Browse focused self-paced skill upgrades by tool, level, or outcome. Pick a Short Course when you want practical work, a clear project, and a Professional Certificate.
Short Course decision support
Use Short Courses to build a specific tool or workflow without committing to a full diploma. Compare the project, level, tools, and support options before deciding.
Start with high-impact Short Courses that can map into TechOga Professional Diplomas.
4 total Short Courses
Query databases, join tables, summarize records, and uncover business insights with SQL.
Learn the SQL skills data analysts use to extract, filter, join, group, and analyze data from relational databases.
Turn raw spreadsheets into clean analysis, useful reports, and business-ready insights.
Master the Excel skills used by data analysts to clean, organize, calculate, summarize, visualize, and report business data with confidence.
Build interactive dashboards and business reports that make performance clear.
Learn to connect, clean, model, measure, visualize, and present business data using Power BI.
Filter by school first, then refine by level, skill, tool, or outcome.
4 Short Courses shown
Try SQL, Power BI, Django, React, Python, or portfolio.
Query databases, join tables, summarize records, and uncover business insights with SQL.
Learn the SQL skills data analysts use to extract, filter, join, group, and analyze data from relational databases.
Turn raw spreadsheets into clean analysis, useful reports, and business-ready insights.
Master the Excel skills used by data analysts to clean, organize, calculate, summarize, visualize, and report business data with confidence.
Build interactive dashboards and business reports that make performance clear.
Learn to connect, clean, model, measure, visualize, and present business data using Power BI.
Clean, explore, analyze, automate, and visualize data with Python.
Learn Python for real analytics work: data cleaning, exploration, transformation, automation, and visual insight generation.
If you understand learning through courses, use this view to see the full curriculum behind each diploma. Some courses are available only inside the diploma because they depend on the projects, reviews, and outcomes around them.
Become a practical data analyst who can clean, analyze, visualize, and communicate business data using Excel, SQL, Python, Power BI, and real-world analytics projects.
Target role
Data Analyst, BI Analyst
Duration
12 months · 6–10 hours/week
Support
Choose your learning support level
Build the essential foundation for working with data, understanding business problems, and preparing for tools like Excel, SQL, Python, Power BI, machine learning, AI, and data engineering.
Master the Excel skills used by data analysts to clean, organize, calculate, summarize, visualize, and report business data with confidence.
Learn to connect, clean, model, measure, visualize, and present business data using Power BI.
Learn the SQL skills data analysts use to extract, filter, join, group, and analyze data from relational databases.
Learn Python for real analytics work: data cleaning, exploration, transformation, automation, and visual insight generation.
Apply Excel, SQL, Python, Power BI, and storytelling to complete end-to-end analytics projects for your portfolio.
Build practical data science and machine learning skills by learning Python, statistics, data preparation, model training, evaluation, interpretation, and end-to-end data science projects.
Target role
Data Scientist, Applied ML Engineer, Analytics Scientist
Duration
12 months · 6–10 hours/week
Support
Choose your learning support level
Build the essential foundation for working with data, understanding business problems, and preparing for tools like Excel, SQL, Python, Power BI, machine learning, AI, and data engineering.
Learn Python for real analytics work: data cleaning, exploration, transformation, automation, and visual insight generation.
Build the statistical foundation needed to understand data, measure uncertainty, test assumptions, interpret patterns, and prepare for machine learning.
Apply machine learning to realistic datasets through feature engineering, model selection, evaluation, tuning, interpretation, and project presentation.
Complete end-to-end data science projects that combine problem framing, data cleaning, exploration, statistics, visualization, modeling, evaluation, storytelling, and presentation.
