up a continuous improvement process concerning data quality. What are the best practices to secure data results, so that you don’t end up with the same junk data time and again? Can AI completely take over this process of continuously improving data?
- Improvement cycles: the teacher will give you practical insight into how to improve data quality by using the PDCA cycle by Dr. Deming, among other ways.
- Awareness and culture: how do you create awareness among management? How do you convince employees of the importance of data quality? What does it get them? Which cultural aspects play a role, and which skills are required? What kind of culture do you need to foster when algorithms start playing a bigger role?
- Behavior and support: how do you handle certain employee behavior, and how can you create a wide base of support?
- Securing improvements: learn how to complete the PDCA cycle every time (daily, weekly) and secure the results.
Day 6: Developing aspects of AI, machine learning & AI architecture
During day 6 of the Data Science masterclass, you’ll be introduced to the analytical and predictive models that form the foundation of AI and machine learning and the requisite architecture.
MODULE 16: AI, big data, data science & machine learning
During this module you’ll learn what, exactly, AI means, and of which parts it consists. You’ll also be introduced to the two most important aspects of AI and data science: machine learning and statistics.
- In-depth look at AI and concepts: what is AI and how does it relate to other common concepts like big data, predictive analytics, data science, text mining, and data mining? What is machine learning and what is its place in the data science process? How does AI relate to BI?
- Data Science process: what does a data scientist do, and what steps comprise the data science process? What role does machine learning play in this and how can you implement the steps in your organization?
- Statistical knowledge: Applying the right statistics is crucial. What statistical knowledge do you need in your data science project?
MODULE 17: The business case for AI & Data science
There’s a lot of money involved in AI-related initiatives. The total market worth is estimated at 1,000 trillion Euros annually worldwide. But the returns can be huge, as Netflix’s past success with shows like House of Cards shows. The trainer will discuss many best practices you can learn from.
- Thinking holistically: what is the relationship between algorithms, big data, innovation, and process improvement? What are the essential components of a big data business case? How do you approach experiments and sandboxes outside of your field of view or responsibility?
- Convincing management: how do you lead management by the hand and convince them of the use and necessity of analytical models and AI? What kind of leadership do you need in your organization?
- Steps and pitfalls: what steps should you take in your Big Data project, and which pitfalls and risks should you avoid?
MODULE 18: AI architecture
How does AI architecture relate to classic BI and DWH architecture? How can you make the AI tools match the existing architecture and infrastructure? Subjects discussed include:
- Data science technology: what are the most important tools used, and what impact do they have on your (existing) architecture? How do you approach the most common dilemmas in this area and during the AI transition?
- Data science reference architecture: the trainer presents a data science reference architecture and shows you its most crucial principles.
- New developments: consider the cloud, Docker containers, automated machine learning, sensors, specialized hardware (such as GPUs), SPARK, Hadoop, and REST APIs.
Day 7: Machine learning algorithms: theory and practice
During the 7th day of the Data Science training course, you’ll be introduced to the theory, the techniques, and practice. Under the trainer’s supervision you’ll start working with Python and R.
MODULE 19: the algorithms and machine learning techniques
First, the trainer will delve into the various types of algorithms. He will present the application, how it works, and the pros and cons. Subjects discussed include:
- Meaning and use of algorithms: what is an algorithm, and other related terminology? How do data scientists use algorithms?
- Supervised, unsupervised learning and reinforcement learning: during this part of the training you’ll learn the meaning of supervised learning, unsupervised learning, and reinforcement learning, and which type to use when. The trainer will provide insight into the various types of algorithms such as classification, regression, clustering, decision trees, KNN, ensemble models, and neural networks.
- What is deep learning: gain insight into how algorithms can apply deep learning.
- Validating algorithms: how do you measure the performance of a used algorithm, and how do you avoid the most important pitfalls, such as overfitting and underfitting.
MODULE 20: Data science tools
In this module you’ll start working with various data science tools used by data scientists. The trainer will start with an overview of the most commonly-used tools.
- Open source data science tools: what is it and why are most data science tools open source? You will be given an overview of the most commonly-used (open source) tools, such as Python, R, SCALA, SQL, sklearn, pandas, and numpy.
- Commercial data science tools: which commercial data science tools are commonly used, and where do they fit in the data science process? A number of commercial data science tools, such as Rapidminer, SAS, Dataiku, and IBM Watson Studio will be covered.
MODULE 21: Data science tools in practice, introduction to Python & R
During this workshop you will be introduced to commonly-used programming languages like Python and R, but also the various interfaces data scientists work with. During the workshops, the emphasis will be on working with Python, because it’s the most popular tool among data scientists. But you’ll also meet the language R.
- Introduction: how should you position Pyton and R, where do these languages come from, and how can you use them? How can you interface with the cloud or other platforms, or will you use a standalone tool? Which ones are available?
- Notebooks: what is a notebook and how do they work?
- Python basics: what are the basic elements needed to work with Python? Consider the use of variables, lists, functions, and help. Packages are a kind of plug in that you can load into Python (or R). What are the common packages, and how do you load them into Python?
Day 8: Data science in practice
During day 8 of this data science training course, the trainer will continue where he left off the last day. In a number of workshops, you will employ machine learning and text mining to develop your own models, train them, test them, and validate them.
MODULE 22: workshop developing machine learning models
Based on various data sets, you will walk through all the steps of the data science process. You will do that with Python, R, and a visual modeling environment. Each part will consist of the following steps:
- Importing data
- Exploratory Data Analysis (EDA)
- Data wrangling. Cleaning and editing data
- Splitting data into training and testing data sets
- Training machine learning model using multiple algorithms
- Validation machine learning model. How does the machine learning model perform?
- Visualization. How does a data scientist visualize the data?
You will also participate in a text analytics workshop where you perform a sentiment analysis based on twitter data.
Day 9: Ethics, privacy, skills, and competencies
During the 9th day of the data science workshop, we’ll cover the most important ethics principles and privacy legislation. You’ll also learn which skills and competencies you need to make data science into a success.
MODULE 23: Privacy, ethics, and the legal frameworks of data science
The abundance of available data poses a huge amount of challenges. Firstly: what can and should you do with it? How can you develop Big Data applications and implement them?
- Frameworks: which (legal) frameworks are available to test if your data plans are ethically viable?
- Laws and regulations: what do relevant laws and regulations have to say about using data that’s tied to personal identity? What can you do to keep public opinion on your side and avoid reputation damage?
- Techniques and privacy solutions: which techniques and solutions are available to use and analyze personal data while respecting privacy? Consider things like data masking combined with data aggregation, or asking permission of the people in question.
MODULE 24: Skills and competencies for Data Science and AI
Data Science has become a professional field. New technologies and knowledge are entering the field at a rapid pace. How do you keep up? How do you lift your team to a higher level? Statistics, data blending, analytics, and data visualization belong to the core competencies of a good data scientist, but experience developing machine learning models and programming is always a bonus. Good communicative and consultancy skills, knowledge of BI, privacy, and data governance also belong to the standard skills of a professional data scientist.
- Three base skills: Which three base skills does every data scientist absolutely need, and how do you develop them? How do you handle the shortage of Data Scientists?
- Data Science team: the most important Data Science skills – soft & hard skills – are discussed. How do you put together a Data Science team that prepares your organization for a new phase of development? Which roles and competencies should you consider?
Day 10: final assignment and certification
During the week, you’ll work on a challenging assignment in groups. The test is designed for you to apply the theory in practice.
The assignment will be handed in a week before the final day. We’ll discuss the results of the assignments in detail.
The teachers of the Data Science training course
This Master of Data Science training course is taught by Bert van der Zee, Dick Pouw MBA, and Daan van Beek MSc. Together, they have over 70 years of experience in the field, and they teach at various universities and colleges, including TIAS, the School for Business & Society, as well as teaching masterclasses all over the world.
Striving for data perfection: user acceptance
The example of the Amsterdam Police Department perfectly illustrates how Data Science doesn’t stop at the technology. The PD’s first Data Science project plotted patrol routes for officers where the odds of catching criminals red-handed was the highest. Although the officers seemed enthusiastic about the solution, they didn’t initially accept it. They kept patrolling the way they always did. Striving for data perfection without considering user acceptance is like buying a 24-carat diamond ring that no one wears.
Interactive Data Science & AI training course
The Passionned Academy training is highly rated, thanks in no small part to its interactive nature. Every day offers plenty of room for participants to exchange experiences and cases. The practical assignment creates an ideal combination of practice and theory.
After completing the Master of Data Science training course, you’ll receive four digital badges for LinkedIn, a Passionned Academy certificate, and a signed copy of the data science book Data Science for Decision-Makers.
“Very good! Easy to understand and we use the examples from the course to apply to our business. I liked all parts of the training especially the examples.”
BW Maritime Pte Ltd
The Master of Data Science course is meant for anyone who wants to make their organization more intelligent and data-driven. The training is predominantly designed for (up-and-coming) Data Science managers, Business Intelligence Managers, Chief Information Officers, business managers, program managers, Chief Data Officers, controllers, business analysts, BI consultants, information managers, Data Scientists, and data analysts.
The price includes arrangement fees, excluding VAT, and includes a copy of the book “We are Big Data” by Sander Klous and the Data Science book Data Science for Decision-Makers by Daan van Beek.
Apply for in-company training
Our courses are taught in-company at a location of your choosing. Contact us now to discuss rates, dates, and possibilities. The courses can be custom-tailored to your needs. We hope to hear from you!