Data science has become a core capability for organizations seeking competitive advantage through analytics, automation, and predictive modeling. Python is one of the most widely used programming languages in data science due to its powerful ecosystem of libraries and flexibility. This 5-day instructor-led program provides a comprehensive pathway from Python fundamentals to advanced machine learning implementation.
The Python for Data Science course begins with foundational programming concepts, ensuring participants understand variables, control flow, functions, file handling, and environment setup using tools such as Anaconda and Jupyter Notebooks. This establishes a strong base before moving into applied analytics.
Participants then focus on data manipulation using Pandas and numerical computing with NumPy. They learn how to clean, transform, merge, and aggregate datasets efficiently. Key capabilities covered include:
Handling missing data and formatting issues
Grouping and summarizing large datasets
Reading and writing CSV and Excel files
Performing advanced array operations with NumPy
The course progresses into data analysis and visualization using Matplotlib and Seaborn. Participants learn how to create meaningful charts, customize visualizations, and interpret statistical patterns to support business insights.
A highlight of the program is building a recommendation engine using collaborative filtering techniques. Participants explore data wrangling, pivot tables, correlations, and model tuning to create a movie recommendation system similar to streaming platforms.
The machine learning component introduces supervised and unsupervised learning concepts. Using Scikit-learn, participants implement:
Regression and classification models
Clustering techniques
Dimensionality reduction
Model evaluation using cross-validation and performance metrics
Advanced topics include introductory deep learning concepts, time series analysis, and natural language processing fundamentals.
The course concludes with project work, enabling participants to apply Python, machine learning algorithms, and visualization techniques to solve real-world data problems.
By the end of the program, participants will be able to:
Write Python code for data science applications
Manipulate and analyze complex datasets
Build and evaluate machine learning models
Develop portfolio-ready data science projects
Support data-driven decision-making initiatives
Frequently Asked Questions
Is this course HRDC claimable?
Yes. This course is HRDC claimable subject to approval and compliance with HRD Corp requirements. Organizations may apply for funding support according to HRDC guidelines.
Can this course be customized for our industry or datasets?
Yes. The course can be tailored to align with your organization’s datasets, industry use cases, and specific machine learning objectives.
Will I learn machine learning in this Python course?
Yes. The course covers supervised and unsupervised learning, regression, classification, clustering, dimensionality reduction, and model evaluation using Scikit-learn.
Do I need prior programming experience?
Participants should have a basic understanding of programming concepts such as variables, loops, and functions, along with basic mathematical knowledge.
Does this course include hands-on projects?
Yes. Participants complete practical exercises and project work, including building a recommendation system and applying machine learning to real-world datasets.