How to build a data science project in a corporate setting, by Soraya Christina, Senior Data Scientist at Morgan Stanley
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The document outlines best practices for conducting data science projects in a corporate environment, emphasizing the importance of collaboration with business stakeholders, proper problem definition, and effective communication of results. It highlights common challenges such as data availability, tool accessibility, and model selection, and suggests strategies for overcoming these obstacles. A real-world example of a stock price prediction using convolutional neural networks illustrates the application of these principles in practice.
How to build a data science project in a corporate setting, by Soraya Christina, Senior Data Scientist at Morgan Stanley
- 1. Applied data science in the industry: How to build a data science project in a corporate setting BEST PRACTICES AND A REAL-WORLD EXAMPLE Soraya Yama Wednesday, June 26, 2019 WIMLDS Montreal #3: Business & AI
- 2. How to guarantee the success of your data science project in industry? Challenges and solutions when building data science projects in industry or in a corporate environment How to generate insights for better business decision making is what drives data science projects? How to work with business side by side? How to build a reliable and understandable analysis flow/solution/product? How to properly communicate results and key elements?
- 3. Data science in industry vs in research Industry Research Faster pace than academia – quick iterations Experiments are easier in a lab If analysis does not produce results quickly, drop it and/or redesign it Follow best practices to get approvals after peer reviews Simple solutions are preferred over novel complex ones – hard to understand, hard to trust Let’s go for the fancy cool new algorithms!!! Limited time and resources so need to balance research excellence with business needs Research is expected to take a lot of time Not everyone you work with understands data science need to convince decision makers to use the insights to drive decisions Peers understands data science and the importance of research The team might not be data-driven or analytics- minded You will most likely have more than one analyst in the team Explain statistical concepts in layman terms Your peers are more likely to understand the statistical jargon you use You won’t do data science only – you might need to learn new skills (data engineering, new programming language, new packages etc) It is less likely that you do data engineering or architecture while being a data scientist Rejecting a hypothesis is equally interesting Rejecting a hypothesis can be looked at as a failure
- 4. Focus on industry specific projects
- 5. Challenges faced 1. Sometimes problems are not well defined 2. Sometimes data is not available or not in a usable format 3. Sometimes tools or data analysis platforms are not available 4. Which models to use? Which algorithms are more suitable for the analysis and the infrastructure? 5. Sometimes clients or business lines will not understand your analysis, the methods used 6. How to build your data science flow and what to avoid? 7. How to presents results in a way business stake holders understand them?
- 6. 1. Sometimes problems are not well defined Data Science is a science therefore it follows the scientific method In a scientific method, the process starts with a question to be asked or a problem to be identified In data science, the process also starts with a problem to solve This requires a proper understanding of the business context Sometimes sitting with the business and help formalize the problem is key
- 8. 2. Sometimes data is not available or not in a usable format Which data sources to use? ◦ data lake, data warehouse, database, row data to be imported like images, sound files, spreadsheets or flat files How to collect the data? ◦ import data, create a data pipeline Who to work with for the data acquisition? ◦ data engineers, database system managers etc. How to convince teams you need this data? ◦ explain the use case, have your manager support you How to maintain this new data acquisition? ◦ is it a one shot data acquisition, is it a recurrent feed? Where to store the data? ◦ big data storage, file system, cluster like Hadoop? If it’s a data stream, how to build it? ◦ Kafka, AWS, Flume etc.
- 9. 3. Sometimes tools or data analysis platforms are not available Identify which tools or platforms are well adapted to solve the problem and which ones are available or easy to get Request them / install them Work on the data infrastructure
- 10. Questions to ask Eg. Can I solve this specific use case using a Python script in and IDE? Am I looking at big data in which case I might need a distributed system like Spark? Shall I store the data in a filesystem or on HDFS? The team is using R, but can I productionnize a script written in R? There is a vendor product I am asked to use, but is it convenient for the purpose of the use case?
- 11. 4. Which models to use? Which algorithms are more suitable for the analysis and the infrastructure? KNN is a weak learner Decisions trees work best to detect non-linear interactions (so should not be used for time- series) Radom forests can work with large labelled or unlabelled data Ordinary Least Square should be used if high dimensional data set (nb variable > nb observations) Stratified sampling is better than random sampling for classification problems Etc. Ask yourself the right questions before jumping ahead and using the fanciest model you can think of. Business might not understand it.
- 12. 5. Sometimes clients or business lines will not understand your analysis, the methods used Start small – use a data sample to build your case Do a prototype (Proof of Concepts) and show them how they can leverage data analysis Do not use the statistical jargons, use layman terms to communicate your idea Sell your idea Make it simple enough to understand, efficient enough to implement, interesting enough to use
- 13. Real world example Signal analysis followed by a stock price behaviour prediction using a convolutional neural network Data points to be investigated will labelled 1. All other cases will be labelled 0.
- 14. Detecting ratios anomalies using tradition statistical detection method and isolation Forest (clustering for anomaly detection) - Process time very long especially when using millions of rows – need to distribute the data Isolation Forest exist in sklearn, but has not yet been fully implemented in MLLib + Isolation Forest efficient when handling big data Very accurate detection compared to traditional methods
- 15. 6. How to build your data science flow and what to avoid? Your analysis code has to be understandable and reproducible (structured and testable) If you are using a data analysis flow, your flow has to be structured
- 16. 7. How to presents results in a way business stake holders understand them? Making complex concepts easy to understand by business lines Sometimes a graph is worth a thousand words Reports or dashboards have to be clear with ideally one insight per view (do not overload the page) Show the results in a way they are easily interpretable
- 17. A real-world example of a real-time failure prediction using Spark System failure real-time predictions using: Sources systems metrics Kafka for data streaming Spark for the predictions HDFS to store data Javascript/Jquery or vendor product for the frontend Source systems Kafka Stream Spark Streaming Spark ML Front End HDFS
- 20. Offline training – Online testing using Spark
- 21. ANNEXE
- 23. Data science tools magic quadrant January 2019