About this Event
Group Discounts:
- Save 10% when registering 3 or more participants
- Save 15% when registering 10 or more participants
Duration: 1 Full Day (9:00 AM – 5:00 PM)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, beverages, and light snacks included
Course Overview
The Python for Data Science course provides the essential skills to analyze, visualize, and interpret data using Python. You’ll learn to use popular libraries like Pandas, NumPy, Matplotlib, and Seaborn to explore datasets and uncover insights. Designed for aspiring data scientists and analysts, this course bridges core programming with data-driven decision-making — empowering you to start your journey in the world of data science.
Learning Objectives
By the end of this course, you will:
- Use Python libraries to load, explore, and manipulate data
- Apply data cleaning techniques to prepare data for analysis
- Create visualizations to convey patterns and insights
- Understand the basics of machine learning workflows
- Perform data-driven decision-making with confidence
- Start your journey toward becoming a skilled data analyst or scientist
Target Audience
Aspiring data scientists, analysts, developers, and students.
Why is it the Right Fit for You?
This course is designed for anyone ready to explore the world of data science using Python, regardless of prior experience. With expert guidance and real-world datasets, you’ll learn to make sense of data and present your findings visually. Our hands-on approach ensures you don’t just learn theory, but also apply it through practical exercises. Plus, Python is the most popular language in data science — learning it unlocks opportunities in AI, analytics, automation, and more.
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Agenda
Module 1: Introduction to Python for Data Science
Info: • Why Python is essential for data science
• Setting up Jupyter Notebook or VS Code
• Overview of data science workflow
• Activity
Module 2: Working with Data using Pandas
Info: • Introduction to DataFrames and Series
• Reading and writing CSV, Excel files
• Data exploration and basic stats
• Activity
Module 3: Numerical Computing with NumPy
Info: • Understanding arrays and vectorized operations
• Array creation, indexing, reshaping
• Using NumPy for basic calculations
• Case Study
Module 4: Data Cleaning Essentials
Info: • Handling missing data and duplicates
• Data type conversions and string operations
• Filtering, sorting, and transforming data
• Activity
Module 5: Data Visualization with Matplotlib
Info: • Plotting line, bar, scatter, histogram charts
• Customizing styles and labels
• Creating multiple plots in a figure
• Activity
Module 6: Advanced Visualization with Seaborn
Info: • Heatmaps, pair plots, categorical plots
• Using themes and palettes
• Visualizing data distributions
• Activity
Module 7: Introduction to Machine Learning Concepts
Info: • What is machine learning?
• Basics of supervised vs unsupervised learning
• Using scikit-learn for simple models
• Case Study
Module 8: Mini Data Project
Info: • Choose a dataset and perform analysis
• Clean, analyze, visualize, and present findings
• Share insights and interpretations
• Activity
Event Venue & Nearby Stays
Regus - Montreal, QC, 1200 Avenue McGill College, Montreal, Canada
CAD 616.10 to CAD 797.09
