Bridge the gap between data and decision-making — become a hands-on data science practitioner.
Learn how to solve real-world business problems using machine learning, Python, and effective storytelling.
Move from insights to implementation — and earn a globally recognized data science certification along the way.
Course Overview
In today's data-driven world, organizations need more than dashboards — they need practitioners who can wrangle data, build models, and communicate insights with clarity and confidence.
This 5-day instructor-led course prepares learners to implement end-to-end data science projects using Python, SQL, and key machine learning techniques. You'll cover everything from initiating business-aligned projects, extracting and transforming data, building classification and regression models, to deploying and monitoring production pipelines.
Aligned to the CertNexus DSP‑210 certification, this course emphasizes business value, ethical practices, and hands-on application — preparing participants to become real contributors in any data team.
Learning Objectives
Initiating and scoping data science projects
Performing ETL (extract, transform, load) operations
Data visualization and preprocessing techniques
Designing and testing ML hypotheses
Building classification, regression, and clustering models
Evaluating model performance with appropriate metrics
Communicating insights to stakeholders
Deploying, testing, and monitoring ML pipelines in production environments
Who Should Attend
Developers and analysts transitioning into data science roles
BI professionals expanding into predictive analytics
Programmers with Python/SQL experience who want to apply machine learning
Business-driven professionals looking to lead applied AI/data projects
Prerequisites
Experience with Python (NumPy, pandas) and basic SQL querying
High-level understanding of data science roles and lifecycle
Recommended: Completion of CertNexus DSBIZ (DSZ‑110) or equivalent exposure to foundational AI concepts
Course Modules
Module 1: Addressing Business Issues with Data Science
Initiate projects, define business value, and formulate problem statements.
Module 2: Extracting, Transforming, and Loading Data
Work with raw data: extract, clean, transform, and load using Python.
Module 3: Analyzing Data
Visualize distributions, identify patterns, preprocess, and ready data for modeling.
Module 4: Designing a Machine Learning Approach
Define hypotheses, select algorithms, and understand model suitability.
Module 5: Developing Classification Models
Train, tune, and evaluate classifiers like logistic regression and decision trees.
Module 6: Developing Regression Models
Predict continuous values using linear models and assess accuracy.
Module 7: Developing Clustering Models
Use unsupervised learning (e.g., k-means) to find patterns in unlabeled data.
Module 8: Finalizing a Data Science Project
Present findings, build basic web apps, and deploy models in test environments.
Public Class Details
Professional Outcomes
This course prepares learners for roles such as Data Science Practitioner, Machine Learning Developer, or Applied Data Analyst — capable of building models, translating results, and supporting data-driven transformation.
Certification Details
Overview
As a candidate for this certification, you:
Scope business problems and align them with appropriate machine learning strategies
Prepare datasets via ETL processes and exploratory analysis
Apply supervised and unsupervised learning algorithms in Python
Evaluate and communicate model performance
Deploy and monitor models while maintaining ethical and privacy compliance
You are expected to be proficient with:
Python libraries for data science (NumPy, pandas, scikit-learn)
ML modeling techniques for classification, regression, and clustering
Business translation of technical outputs
Basic SQL for data extraction and manipulation
Skills Measured
Solve Business Problems Using Data Science
Extract, Transform, and Load Datasets
Analyze and Visualize Data
Build and Evaluate:
- Classification Models
- Regression & Forecasting Models
- Clustering ModelsCommunicate Insights and Model Outputs
Deploy and Monitor Production Models
Address Ethics, Bias, and Governance in Data Projects
Certification Logistics
Exam Code: DSP‑210
Format: Multiple-choice; 90 items total (75 scored)
Duration: 120 minutes
Passing Score: 72% (score required may vary)
Delivery: Pearson VUE (in-person or online proctored)
Credential: CertNexus CDSP – Certified Data Science Practitioner
Frequently Asked Questions
Is this a beginner-level course?
No. This course is designed for intermediate-level learners with some Python and data handling experience.
Will I be building models hands-on?
Yes. The course includes labs for regression, classification, and clustering with Python.
Do I need to know statistics or math?
A working understanding of applied stats helps, but the course focuses on practical model implementation.
Is this course certification-aligned?
Yes. It maps directly to the CertNexus DSP‑210 exam blueprint.
Do I need prior AI experience?
No. Completion of CertNexus DSBIZ or similar awareness-level courses is recommended but not required.
Is deployment and production monitoring covered?
Yes. Module 8 focuses on testing, web integration, and model pipeline awareness.
Is this HRDC claimable?
Yes. It is fully claimable under HRD Corp for eligible Malaysian employers.
Can I run this as a private training for my analytics team?
Yes. GemRain offers in-house and virtual delivery options for corporate clients.
Will I get a certificate of completion and exam prep support?
Yes. You will receive a GemRain certificate, and the course includes exam-aligned content and practice.