A guided path from foundations to portfolio and internship readiness.
A complete career-focused program combining Python, SQL, statistics, ML, projects, mentoring, and job preparation.
Learning outcomes
Python / SQL / Statistics / Pandas / Power BI / Data Cleaning / Visualization / Machine Learning / Business Insights / Portfolio
Course syllabus
Explore the topics, practice activities, and outputs in each module.
10 modules / 16 weeks / Instructor-led
Explore the curriculum
Understand what Data Science is, how organizations use data, and how a Data Science project works from beginning to end.
Explore how data, statistics, technology, and business thinking work together.
Distinguish the purpose, methods, and outputs of related data disciplines.
Compare the responsibilities of analysts, scientists, BI specialists, and ML engineers.
Follow a project from business question and data collection to insight and action.
Build the Python programming foundation needed to work confidently with data.
Set up a practical workspace and write clear Python statements.
Store, inspect, and convert common values used in data work.
Organize related values with Python's core data structures.
Automate decisions and repeated data-processing tasks.
Create reusable logic with parameters, return values, and basic error handling.
Apply core Python skills to small, realistic data challenges.
Learn how to import, clean, transform, and analyze real-world datasets using the essential Python data toolkit.
Work efficiently with numerical arrays and mathematical operations.
Understand structured datasets using rows, columns, indexes, Series, and DataFrames.
Load common business data files into Python for analysis.
Identify and fix common data-quality problems.
Filter, sort, group, merge, reshape, and create calculated columns.
Use practical statistics to understand data, identify patterns, and support business decisions.
Summarize datasets with measures of center, spread, and position.
Measure and interpret how values vary around the average.
Use probability concepts to reason about uncertainty.
Recognize distribution shapes, normality, and unusual values.
Evaluate how variables move together without confusing correlation with causation.
Learn how to query, combine, and analyze business data stored in databases.
Understand relational data, tables, keys, rows, and columns.
Retrieve, filter, sort, and deduplicate useful records.
Calculate totals, averages, counts, minimums, and maximums.
Summarize business performance by useful dimensions.
Combine related datasets across multiple database tables.
Answer realistic stakeholder questions with complete SQL queries.
Learn how Data Scientists investigate datasets before building models.
Use a repeatable process to investigate unfamiliar datasets.
Assess dataset structure, completeness, quality, and consistency.
Explore value patterns and identify unusual observations.
Compare groups and discover meaningful changes over time.
Connect analytical evidence to clear business recommendations.
Turn complex data into clear charts and meaningful visual stories.
Design charts that communicate clearly and honestly.
Build and customize common analytical charts in Python.
Match chart forms to comparisons, trends, distributions, and relationships.
Guide an audience from evidence to a meaningful conclusion.
Create a polished visual summary from a realistic dataset.
Build interactive dashboards that help businesses monitor performance and make better decisions.
Understand how BI turns operational data into decisions.
Import, clean, and shape data through repeatable transformation steps.
Create useful relationships and a clear analytical model.
Build measures and calculated columns for business metrics.
Create interactive reports with KPIs, filters, and drill-down.
Deliver an executive-ready dashboard from a realistic brief.
Understand how Machine Learning uses historical data to make predictions.
Understand common prediction problems and the supervised learning workflow.
Define the information a model uses and the outcome it predicts.
Separate data for learning and honest performance evaluation.
Predict continuous values with foundational regression methods.
Predict categories with logistic regression and decision trees.
Interpret accuracy, precision, recall, confusion matrices, and overfitting.
Learn how modern AI tools can support Data Science work and improve analyst productivity.
Identify high-value uses of AI across modern data workflows.
Write precise prompts with context, constraints, and validation criteria.
Use AI to draft, explain, debug, and improve analytical code responsibly.
Accelerate exploration, visualization, and reporting while validating every result.
Projects
These course assignments use practice datasets and business scenarios. Build your own work with instructor guidance.
Foundation Project
Prepare a messy multi-source dataset for trustworthy analysis.
Deliverable: Clean dataset + quality report
Python / Pandas / Quality. Understand the brief, apply the course skills, validate the result, and document your findings.
Analytics Project
Answer management questions from a relational database.
Deliverable: Query pack + insights
SQL / JOIN / Aggregation. Understand the brief, apply the course skills, validate the result, and document your findings.
BI Project
Build an interactive dashboard for executive KPIs.
Deliverable: Portfolio dashboard
Power BI / DAX / Storytelling. Understand the brief, apply the course skills, validate the result, and document your findings.
ML Project
Train and explain a practical business prediction model.
Deliverable: Model notebook + evaluation
Features / Modeling / Metrics. Understand the brief, apply the course skills, validate the result, and document your findings.
Career Project
Turn technical work into a clear employer-ready story.
Deliverable: Published case study
Documentation / Presentation / GitHub. Understand the brief, apply the course skills, validate the result, and document your findings.
Final project
Combine analysis, querying, visualization, modeling, and communication in one portfolio centerpiece.
Problem / Data / Analyze / Model / Visualize / Present
The program runs for 16 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 builds on existing skills. Discuss your experience with an advisor before registering.
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.