Python for Data Science is an in-depth, instructor-led program designed to guide learners from Python fundamentals to applied data science and machine learning techniques. The course is structured to help professionals and aspiring data practitioners develop the skills needed to analyze complex datasets, build predictive models, and make data-driven decisions using Python.
Python has become the leading language for data science due to its simplicity, flexibility, and powerful ecosystem of libraries. This course begins with a strong foundation in Python programming, ensuring learners understand core concepts such as variables, control flow, functions, and file handling before progressing into data manipulation and analysis.
As the course advances, participants gain hands-on experience using Pandas and NumPy to clean, transform, and analyze real-world datasets. Learners are taught how to handle missing data, perform aggregations, merge datasets, and efficiently process large volumes of information. These skills are essential for preparing data for analysis and modelling in real business environments.
What learners will gain from this course:
A solid understanding of Python programming fundamentals for data science
Practical skills in data manipulation and cleaning using Pandas
The ability to analyse and visualise data using Matplotlib and Seaborn
Experience applying statistical concepts and numerical computing with NumPy
Hands-on exposure to machine learning algorithms using Scikit-learn
The confidence to design and evaluate real-world data science projects
Key learning areas include:
Python Foundations for Data Science
Setting up Python environments with Anaconda and Jupyter Notebooks
Core programming concepts, functions, modules, and exception handling
Data Manipulation and Analysis
Working with Pandas Series and DataFrames
Cleaning, formatting, merging, and aggregating data
Reading and writing data from CSV and Excel files
Data Visualization
Creating meaningful charts and plots with Matplotlib
Using Seaborn for advanced statistical visualizations
Applied Data Science Projects
Building a recommendation engine using collaborative filtering
Creating a movie recommendation system similar to Netflix
Data sourcing, wrangling, correlation analysis, and visualization
Machine Learning with Python
Supervised and unsupervised learning concepts
Regression, classification, clustering, and dimensionality reduction
Model evaluation, validation, and hyperparameter tuning
Advanced Topics
Introduction to deep learning concepts
Time series analysis and basic natural language processing
The course also includes project-based learning, allowing participants to apply concepts to real-world datasets and develop a portfolio that demonstrates their analytical capabilities.
This program is suitable for learners with basic programming and mathematical knowledge who want to build practical, job-relevant data science skills. By the end of the course, participants will be equipped to analyze data, build predictive models, and contribute meaningfully to data-driven initiatives within their organizations.
Training Course Modules
Module 1: Introduction to Python for Data Science
Overview of Data Science and its Importance
Setting up Python Environment (Anaconda, Jupyter Notebooks)
Basic Python Syntax and Concepts
Variables, Data Types, and Operators
Control Flow (If statements, Loops)
Functions and Modules
Exception Handling
Working with Files
Module 2: Data Manipulation with Pandas
Introduction to Pandas
Series and DataFrames
Data Indexing and Selection
Data Cleaning (Handling Missing Data, Data Formatting)
File Operations (Reading and Writing CSV, Excel files)
Grouping and Aggregating Data
Merging, Joining, and Concatenating DataFrames
Module 3: Data Analysis and Visualization
Statistical Analysis Basics
Introduction to NumPy
NumPy Arrays
Array Operations and Broadcasting
Advanced Array Manipulations (Indexing, Slicing, Iterating)
Data Visualization with Matplotlib
Plotting Basics (Line plots, Bar charts, Histograms)
Customizing Plots (Labels, Legends, Colors)
Advanced Data Visualization with Seaborn
Statistical Plots (Box plots, Violin plots, Pair plots)
Module 4: Recommendation engine
Collaborative filtering
User based filtering
Item based filtering
Data collection and cleaning
Creating a movie recommendation system similar to Netflix
Sourcing data
Munging and wrangling data
Pivots and correlations
Hyperparameters
Visualization and presentation
Module 5: Introduction to Machine Learning
Machine Learning Concepts and Terminology
Types of Machine Learning Algorithms (Supervised, Unsupervised)
Module 6: Reinforcement Learning
Data Preprocessing for Machine Learning
Feature Engineering
Handling Categorical Data
Scaling and Normalization
Splitting Data into Training and Testing Sets
Module 7: Machine Learning with Scikit-learn
Overview of Scikit-learn
Implementing Regression Models
Implementing Classification Models
Clustering Techniques
Dimensionality Reduction
Model Evaluation and Validation
Cross-Validation
Performance Metrics (Accuracy, Precision, Recall)
Tuning Machine Learning Models (Grid Search, Random Search)
Module 8: Advanced Topics and Project Work
Introduction to Deep Learning and TensorFlow/Keras (Overview)
Time Series Analysis
Natural Language Processing (NLP) Basics
Project Work: Applying the learned concepts on real-world datasets to solve problems.
Move beyond spreadsheets. Use Python to analyze, visualize, and predict with confidence.
From data wrangling to machine learning — get hands-on with real projects that showcase your skills.
Build your portfolio and become the data-driven decision-maker every organization needs.
Course Overview
In the age of data, those who can extract insights hold the competitive edge. This 5-day instructor-led course equips learners with the Python programming skills needed to solve real data science challenges.
You’ll begin with foundational Python syntax, then move on to powerful tools like Pandas for data manipulation, Matplotlib and Seaborn for visualization, and Scikit-learn for machine learning. You’ll also work on a real-world recommendation system project and apply ML models to real datasets.
Ideal for aspiring data scientists and analysts, this course gives you both theory and practical exposure to the most in-demand Python libraries used in the field today.
Learning Objectives
Python fundamentals for data science
DataFrames and cleaning using Pandas
Numerical computing with NumPy
Statistical and advanced data visualization
Recommendation engine design
Supervised and unsupervised ML with Scikit-learn
Model evaluation and hyperparameter tuning
Basics of NLP, deep learning, and time series
End-to-end project execution with real-world data
Who Should Attend
Beginners in data science and analytics
Analysts and Excel users transitioning to code
Python developers exploring data applications
Business intelligence professionals seeking deeper analysis tools
Students or graduates building a data science portfolio
Prerequisites
Basic understanding of programming concepts such as variables, functions, and loops.
Familiarity with algebra and statistics is recommended.
Course Modules
Module 1: Python for Data Science
Set up your Python environment and learn the essentials: variables, data types, control flow, and file handling.
Module 2: Data Manipulation with Pandas
Work with Series, DataFrames, and clean messy datasets. Learn grouping, merging, and advanced operations.
Module 3: Analysis and Visualization
Use NumPy for computation and create insightful visuals with Matplotlib and Seaborn.
Module 4: Recommendation Engine
Design and build a movie recommendation system using collaborative filtering and correlation techniques.
Modules 5–6: Intro to Machine Learning
Understand ML types, preprocessing, feature engineering, and data splitting for model development.
Module 7: ML with Scikit-learn
Build regression, classification, clustering models, evaluate them, and tune hyperparameters for optimal performance.
Module 8: Advanced Topics & Project
Explore NLP, time series, and deep learning fundamentals. Apply everything through a capstone project on real data.
Professional Outcomes
This course supports roles such as Data Analyst, Python Developer for Data, Junior Data Scientist, or Business Analyst — empowering learners to build dashboards, models, and data products independently.
Certification Details
No specific exam for this course
Frequently Asked Questions
Is this course suitable for absolute beginners?
Yes. This course starts from the basics and gradually moves to advanced data science concepts.
Do I need to know machine learning beforehand?
No. The course covers the fundamentals of machine learning and explains them in a beginner-friendly way.
Are real datasets used in the training?
Yes. You will work with real-world datasets in exercises and projects.
Will I build a recommendation engine?
Yes. One module is dedicated to designing and implementing a recommender system.
Does this course include a capstone project?
Yes. The final module includes an applied project using the techniques taught throughout the course.
Are libraries like Pandas, Matplotlib, and Scikit-learn included?
Yes. The course focuses heavily on the most popular and powerful Python data libraries.
Is this course certification-based?
No. This is a skills-based course focused on project execution and hands-on proficiency.
Is this course HRDC claimable?
Yes. It is claimable under HRDC for eligible Malaysian employers.
Can this course be customized for our internal team?
Yes. GemRain offers both in-house and virtual delivery for organizations.
Will I get a certificate of completion?
Yes. You will receive an official GemRain certificate upon completing the course.