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Executive Education
Clariden Leadership Institute
Python For Data Science (Singapore)
Philippe Touati
Belmond Capital
Philippe Touati, Belmond Capital


  • Experienced Finance and Banking leader with over 30 years of experience in the banking and telecommunication industry
  • Former Head of Institutional Banking for ANZ and Managing Director/Co-head of Standard Chartered Bank Singapore, Wholesale Bank
  • Regular speaker and trainer in Deep Tech, Advanced Data Analytics and AI-based digital transformation frameworks

Philippe builds and finances AI ventures offering automation, optimization, personalization and virtualization solutions to enable enterprises’ digital transformation. Previously, he was Head of Institutional Banking at ANZ and MD/Co-Head of the $1 billion revenue Wholesale Bank at Standard Chartered Bank Singapore. His financial services experience also includes running Capital One Bank’s European operations functions.


Before financial services, Philippe held senior leadership positions in telecoms and technology where he designed a VLSI integrated circuit at Bell Labs, launched operations of New T&T, a HK telecom start-up, ran IT and operations for ICO, a satellite MEO company and managed 5 operation centres at France Telecom.


Philippe has a Master of Science in Mathematics from Ecole Polytechnique as well as Masters of Science in Electrical Engineering from both Telecom ParisTech and Columbia University.



"Philippe’s course has broadened my appreciation for AI. It gave me some keywords that I can drop in meetings to make me sound good!" - Victorian Building Authority


"I have learnt about some new techniques and ways on how they can be applied. Also heard from the group on the sort of problems they face and suggestions from Philippe on how they can be solved." - Tower Insurance

Program Summary


Data Science with Python is designed to give you practical insights on industry-standard data analysis and machine learning tools in Python. You will discover how to execute predictive trend analysis with Python and build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms based on supervised and unsupervised machine learning using scikit-learn package


The program will help you understand how you can use Pandas and Statsmodels to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will also  discover how to tune the algorithms to provide the best predictions and uncover hidden patterns and relationships to aid important decisions and predictions.


By the end of this program, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

Programs, dates and locations are subject to change. In accordance with Clariden Global policy, we do not discriminate against any person on the basis of race, color, sex, religion, age, national or disability in admission to our programs.



Our Advanced Data Analytics and AI Transformation expert Philippe Touati will walk you through practical insights on using Python libraries for segmenting customer groups, modelling trends, scoring potential outcomes and forecasting to solve real business problems.


You will take away knowledge of key models and concepts of different Python libraries, data science tools and practical Python implementation of regression, time series, clustering and classification algorithms. In addition, you will discover how different data science models such as ARIMA, logistic regression, seasonal decomposition and more help your organization visualize data meaningfully.


What You Can Expect


What You Will Obtain After This Program

  • Demystify the use of Data Science with Python to provide answers to key business questions
  • Gain practical insights on data science and modelling through real-world case studies
  • Discover the best uses of different data science tools with practical Python implementation of regression, time series, clustering and classification algorithms
  • Learn how different models (ARIMA, logistic regression, seasonal decomposition) work and are implemented using Python libraries

Who Will Benefit Most


This Executive Program Is Designed For:


This course is suitable for Executives, Managers, Directors, Head of Departments and C-Suites in (but not limited to) data science & engineering, solution architects, business intelligence and analytics, information technology, artificial intelligence and software development, as well as practitioners who wish to gain deeper understanding and knowledge in using data with Python tools.


Program Outline


Day 1


Case Study 1: Trend Analysis With Python

  • Set up the right python environment for visualization of graph data
  • Trend analysis with statistical control limits
  • Python modules: Pandas, NumPy, Matplotlib, Jupyter Notebook

Session 1: Enhancing Business Performance With Data Science

  • Data modelling as a source of competitive advantage in a data driven world
  • Types of data modelling and forecasting: Regression, Time Series, Clustering, Classification 

Case Study 2: Housing Price Forecasting: Non-Model vs Model Based Regression Analysis 

  • Instance based forecasting: K-Nearest Neighbors
  • Multivariate linear regression - Supervised machine learning for data model based forecasting
  • Choosing the right metrics RMSE and coding it
  • Python modules: Statsmodels, Scikit-learn, Pandas

Session 2: Data Science Process For Predictive And Prescriptive Analytics

  • Components of a data model
  • Selecting data and machine learning model for generalization: Bias and variance trade-off, overfitting

Session 3: Time Series Forecasting Based On Supervised Machine Learning

  • Exponential smoothing windows
  • Moving average and autoregressive model
  • Data stationarity review with AD Fuller test
  • ACF and PACF times series reviews

Case Study 3: Forecasting Demand: Time Series Forecasting Using Statsmodels

  • Practical (S)ARIMA
  • Forecasting testing data model with ARIMA and state space models
  • Comparing models with the right metrics: RMSE, AIC, QoQ plot
  • Python modules: Statsmodels, Pandas


Day 2


Session 4: Data Transformation

  • ETL process
  • Data representation
  • Features selection/extraction

Session 5: Clustering Based On Unsupervised Machine Learning

  • Steps to build the right clusters, anomaly detection or segmentation
  • K-Means, Hierarchical Clustering, DBScan
  • Model assessment

Case Study 4: Customer Segmentation Using Clustering Algorithms

  • Principal component analysis to reduce dimensionality
  • K-Means algorithms applied to customer segmentation
  • Python modules: Scikit-learn, Pandas

Session 6: Classification Forecasting Based On Supervised Machine Learning

  • Build the right classification models
  • Logistic regression
  • Decision Tree, Boosted Tree and Random Forest
  • Loss function metrics and X-entropy

Case Study 5: Scoring For Predictive Maintenance/ Lead Scoring/ Fraud Detection Using Classification

  • Class imbalance challenges
  • Data model/ algorithm comparison and selection
  • Assessing with the right metrics: Precision/ Recall/ Accuracy/ AUC
  • Python module: Scikit-learn

Case Study 6: Complex Multi-Step Forecasting Combining Various Data Modelling Approaches 

  • Forecasting customer profitability, supply chain inventory management or fraud detection
  • Python modules: Pickle, Joblib

Conclusion: How To Kickstart Data Model Based Forecasting In Your Organization


CFOs Leadership :
Experience Clariden
Discover how our leadership program has shaped the perspectives of CFOs across Asia
Venue: Shangri-La Hotel Singapore
Date: 14 – 15 July 2020
Faculty: Philippe Touati
Early Bird 1: S$2,795 (by 22 May 2020)
Early Bird 2: S$2,895 (by 19 Jun 2020)
Regular Fee: S$3,095
Group Discount: 2nd participant get 10%, or register 3 participants and 4th participant get a complimentary seat
(1 discount scheme applies)
Note: GST is applicable to participants from Singapore registered companies.
Contact: [email protected]
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