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+# Smart Infrastructure
+
+## General Methodology
+
+1. Business understanding: What is the problem to solve
+    - What is holding Londoners back from cycling?
+1. Analytics approach: How can I use data to answer Q1?
+    - Using data, only 2% of trips are done cycling, why? Try to learn causes by
+      the data
+1. Data requirements: What Existing Do I need to analyze the problem
+    - Cyclists’ casualties data
+    - City data
+    - Cycle thefts
+1. Data collection: collect new data
+    - Try to collect data using sensors
+1. Data understanding: Verify if the data collected can solve the problem
+    - Using tools like _uni-variate_, _pairwise correlation_, and histogram
+1. Data preparation (loop back to data collection): If the data is usable, or if
+   preparation must be done,
+    - Possible problems:
+        - Structural error
+        - Merging of data
+        - Outlier analysis
+        - Redundancy
+    - Data collected contains observations (values), and attributes / features
+      (keys), can be:
+        - Continuous or Discrete
+        - Numeric or nominal
+1. Modeling: Visualizing the data to answer questions
+    - Using ML: split dataset to train, validate and test them
+        - Train: to fit the model
+        - Validate: provide unbiased evaluation while training (tuning
+          hyper-parameters)
+        - Test: provide evaluation on final model fit
+1. Evaluation: Does the model answer the question or is change needed
+1. Deployment: Using the model in practice
+1. Feedback (loop back to modeling): Use feedback and new data, to possibly
+   re-train or fine-tune the model, and answer the initial question.