Live instructor-led programLearn Python by solving real data problems and building portfolio-ready work.
Work with real-world style business datasets, clean and analyze raw data with Pandas, create professional visualizations, discover insights, and complete portfolio-ready projects.
Learning outcomes
Python / Pandas / NumPy / Jupyter Notebook / Data Cleaning / EDA / Matplotlib / Business Insights
Course syllabus
Explore the topics, practice activities, and outputs in each module.
10 modules / 8 weeks / Instructor-led
Explore the curriculum
Set up your Python workspace and understand how Python is used in modern data analysis.
Understand the learning path, projects, tools, and expected outcomes.
Prepare a reliable environment for notebooks and Python scripts.
Write, run, and inspect simple Python statements.
Connect Python tools to common data-analysis tasks.
Learn how Python stores, transforms, and validates the values used in analytical programs.
Create clear variables that make analytical code easy to understand.
Calculate totals, rates, percentages, and other business measures.
Clean, combine, search, and format text values.
Build logical checks for filtering and validation.
Convert incoming values safely between common Python types.
Organize related values efficiently using Python's essential built-in collections.
Store, update, sort, and analyze ordered collections.
Use fixed collections and efficiently manage unique values.
Represent records and look up values by meaningful keys.
Select precise elements and ranges from collections.
Work with realistic records containing multiple levels of data.
Control program behavior and automate repetitive data-processing work.
Make decisions using if, elif, and else conditions.
Process records and collections with repeatable logic.
Repeat tasks while a defined business condition remains true.
Control complex loop behavior safely and clearly.
Apply conditions and loops to detect invalid values.
Write organized, reusable Python functions for cleaner analytical workflows.
Package repeated logic into clear reusable blocks.
Make functions flexible for different datasets and values.
Produce useful outputs and manage variables predictably.
Use naming, docstrings, and structure to improve maintainability.
Build reusable functions for KPIs, discounts, and performance checks.
Read external data, handle common failures, and extend Python with trusted packages.
Work safely with local text and structured data files.
Organize notebooks, scripts, data, and output files.
Use try and except to manage predictable failures.
Reuse Python's standard library and third-party tools.
Build a workflow that validates files before analysis.
Use fast numerical arrays and vectorized operations to prepare quantitative data.
Create and inspect efficient numerical arrays.
Select and reshape multidimensional numerical data.
Replace slow loops with efficient array calculations.
Calculate totals, averages, ranges, and other summaries.
Analyze a realistic set of quantitative business records.
Import, explore, filter, and summarize real-world tabular datasets with Pandas.
Understand the main structures used for tabular analysis.
Load common business datasets and inspect their structure.
Find the exact records needed to answer a question.
Create useful metrics and categories from existing fields.
Summarize performance by customer, product, region, or time.
Reshape summaries for easier comparison and reporting.
Turn messy source data into a reliable, analysis-ready dataset.
Profile data to identify missing, invalid, and inconsistent values.
Choose appropriate strategies for incomplete and repeated records.
Standardize fields so calculations and comparisons work correctly.
Merge, join, and concatenate related data sources.
Prepare a final analytical table and verify its quality.
Communicate findings with clear charts and complete a portfolio-ready Python analysis.
Choose charts that accurately answer analytical questions.
Create and customize common business visualizations.
Connect charts, evidence, and recommendations in a clear narrative.
Clean, analyze, and visualize a realistic business dataset.
Improve documentation, presentation, and final project quality.
Projects
These course assignments use practice datasets and business scenarios. Build your own work with instructor guidance.
Each project combines code, analysis, and an explanation of the results. The previews below use illustrative practice data.
Build a Python program that summarizes monthly sales performance for a small business.
Final output: Python script + sales KPI summary
You will create
Transform a messy customer dataset containing missing values, duplicates, inconsistent categories, and invalid fields.
Final output: Cleaned dataset + data-quality report
You will create
Analyze company sales data to identify growth trends, strong products, weak regions, and management opportunities.
Final output: Notebook + charts + KPI summary + recommendations
Example output students learn to create with Python
Explore which customer segments and contract types are associated with higher subscription churn.
Final output: Churn summary + segment chart + retention insight
Example output students learn to create with Python
Compare digital marketing channels and recommend where the next campaign budget should be focused.
Final output: Campaign notebook + KPI analysis + channel recommendation
Example output students learn to create with Python
Python is the tool. Solving real data problems is the goal.
Final project
Complete the entire data analysis process using a realistic business dataset.
Raw Data / Clean / Explore / Analyze / Visualize / Insight / Recommend
The program runs for 8 weeks, with Khmer and English instruction. Contact our team for the next start date, weekly timetable, and available places.
Ask about the next classLearn through guided explanations, class exercises, and feedback on your work. Your advisor can confirm the instructor and teaching schedule for your class.
Complete the required coursework and final project review to receive your Data Insight Cambodia certificate. Your projects help demonstrate the skills behind it.
Yes. Classes are instructor-led. After enrollment, Student Portal access provides class recordings, materials, assignments, and feedback to support your study between sessions.
This course starts with the foundations. An advisor can help you confirm the preparation you need.
A course certificate is provided after you complete the required assignments and final project review.
No. We provide career guidance and project feedback. Employment and internships depend on available opportunities and employer requirements.
Contact an advisor
Ask about this course, class times, or how to register.