Data Science Methodology

A gentle introduction of Data Science Methodology, along with frameworks explanation that commonly used on current tech companies.

Transcript

1. Data Science Methodology Chapter 1 – A Living Parable Fiqry Revadiansyah
2. Trainer Bio Data Scientist Bukalapak (2018 – Present) Technical Content Reviewer Packt Publishing (2019 – Present) Email : fiqryrevadiansyah@gmail.com | Linkedin : https://www.linkedin.com/in/fiqryrevadiansyah Fiqry Revadiansyah Work Experience Teaching Experience Guest Lecturer MB1201 Business Statistics at SBM ITB (April 2020) Part-time Teacher Data Science Purwadhika (2019), and Workshop speaker: Introduction to ML for DS (June 2020) & Statistics for Business Analytics (Nov 2019)
3. Background
4. What do you think about planting?
5. Planting?
6. If you want to plant, what should you do?
7. Planting Activity Choose Plant Seeds Select Planting Method Determine the Planting Objective Optimize Plant Result
8. So, this is not a planting class, right? What is the relation to Data Science?
9. Analogy of Farming My Expression when hear Data Science = Farming
10. Analogy of Farming The Data Science Methodology is an iterative system of methods that guides data scientists on the ideal approach to solving problems with data science, through a prescribed sequence of steps.
11. Data Science Methodology *John Rollins, SDS IBM
12. Data Science Methodology 1. Define the Goal, and Choose the Road 2. Gather the resource, and Identify its Characteristic 3. Do the Plan, and Evaluate it 4. Market to Public, and Regather the Opinion Preparation Stage Data Related Stage Drive the Solution Stage
13. 1. Business Understanding What problem do you want to take action? As a farmer - Gain More Profit - Live Healthy As a Data Scientist - Increase Visitor Numbers - Reduce Fraudulent Acts
14. 1. Business Understanding What problem do you want to take action? Impression • Number of Visit • Number of Active Visitors Budget • Cost Per Product • Cost of Acquisition Users • Number of Retain Users • Satisfaction Score
15. 2. Analytic Approach Which lane do you prefer to take? As a farmer Choose planting method: Hydroponics or Aquaponics As a Data Scientist Choose analytics method: Predictive Modeling (ML) or Diagnostic Analysis
16. 2. Analytic Approach Which lane do you prefer to take? If the question is to determine probabilities of an action • Use a Predictive model If the question is to show relationships • Use a Descriptive model If the question requires a yes/no answer • Use a Classification model
17. 3. Data Requirements What kind of resource do you need? As a farmer Prepare the tools & seeds to achieve the farming goal As a Data Scientist Choose what kind of data that might solve the problem
18. 3. Data Requirements What kind of resource do you need?
19. 4. Data Collections How do you collect the resource? As a farmer Select shop to purchase seeds, either local/online As a Data Scientist Determine and Collect the data from data source
20. 4. Data Collections How do you collect the resource? Local/Internal • User Data • Traffic History Data Public/External • Open Data Repository Scraping • Social Media • Website
21. 5. Data Understanding What have the resource tell you? As a farmer Get to know what is happening on the soil, plants, and the environment As a Data Scientist Get to know, what does the data tell us about the problem, and visualize it
22. 5. Data Understanding What have the resource tell you? Data Visualization Discover data trend, pattern, and any other relevancies accordingly Descriptive Statistics Decipher the aggregate information, such as average median, mean, missing value, etc Funnel Analysis Uncover the hidden information
23. 6. Data Preparation What have to do before doing an action? As a farmer Prepare the suitable soil for the selected plants, set the growing medium well As a Data Scientist Handle data problem, such as missing values, duplicates, and other
24. 6. Data Preparation What have to do before doing an action? Missing Values Duplicated Data Irregular Format Imbalanced Clean Data
25. 7. Modeling How do you make a model from your data to solve the problem? As a farmer Planting the seeds, watering the plants, etc. As a Data Scientist Model the data by Machine Learning Process
26. 7. Modeling How do you make a model from your data to solve the problem? Choose ML Model Determine the model based on expected output (prediction or regression) Model Iteration Iterate the modeling process by K- fold Ensemble Model Combine ML models to gain better model accuracy
27. 8. Evaluation Have the model already answer the problem or need to be improved? As a farmer Inspect the plants, is it free from pest/disease? As a Data Scientist Do the model has good fitting accuracy, should it be enchanted?
28. 8. Evaluation Have the model already answer the problem or need to be improved? Model Interpretation Interpret the model result to be understood by other people Model Evaluation Validate the model performance by its problem type (Accuracy, Precision, Recall, RMSE, etc)
29. 9. Deployment Can you apply the model to the real life? As a farmer Sell the vegetables to the market/store As a Data Scientist Integrate ML model into production ecosystem
30. 9. Deployment Can you apply the model to the real life?
31. 10. Feedback Is there any input to your business solution? As a farmer Gather suggestions/ comments from our customer As a Data Scientist Take many feedbacks from various entity such as end- user, stakeholders, etc.
32. 10. Feedback Is there any input to your business solution? METRICS BEFORE AFTER DEPLOYMENT ML MODEL Daily Active Users 1000 1600 (+60%) Cost Spent 1 mio/month 500k/month (-50%) Revenue Gain 10 mio 30 mio (+300%) SLA 3 days 2 days (-33%)
33. In short… Data Science Methodology are… Define the Goal, and Choose the Road • What problem do you want to take action? • Which lane do you prefer to take? Gather the resource, and Identify its Characteristic • What kind of resource do you need? • How do you collect the resource? • What have the resource tell you? • What have to do before doing an action? Do the Plan, and Evaluate it • How do you make a model from your data to solve the problem? • Have the model already answer the problem or need to be improved? Market to Public, and Regather the Opinion • Can you apply the model to the real life? • Is there any input to your business solution?
34. Lets take a simple exercise Data Science Methodology on these cases:
35. Lets take a simple exercise Youtube Case Define the Goal, and Choose the Road • What problem do you want to take action? • Which lane do you prefer to take? Gather the resource, and Identify its Characteristic • What kind of resource do you need? • How do you collect the resource? • What have the resource tell you? • What have to do before doing an action? Do the Plan, and Evaluate it • How do you make a model from your data to solve the problem? • Have the model already answer the problem or need to be improved? Market to Public, and Regather the Opinion • Can you apply the model to the real life? • Is there any input to your business solution?
36. Lets take a simple exercise Jenius Case Define the Goal, and Choose the Road • What problem do you want to take action? • Which lane do you prefer to take? Gather the resource, and Identify its Characteristic • What kind of resource do you need? • How do you collect the resource? • What have the resource tell you? • What have to do before doing an action? Do the Plan, and Evaluate it • How do you make a model from your data to solve the problem? • Have the model already answer the problem or need to be improved? Market to Public, and Regather the Opinion • Can you apply the model to the real life? • Is there any input to your business solution?
37. Lets take a simple exercise Gojek Case Define the Goal, and Choose the Road • What problem do you want to take action? • Which lane do you prefer to take? Gather the resource, and Identify its Characteristic • What kind of resource do you need? • How do you collect the resource? • What have the resource tell you? • What have to do before doing an action? Do the Plan, and Evaluate it • How do you make a model from your data to solve the problem? • Have the model already answer the problem or need to be improved? Market to Public, and Regather the Opinion • Can you apply the model to the real life? • Is there any input to your business solution?
38. Data Science Methodology Chapter 2 – A Lesson from Tech Industry Fiqry Revadiansyah
39. Frameworks on How every different Division Works
40. Data Driven Framework Product Management AAARRR Model Product Design Double Diamond Cross-Division Lesson
41. Data Driven Framework Business Development Business Model Canvas Engineering Agile Project Management Cross-Division Lesson
42. Product Management AAARRR Model Journey Prototype Describe an end-to-end process of how our customer get impression until producing revenue stream Develop Persistently Focus on dropping channel, constantly evaluate with the whole complexity (engineer, ux, data, etc) Data Driven! They evaluate those channel based on data. Analytics is needed to enhance the decision making process here.
43. Product Design Double Diamond Design Thinking Careful on Iteration A belief to double check progress, start from a helicopter view, end to the ant view. Exploration to Action Focus on explore the situation first, define hypothesis based on pain points, develop product to solve, deliver to evaluate Data Driven! From the beginning till the end, they use data to tell the story about our customers
44. Business Development Business Model Canvas Plan on Demand List down all funnels of business process, set the subjects of every key points to ensure reliability Customer Satisfaction Aside from the streams, this model also focus on customer growth, such how to maintain the relationship, how to segment them Data Driven! Data is always needed to recap every key points of this model
45. Engineering Agile Project Management Iterative Approach Managing software development projects that focuses on continuous releases and incorporating customer feedback with every iteration Scrum and Kanban Scrum is focused on fixed-length project iterations, Kanban is focused on continuous releases. Data Driven! In order to track the process, data is needed to evaluate the process
46. Data Driven Framework Data on Stakeholder Expectations “Okay, so what?” “OK, Thanks” “Seems interesting” “Good, I think we need to do an AB Test” “Impressive! Lets do A, B, C tomorrow!”
47. Data Driven Framework Objective: Connect the dots to find a significance
48. Data utilization through stakeholders expectation “Okay, so what?” “The Actionless Data” When you give data without any particular advantages (rough format) User Table Transaction Table
49. Data utilization through stakeholders expectation “OK, Thanks” “The Labeled Data” Data with particular group/label, an aggregated information. Daily Transacting Users The average of Production Cost on June 2020
50. Data utilization through stakeholders expectation “Seems interesting” “The Data Combination” Interconnected labelled-data brings better point of view. Count Transaction by User Location Conversion Rate (CVR) of Voucher X
51. Data utilization through stakeholders expectation “Good, I think we need to do an AB Test” “The Most Significant Data” Aggregated/Interconnected data, which acted as main metrics of the company Retention Cohort of Total Customer from city X Budget Allocation of Product X based on Customer Group
52. Data utilization through stakeholders expectation “Impressive! Lets do A, B, C tomorrow!” “The Data Guru” Interconnected of most significant findings, as a funnel to answer missing gap (Funnel Type) Retention Cohort based on User Type, Country of Origin, etc Combination of Product Type, Location, and Revenue
53. Lets take a simple exercise Frameworks Application
54. Product Management Awareness Acquisition Activation Retention Referral Revenue Trending Topics Content Uniqueness Content Quality Content Stickiness Gift Away Ads
55. Product Management Awareness Acquisition Activation Retention Referral Revenue Impression Trial Register Flexi Cash Monthly User Gift Promotions Interest Percentage
56. Product Management Awareness Acquisition Activation Retention Referral Revenue Impression Product Trial Sign Up Daily User Satisfaction Partner Gains
57. Are you ready to work like a Data Scientist?