After many years of heading the executive office of the Lindau Nobel Laureate Meetings, I decided it was time for a change. That did not only mean doing the same stuff for a different employer, but rather changing fields entirely. After some consideration of what I wanted to do, I focused on „Data Science“, a field combining my fondness for data visualizations with my existing skills in databases, SQL, some programming, and so on. It took me a while to realize that the field of data science is currently being turned upside-down by teh revolutions of machine learning – so there’s some additional field to acquire new knowledge.
My current learning path includes different categories:
- Foundational and theoretical knowledge (=> MIT Micromaster in Data Science)
- C-level understanding of data (=> MIT Professional Education: Data Leadership)
- Practical, real-life knowledge and exercise (=> IBM Data Science Professional)
- Focus: AI Ethics & AI for Good
- Focus: Data Visualization
- Additional knowledge in programming, data engineering, etc.
MIT Micromaster in Data Science & Statistics
| Title | Provider | Duration | Contents | Status | Certificate |
| Machine Learning with Python: Form Linear Models to Deep Learning | MIT CSAIL | 4 months | linear and non-linear classifiers, regression, collaborative filtering, neural networks (RNN, CNN), LSTM, unsupervised learning (clustering, generative models, Gaussian mixtures, EM), reinforcement learning (q-learning, NLP) | completed | View |
| Probability: The Science of Uncertainty and Data | MIT CSAIL | 4 months | mathematical backgrounds, conditioning, independence, counting, discrete random variables, continuous random variables, PDFs, CDFs, derived distributions, covariance, correlation, expectation, Bayesian inference, LLMS, law of large numbers, central limit theorem, Bernoulli and Poisson processes, Markov chains | completed | View |
| Statistics | MIT CSAIL | 4 months | expectations, convergence of rvs, models, statistical inference, maximum likelihood (estimators), delta method, hypothesis testing, p-values, Bayesian inference, linear and logistic regression, multivariate models, causal inference, classification | completed | View |
| Data Analysis and Statistical Applications | MIT CSAIL | 4 months | statistical theory, hypothesis testing, clinical trials, high-dimensional data analysis, gene data, PCA, network analysis, graph theory, time series, SARIMA models, flow simulation | completed | View |
MIT Data Leadership
| Title | Provider | Duration | Contents | Status | Certificate |
| Data Leadership: Transforming the Corporation’s Operations, Management, and Mindset to Leverage Data, AI and Cloud Computing | MIT PE | 2 months | big data, data parsing, no-code models, data pipelines, agile development, client-server architectures, SQL, APIs, GraphQL, SaaS platforms, data reporting, AI bias and fairness, governance, compliance | completed | View |
IBM Data Science Professional
| Title | Provider | Duration | Contents | Status | Certificate |
| IBM Professional Certificate Data Science | IBM | 6 months | data science theory; introduction to Python, R and SQL; APIs; GraphQL; webscraping; plotting and dashboards; SQL, transactions, procedures, Python-SQL-APIs; data cleaning; exploratory data analysis; single and multivariate polynomial linear regression, ridge regression, logistic regression, KNN, K means, random forest; grid search; model evaluation; pipelines; use of generative AI for code generation | completed | View |
Focus: Ethics of AI & AI for Good
| Title | Provider | Duration | Contents | Status | Certificate |
| Ethics for AI | London School of Economics | 3 weeks | AI ethics in government, society, industry, e.g. AI in social media content moderation, or AI for hiring platforms | completed | View |
| AI for Good: Air Quality Forecasts | Deeplearning.AI | 3 weeks | air quality sensor analysis, data cleaning, visualization, predicting missing values, interpolating values between sensor stations with neural networks | completed | View |
| AI for Good: Climate Change Analysis | Deeplearning.AI | 4 weeks | wind turbine power data analysis, data cleaning, visualization, wind power forecasts | completed | View |
| AI for Good: Disaster Management | Deeplearning.AI | 4 weeks | satellite image damage analysis via neural networks, text message analysis via topic modeling (latent dirichlet allocation) | completed | View |
Focus: Data Visualization
| Title | Provider | Duration | Contents | Status | Certificate |
| Datenvisualisierung mit Python | heise Academy | 3 weeks | matplotlib, plotly, Dash | completed | View |
| Data Visualization and Information Design | Domestika | 1 week | data visualization, design, Adobe Illustrator | completed | View |
Others
| Title | Provider | Duration | Contents | Status | Certificate |
| Transformers: Introduction to LLMs | heise academy | 1 day | LLMs: history, code, functionality | completed | View |
| Python Bootcamp | heise academy | 4 weeks | Python basics | completed | View |