Python is a widely used programming language for data science. It has a comprehensive standard library and supports multiple programming paradigms. Data science involves transforming data into meaningful information through techniques like probability, statistics, and analytics to help with decision making. Data science roles can include data analyst, data scientist, data engineer, and machine learning engineer. The data science process involves gathering, cleaning, analyzing, visualizing, and modeling data to gain insights. Tools like Pandas, Seaborn, and machine learning are used in data science.

1. Python
Implementation
in
Data Scinece

2. Speaker Bio
Andi Mardinsyah
(Data Scientist Telkom Indonesia)
• University of Indonesia -
Electrical Engineering Majoring
in Computer System
• Offenburg University -
Communication and Media
Engineering

3. Python
• Interpreted high-level general-purpose programming language
• Supports multiple programming paradigms, including structured (particularly, procedural),
object-oriented and functional programming, statistics, analytics, and computer science.
• comprehensive standard library

4. Data Science
• Data science enables us to take data and
transform it into meaningful information that
can help us make decisions.
• Data science is interdisciplinary and combines
other well-known fields such as probability,
statistics, analytics, and computer science.
There is a wide range of ways that data scientists
may work with strategy, decision making, and
implementation of analysis
The role of a data scientist may look very different
depending upon what company you’re working on
and what business domain you’re working in!

5. Data Analyst
• Knowledge of database
• Ability to query data (SQL, NoSQL)
• Ability to describe data (Trends, changes, etc)
• Ability to use visualisation tools (Tableau, PBI)
• Fluent at spreadsheet (Excel, sheet)
• Ability to present data (Slides, Dashboards)
• Business Acumen
Data Scientist/Research Scientist
Data Engineer
• Data Modeling (Statistical/Machine Learning)
• Conduct Research (Statistics)
• Experiment (A/B testing)
• Extract insights, Tell Story
• Programming (Python, R, ..)
• Knowledge of database architecture
• Knowledge of cloud platforms
• Data pipeline (ETL)
• Programming (Python, Scala, Java..)
Machine Learning Engineer
• State-of-the art machine learning models
• Deep Learning
• Computer Vision, NLP
• Model deployment
++ Product
Product Analyst
Business
Intelligence
++ Business
++ Statistics
++ Software Engineering
++ Data Engineering
Data Science
In most of the industries
Data Science Roles

6. Data Science Process

7. Data Science Tools
Essential To Start With
Online Tutorial
Google search in action!

8. Data Science Skills

9. Data Science Data Gathering

10. Data Science Coding

11. Data Science Mathematics & Statistics

12. Data Science Data Visualisation & Communication

13. Data Science Exploratory Data Analysis
•Comparison Analysis
•Distribution Analysis
•Trend Analysis
•Ranking Analysis
•Variance Analysis
•Contribution Analysis
•Frequency Analysis
•Recency, Frequency, Monetary Analysis
•(Correlation Analysis
•Pareto Analysis

14. Data Science Machine Learning

15. Data Science
https://teachablemachine.withgoogle.com/
Training, testing?
Train
Model
Testing
Split

16. Pandas Example
Indexing df = df.set_index(df['PassengerId’])
Drop missing value, df.dropna()

17. Seaborn example
sns.scatterplot(x='total_bill', y='tip', data=tips)

18. Use Case Marketing Analytics

19. Use Case Customer Analytics

20. Use Case User Analytics
Active User per Province
New User vs Churn user
Active User (by Payment)
New User by Regional
Churn User by Regional
Active User by LOS
Active User by City (Top 10)
Active User by Regional
Churn User by City (Top 10)
Active User by Group Age
Active User by Gender
Active User by Indihome Product Type
Active User by Urban Rural
Active User by Location Category

21. Use Case Churn Prevention
Strategy to Improve Customer Retention
1. Gather available customer behavior, transactions, demographics data and usage
patterns
2. Utilize these data points to predict customer segments who are likely to churn.
3. Create a model to pattern the risk tolerance of the business with respect to churn
probability.
4. Design an intervention model to consider how the level of intervention could affect
the churn percentages and customer lifetime value (CLV).
5. Implement effective experimentation across multiple customer segments for
reducing churn and promoting retention.
6. Rinse and Repeat from Step 1 (cognitive churn management is a continuous
process and not once a year exercise).
Customer
Historical Data
• Customer
Behavior
• Transactions
• Demographics
• Usage Pattern
Machine Learning
• Machine
Learning Model
• AI Model
• Deep Learning
Model
Churn Predictive
Model
• Probability
Risk
Churn Clustering
• High Risk
• Medium Risk
• Low Risk
Campaign
Planning
• Campaign
based on
Risk Level

22. Use Case Recommendation Engine