Full-Time 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 Apply Now
Part-Time 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 Apply Now
Weekend 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 Apply Now
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
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
or Data Analytics Essentials (DAE) 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
Introduction to Data Science
What is Data Science
Data Science Vs. Analytics
What is Data warehouse
Online Analytical Processing (OLAP)
Data Science and its Industry Relevance
Problems and Objectives in Different Industries
How to Harness the power of Data Science?
ELT vs ETL
Deep Dive into Python Programming
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
Importing / Exporting Data with Python
Importing Data into from Various sources
Database Input (Connecting to database)
Viewing Data objects – sub setting, methods
Exporting Data to various formats
Data Cleansing with Python
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)
Data Visualization with Python
Exploratory Data Analysis
Descriptive Statistics, Frequency Tables and Summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)
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
Introduction to Machine Learning
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
Understanding Predictive Analytics
Introduction to Predictive Modelling
Types of Business Problems
Mapping of Techniques
Segmentation – Cluster Analysis (K-Means / DBSCAN)
Decision Trees (CHAID/CART/CD 5.0)
Time Series Forecasting
Understanding A/B Testing Concepts
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
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.
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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