Big Data and Predictive Analytics are inextricably linked to process improvements and innovation. As a consultant or (project) manager, you want to understand how that works and have your organization work smarter with Big Data and advanced Analytics, but how do you do that? And:
- How do you convince your colleagues of the usefulness of Big Data / Analytics?
- What does a Big Data process look like, and what are the main pitfalls and risks?
- Which algorithms and techniques are available and how do you choose the right one?
- What should you know about statistics, and how do you use text mining for learning?
- What tools and platforms (Hadoop, R, SPSS, Spark, Python, BI tools) are available?
- What place do they have in the architecture and how do you keep a grip on all of this?
But more importantly: how do you successfully apply your Big Data process, and how are you going to embed it in your organization? What hard and soft skills do you and your team need? How do you use Internet of Things (IoT) for data perfection? If you are faced with one or more of the above questions, we recommend our Big Data & Predictive Analytics training.
Things that are directly addressed
The Big Data and Predictive Analytics training course doesn’t just present you with theory; ample attention is also placed on the practice. You’ll become acquainted with a number of recent best practices such as the Amsterdam-Amstelland Fire Department, Netflix, and the Police who collar the crooks before they even commit a crime. Examples from the various supplier solutions are also addressed, such as R, SPSS, SAS Miner, Python, MapReduce, and algorithms like decision trees and neural networks.
Achieving success with Big Data & Analytics
In three intensive days, you’ll be readied to immediately start using Big Data and Predictive Analytics within your organization. Once you have followed our three-day training course, you’ll be perfectly able to start your Big Data process and then achieve step-by-step success with analytics.
Training in advanced Big Data applications
Big Data and Predictive Analytics are very different from mainstream Business Intelligence projects where reporting, KPIs, and dashboarding are central. The size and complexity of the data also differ. The specialist area of Big Data & Analytics therefore requires entirely different skills and tools, but it continues to affect the intelligence of the organization.
Without the use of the right skills and tools, Big Data often provides conflicting results – a fiasco waiting to happen. Our practical model, in which all Big Data tools and methodologies are discussed, creates a clear pathway towards resounding results.
During this 3-day Big Data & Predictive Analytics training course, you’ll learn how a Big Data & Predictive Analytics project is managed from A to Z. You won’t just learn about the Internet of Things (IoT), data lakes and data architectures, but also how to build and clarify a business case, and how to deal with privacy issues and ethics.
Day 1: Introduction, project management, and big data architectures
Introduction Big Data & Predictive Analytics
You will learn exactly what Big Data is and, more importantly, what it is not, and what the characteristics are of Predictive Analytics and data mining.
- What are the four characteristics of Big Data? And what does that mean for your project? And for using tools and people?
- What are data mining and text mining and how do they differ from regular Business Intelligence?
- How to position Big Data & Predictive Analytics in your organization?
- Applying the correct statistics is crucial. What statistical knowledge do you need in your project?
The business case for Big Data & Analytics and project management
Investment in Big Data and Analytics can be enormous, but so are the potential benefits.
- What is the relationship between Big Data, Analytics and innovation and process improvement?
- From what components should the business case be compiled? How to deal with experiments and pilot projects?
- How do you convince management of the usefulness and need?
- How have other organizations addressed this and what can be learned from it?
- What steps must you take in your Big Data project and what are the pitfalls and risks?
Big Data Architecture
Big Data Architecture often involves large amounts of data that no longer fit in a data warehouse. How do you set up an architecture that connects to existing architectures?
- What is the impact of Big Data on architecture? And how do you have Big Data flows link to existing architectures? How do you deal with an existing data warehouse?
- How do you calculate how many servers / clusters are needed based on the data and the questions?
- How do you arrange sensors, where do you place them, and how do you ensure that they cost as little as possible and cause as little inconvenience as possible?
- How do you deal with data quality, especially when it comes to human-generated content on social media?
- What tools and platforms are available for storing and analyzing Big Data? Think of Hadoop, NoSQL, etc. And what are the differences?
Day 2: Predictive Analytics: algorithms, tools, techniques, and Data Discovery
The algorithms, tools, and techniques
The use of the appropriate algorithms, tools, and techniques linking to the specific issue is critical.
- What data-mining algorithms are available, and which one best suits your issue? Think of neural networks, nearest neighbor, decision trees, genetic algorithms, etc.
- What (BI) tools can you use for this? Consider R, SAS, SPSS, MicroStrategy, Pentaho, etc. What should you look for when purchasing those tools? Can they all deal with large amounts of big data?
- How can you “tie together” the various tools so that end users can easily use the results of data mining?
- Techniques: data mining, text mining, image processing, real-time, etc.
Data Discovery & Visualization
How data manifests determines how quickly users can achieve genuine insight.
- Which aspects play a role in effective data visualization, and what should you look for?
- What are the cognitive barriers that you must respond to in order for every user to be able to understand the data as effectively as possible?
- What Data Discovery tools are available, and what should you look for when purchasing one?
Day 3: Building Big Data applications, skills, ethics and privacy
Big Data applications
The abundance of data proposes a number of challenges. What can and should you do with it? How can you devise and implement the right applications?
- What can we learn from compelling cases like:
- City of Dublin: detecting traffic congestion
- Social Analytics (KLM): webcare, electronic word of mouth (eWoM)
- Police and Fire Amsterdam (Dutch BI Award)
- Which success and failure factors played a role in these? What are your own (possible future) experiences, and what can be learned from them?
- Which sensors (sources) offer you the greatest chance for the most appropriate applications?
Ethics, privacy and legislation
In collecting and linking all kinds of privacy-sensitive data, you will also face an important privacy issue.
- What ethics are important in order to create a framework through which decisions are made on whether or not data should be used?
- What do the relevant laws and regulations say about this? How do you deal with it? How do you respond to public opinion and the risk of reputation damage?
- What methods and techniques are available to still have access to the data while not compromising privacy? Consider, among others, data masking.
Skills and competencies
Data Science is becoming a professional discipline and new knowledge is also rapidly becoming available in terms of the skills and competencies.
- What are the top three skills of a data scientist, the most-wanted job of the 21st century?
- What skills (soft & hard) are needed in a successful Big Data analytics team?
Your instructor: Minne van der Sluis
The instructor has more than 25 years of experience as a Data Science consultant and is a Certified Business Intelligence Professional (CBIP). He started this Big Data & Predictive Analytics training course in 2010 and has constantly refined it.
Get to know the Advanced Analytics success factors
Get to know the success factors of advanced Analytics. Our three-day Big Data & Predictive Analytics training highlights both the technical and the business perspectives, such as machine learning, data lakes, project management, data quality, business cases, and applications.
But above all, you’ll learn what the success factors are for Big Data applications and how to convert data into better margins and more satisfied customers and employees.
There is ample opportunity every day to share experiences, participate in group discussions, and work on assignments. This creates a perfect mix of the practical and the theoretical frameworks and models. Upon completion of this unique course, you will receive a certificate as a participant of the Passionned Academy and a copy of the book “We are Big Data” (nominated as management book of the year).
The course is intended for anyone interested in the possibilities and impossibilities of Big Data and Predictive Analytics for their own organization. The program is often followed by up-and-coming project managers, BI managers, IT managers, marketing managers, program managers, data analysts, business analysts, and controllers.
Register for this Big Data & Analytics training
Use our registration form to register directly for the next Big Data & Predictive Analytics training. If you have any questions, or if you want to attend this training in-company, please contact us.