added besinees part short discussion

Author: zoupeicheng

README.md

@@ -7,9 +7,11 @@ Python Data Science Toolbox
 This is my personal open notebook for foundamental Data Science in Python at work place.
 Unlike many books, this notebook is quite superficial. It covers major steps of doing simple data analysis and a lot of simple code examples.
 I learnt the those tools from many sources, welcome to browse and edit this notebook.
-This notebook is just a **remainder for what is available out there**. It does not offer any mathematical or technical or business details.
+This notebook is just a **remainder for what is available out there**. It's under development.
 
 You can visit this [handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) for technical details.
+I recommend taking Coursera's ML courses by Andrew for beginners who want to learn the foundamentals of ML.
+
 
 ## Quick advice
 
@@ -19,10 +21,11 @@ There are many great sources to learn Data Science, and here are some advice to
 2. Get to know the general idea and areas of knowledge about data science.
 3. Practice as you go. Google or Bing any term or question you don't understand. Stackoverflow and supporting documents for specific packages and functions are your best friend. During this process, do not lose sight of the big picture of data science.
 4. Become a better data scientiest by doing more projects! (Don't try to memorize these tools, just do data science!)
-5. Once comfortable with all materials in this notebook and engage in data analysis in business, you will know what skills to pick up next.
+
 
 ## Materials in this notebook
 
+0. [Road Map in Business](roadmap.md)
 1. [Environment Configuration](EnvironmentConfiguration.md)
 2. [Data Processing](DataProcessing.md)
     * Getting Data

roadmap.md

@@ -0,0 +1,80 @@
+# Data Science for Business
+A vast majority of the knowledge is from this [book](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking-ebook/dp/B00E6EQ3Xs).
+
+## Main structure of data science at workplace
+The Cross Industry Standard Process for Data Mining
+See [wiki](https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining).
+
+Center of data mining: automated pattern, knowledge and regularities discovery.
+
+### Classic Tasks
+
+* Classification
+* Regression
+* Similarity matching
+* Clustering
+* Co-occurrence grouping
+* Profiling
+* Link Prediction
+* Data Reduction
+* Causal modeling
+
+
+### Phases of CISP-DM
+
+* Business Understanding: Formulate the business problem to unambiguous data mining problems
+    * What exactly do we want to do?
+    * How exactly would we do it?
+    * What parts of this use scenario constitute possible data mining models?
+* Data Understanding
+    * How reliable is the data for our task?
+    * What is the cost of getting data?
+    * How the data affects our approach? Note that superficially similar tasks could have distinct approaches due to different data available.
+    * Business understanding + data understanding determines possible solutions.
+* Data Preparation
+    * creative, sensible and business minded varialble crafting
+    * systematic data processing/clearning
+    * Pay Special Attention to Data Leakage
+* Modeling
+    * Most technical and scietific part. Others are arts. (joking).
+* Evaluation: in business context, not in the lab.
+    * Quantitative and qualitative assessments
+    * Stakeholders considerations: pros and cons
+    * Comprehensibility of model, or how to making the model more comprehensible?
+    * Do this *Before the deployment*
+    * How susceptible is the model to the changing behaviour of data source?
+    * The model is what developers build (advisable to include them in data science projects)
+
+
+### Side Remark: Managing a data science team
+* Data science tasks are exploratory undertaking in nature and is closer to research and development than it is to engineering.
+* Iterates on approaches and strategy rather than software designs
+* Outcomes are far less certain
+* Results of each step change change the understandings of problems
+* Do not engineeting solution directly for deployment: most of the efforts should go to analytical testings, pilot studies and thowaway prototypes to reduce risks.
+* In building a data science team, the most important qualities are:
+   * Formulate problems well 
+   * Making reasonable assumptions if face of ill-structured problems
+   * Prototype solutions quickly
+   * Design Experiments that represent good investments
+   * Ability to analyze the results
+   * NOT traditional software engineering expertise
+
+### Related Skills
+* Statistics
+* Querying Database
+* Data Warehousing
+* Machine Learning or Applied Statistics or Pattern Recognition
+* Answer Business Questions with These Techniques
+    * Who are the most profitable customers? Querying DB
+    * Is there really a difference between the profitable customers and the average customer? Hypothesis Testing
+    * However, who really are these customers? Can I characterize them? Find pattern that differentiate profitable customers from unprofitable ones.
+    * Will some particular new customer be profitable? How much revenue should I expect this customer to generate?
+
+### Summary
+* There are fields of study closely related to data science, each task type serves different purpose and has an associated set of solution techniques
+* Data Scientist combine these components
+* A successful data project involves an intelligent compromise between what the data can do and project goals
+* Need to keep in mind how data mining results will be used and use this to inform the data mining process itself.
+
+