Using data to drive value creation
The use of big data is a key basis of competition and growth for individual firms. From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously. A shortage of a deep-analytical workforce is blocking growth according to a report of McKinsey.
However, the new generation of self-service tools enables people to get great results themselves without the need of a hardcore data scientist. These self-service tools are designed to focus on creating value from data of instead on the harder technical implementation details.
This course brings you to a point where you can execute many data driven projects yourself without the intervention of a specialist. Also if you have a specific project where you consider using big data and data analytics to drive artificial intelligence, you have the opportunity to discuss the case with the lecturer to get you started and get the most value out of it.
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
At the end of this program you will:
- Be able to select the right machine learning tool for a job
- Use self-service tools to implement a machine learning project
- Build complex and interactive visualizations
- Use the Python programming language to handle complex analytical jobs
- Deploy jobs as webservices
|Morning||Review machine learning concepts and big data tools||Advanced data visualization||Machine learning: regression||Machine learning: unsupervised learning||Real-time data processing|
|Lunch||Lunch @ MSM||Lunch @ MSM||Lunch @ MSM||Lunch @ MSM||Lunch @ MSM|
|Evening||Python for data science: guided workshop||Advanced data visualization||Machine learning: classification||Neural networks||Natural language processing|
- Non mathematical deep dive into data science
- Advanced data visualization with Tableau
- Big data machine learning on AzureML (Microsoft)
- Python for data scientists
- Teaching method
- Case studies
- Online exercises
Managers and professionals who want to be able to lead an data-intensive team but also want to have hands on experience of machine learning.
The ideal student has a good knowledge of statistics, he should be acquainted with regression. Although no advanced mathematics will be used, students will benefit from experience with linear algebra and an introduction to calculus.
Each participant receives a certificate of attendance on the last day of the course. Only for those who will complete an additional assignment will receive a diploma. In case you would like to have more information you contact firstname.lastname@example.org