Advanced Data Science Professional with Python (ADSP)

Upcoming Batch
9:30am to 5:30pm
Singapore Timezone

5 Sessions / 40-Hours

28 Jun to 2 Jul . 26 to 30 Jul
23 to 27 Aug . 27 Sep to 1 Oct
25 to 29 Oct . 22 to 26 Nov
27 to 31 Dec

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Weekday (Every Tuesday)
2:00pm to 6:00pm
Singapore Timezone

10 Sessions / 40-Hours

1 Jun to 3 Aug . 10 Aug to 12 Oct
19 Oct to 21 Dec

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Every Saturday
9:30am to 5:30pm
Singapore Timezone

5 Sessions / 40-Hours

12 Jun to 10 Jul . 17 Jul to 14 Aug
21 Aug to 18 Sep . 25 Sep to 23 Oct
30 Oct to 27 Nov

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Course Information
  • Duration: 5 Day / 40 Hours

  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination

  • Who Should Attend: Professionals or Anyone interested in pursuing a career as a data scientist and use data to understand the world, uncover insights, and make better decisions

Course Objective

Acquire advanced knowledge on how to use Data Science with Python Programming to uncover business insights and trend.

Learn how to use algorithms and basic Artificial Intelligence / Machine Learning techniques to make predictions.


It is preferred that participants successfully completed and pass Data Analytics Essentials (DAE) or Python Programming Essentials (PPE)


Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Data Science and Python Programming based on the syllabus covered

Module 1 Introduction to Data Science

Topics Covered
  • What is Data Science
  • Data Science Vs. Analytics
  • What is Data warehouse
  • Online Analytical Processing (OLAP)
  • MIS Reporting
  • Data Science and its Industry Relevance
  • Problems and Objectives in Different Industries
  • How to Harness the power of Data Science?
  • ELT vs ETL

Module 2 Deep Dive into Python Programming

Topics Covered
  • Python Editors & IDE
  • Custom Environment Settings
  • Basic Rules in Python
  • Most Common Packages / Libraries in Python (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations – Mathematical – string – date
  • Reading and writing data
  • Simple plotting/Control flow/Debugging/Code profiling

Module 3 Importing / Exporting Data with Python

Topics Covered
  • Importing Data into from Various sources
  • Database Input (Connecting to database)
  • Viewing Data objects – sub setting, methods
  • Exporting Data to various formats

Module 4 Data Cleansing with Python

Topics Covered
  • Cleaning of Data with Python
  • Steps to Data Manipulation
  • Python Tools for Data manipulation
  • User Defined Functions in Python
  • Stripping out extraneous information
  • Normalization of Data and Data Formatting
  • Important Python Packages e.g.Pandas, Numpy etc)

Module 5 Data Visualization with Python

Topics Covered
  • Exploratory Data Analysis
  • Descriptive Statistics, Frequency Tables and Summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)

Module 6 Statistics Fundamentals

Topics Covered
  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks (Probability Distributions, Normal distribution, Central Limit Theorem)
  • Inferential Statistics (Sampling, Concept of Hypothesis Testing)
  • Statistical Methods: Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi-square
  • Statistical Methods: ANOVA
  • Statistical Methods: Correlation and Chi-square

Module 7 Introduction to Machine Learning

Topics Covered
  • Statistical Learning vs Machine Learning
  • Iteration and Evaluation
  • Supervised Learning vs Unsupervised Learning
  • Predictive Modelling – Data Pre-processing, Sampling, Model Building, Validation
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Cross ValidationTrain & Test, Bootstrapping, K-Fold validation etc

Module 8 Understanding Predictive Analytics

Topics Covered
  • Introduction to Predictive Modelling
  • Types of Business Problems
  • Mapping of Techniques
  • Linear Regression
  • Logistic Regression
  • Segmentation – Cluster Analysis (K-Means / DBSCAN)
  • Decision Trees (CHAID/CART/CD 5.0)
  • Time Series Forecasting

Module 9 Understanding A/B Testing Concepts

Topics Covered
  • Introduction to A/B Testing
  • Measuring Conversion for A/B Testing/li>
  • T-Test and P-Value
  • Measuring T-Statistics and P-Values using Python
  • A/B Test Gotchas
  • Novelty Effects, Seasonal Effects, and Selection of Bias
  • Data Pollution

Advanced Data Science Professional (ADSP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions

What Past Participants Say
CASUGOL training helps me have a better understanding on how data analytics is being used in different areas for every business.

Eunice Chua Wee Ting

The topics and practical examples are great! CASUGOL trainer highlighted how the technology can be used. CASUGOL training is also delivered in a fun manner.

Preeda Payattakool


Customization of Programs for specific industry, organisation, government agencies, statutory boards.

Flexible programmes designed to cater to the individual needs of participants, whether for professional upskilling, or for general interest.

Benefit from contribution from leading Industry Experts, Academics, and Researchers from across the world.

Opportunities for employers to develop their workforce and for individuals to enhance their career.

Dynamic learning environment that providing participants with professional networking opportunity.

Online support for participants after the training.

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