データサイエンスのための実践Pandas

Master Pandas & Data Visualization for Powerful Data Analysis

Unlock the Power of Data with Pandas

Pandas is a cornerstone library for data analysis in Python. This course equips you with the essential skills to leverage Pandas' powerful DataFrame functionality for data pre-processing, aggregation, and visualization. Developing strong Pandas skills directly translates to enhanced data analysis capabilities. While Pandas offers extensive functionality, mastering it requires focused knowledge and practical tips.

Designed for Professionals: Elevate Your Data Skills

For HR professionals and managers: This course empowers you to elevate data analysis skills across your organization.

• Boost Data Analysis Skills: Equip your DX (Digital Transformation) teams with the analytical capabilities they need.
• Develop Data Scientists: Train data scientists to handle growing data volumes and deliver practical, real-world insights.

Upon completion, you'll be able to effectively aggregate and visualize internal data, fostering a team of proficient data analysts.

Course Objectives: Focused Learning for Maximum Impact

This course strategically focuses on Pandas and data visualization, delivering a concentrated learning experience. By narrowing the scope, we ensure you efficiently acquire the core skills needed for effective data analysis. Practical exploratory data analysis (EDA) exercises solidify your understanding and enable you to apply learned concepts to real-world scenarios.

Who Should Take This Course? & What You'll Achieve

This course is geared towards intermediate-level learners with a solid understanding of basic Python syntax. By the end of this course, you'll possess practical-level Python data analysis skills. We emphasize real-world code examples and tips applicable to common data analysis situations. Transform from someone who uses Pandas to someone who masters it.

Course Overview: A Structured Learning Path

The course is divided into three key sections:

• Pandas Fundamentals & Advanced Techniques: Start with the basics of DataFrames and progress to advanced techniques including:
  • Data Selection & Extraction: Efficiently retrieve specific data subsets.
  • Data Generation, Deletion & Manipulation: Create, modify, and clean your data.
  • Data Pre-processing: Handle data types, missing values (Null values), and duplicate entries.
  • Data Aggregation: Master Groupby, pivot_table, and melt operations.
  • Data Merging: Combine data from multiple sources.
• Data Visualization: Learn to effectively visualize data using industry-standard libraries like matplotlib, seaborn, and plotly. We prioritize visualizations commonly used in practical data analysis.
• Exploratory Data Analysis (EDA): Apply your newly acquired skills to real-world data. This section involves practical data pre-processing, aggregation, and visualization using actual log data. Gain valuable insights by experiencing the entire data analysis workflow.

Key Course Features:

• Hands-on Learning: This is a fully hands-on course where you'll code alongside the instructor, replicating real-world data analysis workflows step-by-step.
• Real-World Data: We utilize diverse datasets from various industries, including sales transaction data, real estate transaction logs, and service usage logs. Learn data pre-processing techniques from start to finish.
• Practical Skills: Gain proficiency in data pre-processing, aggregation, and visualization – essential skills for any data analyst.