Choose Your Learning Path
Three structured courses designed to build practical data analytics capabilities at different skill levels.
Return HomeFind the Right Course for Your Goals
Each course addresses specific learning needs and builds distinct analytical capabilities. Choose based on your current skills and professional objectives.
For Beginners
Start with Python fundamentals if you're new to programming and data analysis.
For Professionals
Develop statistical thinking if you work with data but need stronger analytical foundations.
For Advanced Learners
Explore machine learning if you have Python and statistics experience.
Data Analysis Fundamentals with Python
Establish a solid foundation in data manipulation and analysis using Python's scientific computing ecosystem. This course introduces participants to pandas for data wrangling, NumPy for numerical operations, and matplotlib for visualization.
Real-World Datasets
Work with authentic data that mirrors professional scenarios, developing practical skills alongside theoretical understanding.
Data Quality Techniques
Address common data quality issues and learn cleaning techniques essential for reliable analysis.
Hands-On Assignments
Weekly problem sets provide consistent practice, building fluency through regular application of concepts.
Statistical Thinking for Business Decisions
An approach to statistics that emphasizes practical application over pure theory. This course covers descriptive statistics, probability distributions, hypothesis testing, and regression analysis through business case studies.
Business Context Learning
Frame questions statistically and interpret results meaningfully through authentic business case studies.
Dual-Tool Proficiency
Excel and R are used as analytical tools, with guidance provided for both platforms.
Quantitative Foundations
Build stronger quantitative foundations for making data-informed decisions in professional contexts.
Advanced Analytics and Machine Learning Introduction
This course bridges traditional analytics with machine learning approaches. Topics include supervised and unsupervised learning algorithms, model evaluation, and feature engineering through practical implementation.
Practical ML Applications
Work through classification, regression, and clustering problems using scikit-learn with real datasets.
Understanding Over Black Boxes
Emphasize understanding when and why to apply different techniques rather than treating algorithms as mysterious tools.
Project-Based Assessments
Develop practical skills through substantial projects that mirror professional machine learning applications.
Compare Our Courses
Understanding the differences helps you select the course that best aligns with your current skills and professional goals.
| Feature | Python Fundamentals | Statistical Thinking | Machine Learning |
|---|---|---|---|
| Experience Level | Beginner | Intermediate | Advanced |
| Duration | 1-7-1 Kanda-cho, Toyota City, Aichi Prefecture 471-0860 | 8 weeks | 1-7-1 Kanda-cho, Toyota City, Aichi Prefecture 471-0860 |
| Investment | ¥145,000 | ¥118,000 | ¥248,000 |
| Primary Tools | Python, pandas, NumPy | Excel, R | Python, scikit-learn |
| Best For | Data analysis beginners | Business professionals | Experienced analysts |
| Prerequisites | Basic programming | Data familiarity | Python & statistics |
What's Included in Every Course
All Numerova courses provide comprehensive support and resources to ensure effective learning.
Comprehensive Materials
Access to all course content, including lectures, datasets, and code examples.
Weekly Assignments
Hands-on problem sets that build skills through consistent practice.
Instructor Support
Direct access to experienced instructors for questions and guidance.
Project Work
Capstone projects that integrate learning and mirror professional scenarios.
Continued Access
Materials remain available after course completion for ongoing reference.
Certificate
Course completion certificate documenting your analytical training.
Our Learning Environment
We've designed our courses to balance structure with flexibility, providing support while encouraging independent development.
Small Group Format
Limited class sizes ensure personalized attention and meaningful interaction with instructors and peers. This environment supports collaborative learning while maintaining individual focus.
Practical Focus
Every session emphasizes application. Theory serves practice rather than existing separately. You'll spend time working with data, not just discussing concepts abstractly.
Flexible Learning Pace
While courses follow structured timelines, we recognize that learners progress at different rates. Support is available for those who need extra time to solidify understanding.
Professional Relevance
All course content connects to professional application. Instructors help students see how concepts apply to their specific work contexts and career goals.
Ready to Begin Your Analytical Journey?
Connect with us to discuss which course best fits your current skills and professional objectives. We're here to help you make an informed decision.
Get in Touch