【Python・初級者向け】ビジネスの現場で「すぐに使える」データ分析手法をわかりやすく解説

Learn Practical Data Analysis with Python for Business

Unlock the power of data-driven decision-making! This course provides a fast-track to mastering actionable data analysis techniques you can implement tomorrow in your business. We'll use the versatile programming language Python to equip you with the skills to solve real-world challenges.

No Python experience? No problem! We include a comprehensive introduction to Python fundamentals, making this course perfect for beginners. Learn Python alongside practical business data analysis.

What You'll Achieve:

• Become proficient in data analysis (machine learning) to solve business problems.
• Gain hands-on experience with Python for data analysis.
• Master Google Colaboratory (Google Colab), a powerful, free cloud-based platform for data science. No environment setup required!
• Develop the ability to leverage data for improved business insights.

Who is this course for?

• Business Professionals: Quickly acquire practical data analysis skills.
• Data Analysis Beginners: Learn Python and data analysis from the ground up.
• Non-Technical Managers & Executives: Understand data analysis concepts and apply them to your business.
• IT Professionals: Enhance your skillset with practical data analysis techniques.
• Anyone interested in Machine Learning: Get a practical introduction to ML concepts.

No prior programming experience is needed. We've designed this course to be accessible to everyone, from non-technical business leaders to those new to coding.

Course Sections:

• Python Fundamentals: A gentle introduction to Python, covering essential concepts from basic arithmetic to fundamental programming structures. Skip this section if you already have Python experience.
• Employee Turnover Prediction (Classification): Build a program to predict employee attrition based on factors like job role and age. Apply this technique to customer purchase prediction, defect detection, and other classification problems.
• House Price Prediction (Regression): Develop a program to forecast house prices using data like size and age. Extend this to predict various other numerical values, such as product pricing.
• Wholesale Customer Analysis (Clustering): Analyze customer purchase data and segment customers into distinct groups (clustering). Use this for targeted marketing and persona development.
• Recommendation System (Collaborative Filtering): Create a recommendation system that suggests movies to users based on the reviews of other users. Understand and implement recommendation engines like "Recommended for You" on e-commerce sites.

We're confident this course will empower you to drive success in your business and projects.