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 about all the important technical aspects. We’ll cover advanced technology, machine learning, and algorithms. But our Data Science training course emphasizes the business side:
- How do you put Data Science on the map in your organization? How do you make the business case?
- How do you embed it in the right Business Intelligence processes and frameworks?
- What tools can you use, and which ones best fit your specific problem?
- Which algorithms and methods for pattern recognition can you use?
- How do you successfully implement a Big Data solution, and what’s involved?
- What’s the relationship between innovation and Data Science?
- How do you handle the internal politics and create a base of support and acceptance among users?
This complete, practical Data Science training course contains 27 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.
Complex data challenges
Data-driven society is a fact, 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 captured 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.
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: The trends, project management & the intelligent organization
You’ll also learn which steps you should take to successfully complete a Data Science project, and the role of the BICC. The following modules will be discussed:
MODULE 1: Introduction to BI & Data Science
- Definitions and vision: what is Business Analytics? What do we mean by “management information”? Which four essential matters does an intelligent organization have under control? How do you develop a vision on Business Analytics and Data Science? And how do you ensure that managers and teams in your organization actually use actionable data to make better decisions?
- Trends in Data Science and BI: what trends should you be aware of? You’ll learn more about Big Data, self-service BI, cloud BI, Social Analytics, mobile BI, and Data Discovery, and especially when they’re relevant to your situation.
MODULE 2: Insight into data warehousing & data governance
- Insight into data warehousing: which purposes does a data warehouse serve? Which implementation aspects are important to data warehousing? We’ll also discuss the essentials of meta data, modeling, data integration, and architecture. See also day 4 & 5.
- Insight into data governance: everyone recognizes that data quality is important, but how do you take steps towards improving that? What aspects, tools, and methods are available to improve data quality in a continuous process? See also day 6.
MODULE 3: Project management & BI Governance
- BI 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).
Day 2: BI strategy, skills, and maturing
MODULE 4: Business cases & maturity
- 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?
- Maturity in BI & Analytics: Most organizations are stuck in reporting, the lowest level of maturity. You’ll meet the various maturity and ambition levels of BI & Analytics. Most importantly, how can you assess where your organization is at? How can you chart a course towards a higher level of maturity?
MODULE 5: Business Intelligence Strategy
- Workshop strategy approach: under the teacher’s guidance, in groups, develop a vision on Business Analytics and Data Science. Formulate a BI mission and strategy for your own organization.
MODULE 6: Consultancy skills for BI
- BI consultancy skills: What role should a professional BI consultant play, and what is their workplace? Which dilemmas are there in this field, and which skills are essential for BI consultants? You’ll learn how you can let your teams develop from report builders to internal consultants.
Day 3: KPIs, User eXperience (UX), sata visualization & BI success
You’ll also learn how you can improve the User eXperience of data by using effective data visualization, among other things. You’ll learn to understand and apply Business Intelligence frameworks. Finally, the 12 most critical success factors of Business Analytics will be discussed.
MODULE 7: Information analysis & KPIs
- Determining KPIs and million-dollar insights: the four most important methods for information analysis will be discussed: strategy-driven, process-driven, market-driven, and data-driven. How do you determine the most essential BI content, how do you determine what genuine KPIs are, and how do you identify the million-dollar insights? Guided by the teacher, participants will work on this in groups.
MODULE 8: Process steps and data visualization
- Business Intelligence is a process: you’ll be introduced, in detail, to the 15 steps for processing, analyzing, and distributing information, and especially using it effectively to make better decisions.
- User eXperience (UX): become acquainted with the most powerful data visualization 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. Includes the BBC documentary How to Make Better Decisions.
MODULE 9: BI Frameworks
- Business Intelligence frameworks: Learn how to get rid of the arsenal of reports and build a directly-applicable model that makes insights usable and reusable for the various organizational roles with BI frameworks. It ensures that everyone can access the right information in the right form at the right time, regardless of their role.
MODULE 10: Success factors of BI & Data Science
- Achieving success with BI & Data Science: what are the 12 most critical success factors to achieving real returns on BI & Data Science? Learn why all-round vision, analytics, agile working, and continuous improvement should be closely connected.
Day 4: Introduction DWH, goals, alternatives & architecture
MODULE 11: Introduction data warehousing
How do you arrive at a central database for management information that ensures that there’s one version of the truth? What other goals does a data warehouse serve? Data from various data sources (company processes) and external data are cleaned up and connected, so that the end user can easily bring them together.
- 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?
- Alternatives to the data warehouse: there are various alternatives to data warehousing these days. We’ll introduce you to data warehouse appliances, in-memory BI, data lakes, and data virtualization. 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 12: Data warehouse architecture
Working with a data warehouse architecture is of huge importance to agile BI. Which tools and skills do you need? How do you connect those to the overall IT and company architecture? Learn why internal consistency is the key to success.
- 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 teacher will discuss the pros and cons of the various methods: should you choose one method, or a combination?
- Big Data and the DWH: sometimes, data is too voluminous or unstructured to fit inside a traditional data warehouse. How can you handle this from an architectural standpoint? See also: day 7.
- Tooling overview: how do you choose a specific data modeling tool and database software? Which criteria are most important?
MODULE 13: The data warehouse & ETL processes
Filling a data warehouse happens sequentially in three steps: extraction, transformation, and loading (ETL). This can be done using the powerful analytical language SQL, or using ETL tools. These form 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? How do you shape the processes, and what’s involved?
- ETL 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 5: Analytics, Master Data Management, metadata & maintenance
MODULE 14: 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 or data lake? 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 15: Master Data Management (MDM) & 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.
- 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 16: Managing the data warehouse 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.
- 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? What are the most important success factors for a well-functioning data warehouse?
- Support and maintenance: how do you develop a data warehouse, keeping in mind support and maintenance?
- Competencies and skills: how is DWH maintenance different (or the same) from “traditional” maintenance? Which competencies and skills are required for successful DWH maintenance?
Day 6: Data Governance, data quality, and continuous improvement
MODULE 17: 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?
- 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.
- 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?
MODULE 18: 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?
- 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?
- Success factors: what pitfalls and success factors play a role? What are your own experiences, and what can you learn from them?
MODULE 19: Continuous improvement of 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?
- 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?
- 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 7: Introduction to Big Data, business case & big data architecture
MODULE 20: Introduction to Big Data & Predictive Analytics
During this module, you’ll learn exactly what Big Data is, and more importantly, what it isn’t. We’ll also cover the most critical properties of Predictive Analytics and data mining.
- The four properties: Big Data is characterized by the 4 Vs, but what does that mean for your own project, and for the algorithms, solutions, knowledge, and skills required?
- Mining & Analytics: how and when should you use data mining and text mining, and how do these differ from regular Business Analytics?
- Positioning: how can you best position Big Data & Predictive Analytics within your organization?
- Statistical knowledge: applying the right statistics is crucial. Which statistical knowledge do you need in your project?
MODULE 21: The business case of Big Data & project management
There’s a lot of money involved in Big Data-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.
- Thinking holistically: how should you link Advanced Analytics, Big Data, innovation, and process improvement?
- Business case components: what are the most important components in a Big Data business case? How should you deal with experiments that happen outside of your field of vision or responsibly?
- Convincing management: how do you lead management by the hand and convince them of the use and necessity of analytical models and Big Data?
- Big Data best practices: how have other organizations handled this, and what can you learn from this?
- Steps and pitfalls: what steps should you take in your Big Data project, and which pitfalls and risks should you avoid?
MODULE 22: Big Data architecture
Large quantities of (unstructured) data can’t be loaded into a traditional data warehouse. How can you handle this, and how should you design your architecture to match existing architectures?
- Impact on architecture: what’s the specific impact of Big Data on your Business Intelligence architecture? How are Big Data streams diverted to the right people, and in what form? How should you handle a pre-existing data warehouse?
- Calculating capacity: how do you calculate how many servers and clusters you need based on the demands and composition / volume of your data? Where can you purchase them in the cloud?
- Sensor data: when do you place sensors to collect data, and where do you put them? How can you ensure that they cost as little as possible and provide the least amount of overhead?
- Big Data quality: how do you handle the data quality of human-generated content on social media?
- Tooling: which platforms and tools are available to store and analyze large volumes of (unstructured) data? Consider all the Hadoop-based variants, NoSQL, etc. What are the differences and similarities?
Day 8: Predictive Analytics: algorithms, tools, techniques, and Data Discovery
MODULE 23: The algorithms, techniques, and tools
Matching the right issues with the right available algorithms, tools, and techniques is very important to the success of Data Science.
- Overview algorithms: which algorithms are available for data mining, and which one is the best fit for your situation? Consider neural networks, decision trees, genetic algorithms, nearest neighbor, etc.
- Techniques: which techniques can you use: data mining, text mining, image processing, real-time, etc.
- BI tools: which (BI) tools can support this process? Consider, for example, R, SAS, IBM SPSS, MicroStrategy, Watson Analytics, Pentaho, Spark, etc. What should you look out for when purchasing one of these tools? Which tools can handle extremely large volumes of data, and which ones fall short?
- Tool integration: how can various tools be linked, so that users can effortlessly analyze, report, and use the outcomes of the data mining process?
MODULE 24: Data visualization & Big Data Discovery
The way in which Big Data can manifest determines how quickly users can arrive at genuine insights and take the right action. Because data can be fleeting and ephemeral, more advanced data visualizations and computing power is required.
- Factors of effective data visualization: what do you need for effective Big Data visualization, and which factors should you take into account specifically?
- Cognitive barriers: which cognitive barriers should you take into account so that messages can reach the users without issue? Big Data requires you to be extra aware of this.
- Solutions for Big Data Discovery: which solutions for Data Discovery are on the market, and which criteria do you need to take into account to use them successfully?
Day 9: Building Big Data applications, skills, ethics, and privacy
MODULE 25: Developing Big Data applications
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?
- Case studies: what can we learn from case studies like the City of Dublin (detecting traffic jams), KLM’s social analytics (webcare and electronic word of mouth), and the Amsterdam PD and FD (winners of the Dutch BI & Data Science Award)?
- Big Data success and failure factors: the teacher will zoom in on these cases, and in groups, you’ll come up with the most important success and failure factors. What are your own experiences, and what can others learn from them?
- Sensors: what sensors are available, what types do you need for certain applications, and which ones offer you the best change of successful Big Data applications?
MODULE 26: Privacy, ethics, and legal frameworks
You can’t practice Data Science without considering privacy and ethics anymore. The large amounts of data poses an important question: how should you handle privacy and data ethics?
- 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 27: Skills and competencies
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?
- 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
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 training
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 management book The Intelligent Organization.
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.
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!