Python for Data Science
From Data to Decisions: Making Sense of the Numbers
Course Overview
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Course Outline
Day 1: Foundations of Data Science
Introduction to Data Science and Analytics
- Course Introduction
- Definitions and Terminologies
- Data Science Workflow
- Career Landscape
Project Management
- Packages Management with Conda
- Version Control with Git
- Introduction to Jupyter Notebooks
- Hands-On: Setting up your project
Python Essentials
- Refresher: Data Structures and Control Flow
- Functional Programming
- Pipeline Workflow with Comprehension
- Code Review: Common Code Snippets
- Hands-On: Exercises in Data Processing
Introduction to Pandas
- Pandas Data Structures (Series and DataFrame)
- Data Slicing and Filtering
- Fundamental Operations and Data Manipulation
- CBasic Data Visualization and Matplotlib and Plotly
- Hands-On: Employee Engagement Report
Day 2: Data Processing
Introduction to Data Analysis
- Descriptive Statistics (Central Tendency, Variability, Frequency, Distribution)
- Variable Relationship (Covariance, Correlatiton, etc.)
- Choosing the right visualization
- Case Studies: Analyzing Public Datasets
- Hands-On: Job Market Trend Analysis
Fundamentals in Probability Theory
- Probability Distribution in Real-world Applications
- Hypothesis Testing
- Case Studies: Review of Research papers
- Hands-On: Research Analysis
Feature Engineering with Numpy
- Introduction to Tensors
- Feature Shaping and Transformation
- Feature Creation and Selection
- Case Studies: Analyzing Large Datasets
- Hands-on: Dataset Feature Extraction
Day 3: Intermediate Techniques
Regression Models
- Linear Regression Model and Metrics
- Logistic Regression Model and Metrics
- Case Studies: Regression on Public Datasets
- Hands-On Exercises: Performance Predictions
Introduction to Machine Learning
- Conceptual Discussion and Terminologies
- Custom Model Creation and Design Considerations
- Case Study: Fraud Detection
- Hands-On Exercises: Image Classification
Text Analysis
- Introduction to Regular Expressions
- Natural Language Processing Techniques
- Using SpaCy and NLTK for Text Processing
- Case Study: Exploring common use cases
- Hands-On: Sentiment Analysis on a Text Dataset
Day 4: Data Presentation
Intermediate Data Visualization
- Customization and Layout Options for Plotly
- Multivariate Data Visualization Techniques
- Data Time and Multi-Index Handling
- Code Review: Common Code Snippets
- Hands-On: Data Visualization Challenges
Introduction to Streamlit
- Description and Motivation
- Streamlit Output Components (Text, Buttons, Sliders, Plotting, etc.)
- Streamlit Input Components (Radio Buttons, Text Input, Select Box, etc.)
- Advance Components (Tabs, Cache, State, Markdown, HTML, etc.)
- Hands-On: Data Analysis Dashboard
Storytelling
- Connecting with the Audience
- Handling Structure and Complexity
- Best Practices and Techniques
- Hands-On: Crafting a story from analysis results
Final Project
- Developing a Complete Report Dashboard from scratch
- Open discussion, questions, and further resources
- Closing Remarks
Frequently Asked Questions
What are the prerequisites for this course?
- Basic Computer knowledge and Skills in PC or Mac.
- Must have completed the Python Programming Essentials Course.
- Basic knowledge of mathematics and statistics is an advantage though not required.
What's included after completing the training?
- Certificate of course completion
- Training references (PDF)
- Exercise materials
- Some add-ons
- Free trainer consultation
- Unlimited Free Retakes
How to avail the Unlimited Free Retakes?
Unlimited Retakes = Unlimited Hours of Learning!
We understand that individuals have different learning styles and paces, that’s why we offer the opportunity to learn at your own speed. If you need more time to grasp a concept, you are welcome to come back and retake the class at no additional cost. We believe in providing students with the support they need to succeed.
To Avail: Finish the course to qualify for the unlimited refresher classes
Validity: 1 Year
Who should enroll in this Python for Data Science training?
This course is designed for those who want to turn raw data into actionable insights using Python. It is highly recommended for:
- Beginners in Data Science: Start with a solid foundation in data manipulation, visualization, and statistical analysis.
- Aspiring Data Scientists & Analysts: Master the essential libraries like Pandas, NumPy, and Matplotlib used in professional data-driven roles.
- Software Developers: Expand your skillset into machine learning and predictive modeling to build smarter applications.
- Business Professionals & Entrepreneurs: Learn to leverage data to automate reports and make better, data-backed business decisions.
- Career Shifters: Transition into the high-demand field of analytics with practical, hands-on data science skills.
What career paths are available after this training?
Data is the “new oil” of the modern economy. Completing this training prepares you for high-paying roles in industries ranging from finance to healthcare:
- Data Analyst: Interpret complex datasets and create visual reports to help organizations solve business problems.
- Junior Data Scientist: Build predictive models and use machine learning algorithms to forecast trends and outcomes.
- Business Intelligence (BI) Developer: Design and manage data systems that provide critical insights for corporate strategy.
- Data Engineer: Develop and maintain the pipelines that transform raw data into usable formats for analysis.
- Machine Learning Specialist: Focus on creating AI-driven systems and algorithms that learn and improve from data.
Enroll 4 students and get an extra ₱500/person off, on top of existing discounts.
- Basic to Advanced Coverage
- Workstations are Provided
- Certificate of Completion
- Training Materials and References
- Free Wifi Access
- Free Unlimited Coffee
- Free Trainer Consultation
- Free Unlimited Class Retakes
APRIL 2026
About the Registration
Feel free to register! Registering does not commit you to paying for the course immediately. Registration helps us track attendee numbers and enables us to stay in touch. Payment will only be requested once the course is confirmed to proceed.
Note: When choosing a schedule, you are limited to the available training dates posted on our website.