What you will learn in Prepare Data for Exploration Course
- Understand factors to consider when making decisions about data collection.
- Discuss the difference between biased and unbiased data.
- Describe databases with references to their functions and components.
- Describe best practices for organizing data.
Program Overview
Data Types and Structures
⏱️4 hours
Learn about structured and unstructured data, data types, and data formats.
Bias, Credibility, and Ethics
⏱️4 hours
Understand different types of bias in data and the importance of data ethics and privacy.
Databases: Where Data Lives
⏱️ 4 hours
Explore how analysts use spreadsheets and SQL within databases and datasets.
Organizing and Protecting Your Data
⏱️ 4 hours
Learn best practices for organizing data and keeping it secure.
Course Challenge
⏱️ 3 hours
- Apply the skills learned in a hands-on project to prepare data for exploration.
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Job Outlook
- Proficiency in data preparation is crucial for roles such as Data Analyst, Business Analyst, and Data Scientist.
- Skills acquired in this course are applicable across various industries, including technology, healthcare, finance, and more.
- Completing this course can enhance your qualifications for entry-level data analytics positions.
Specification: Prepare Data for Exploration
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FAQs
- It involves cleaning messy datasets by fixing errors and removing duplicates.
- Missing values are handled so they don’t affect the analysis.
- Data is organized into a structured format (tables, rows, columns).
- Preprocessing makes data reliable for visualization and machine learning.
- Basic Python knowledge is helpful, but not mandatory.
- You should be comfortable working with spreadsheets or CSV files.
- The course explains concepts step by step, making it beginner-friendly.
- No need for deep statistics knowledge — the focus is on practical skills.
- Python libraries like Pandas and NumPy for data handling.
- Tools for cleaning and transforming raw datasets.
- Methods to merge, filter, and sort large datasets efficiently.
- Practical use of spreadsheets and basic data visualization tools.
- Raw data is often incomplete or inconsistent.
- Errors in unprepared data can lead to wrong conclusions.
- Clean, structured data helps models and visualizations work correctly.
- Data preparation saves time in later stages of analysis.
- Ability to clean, organize, and format raw datasets.
- Understanding of how to handle missing or inconsistent values.
- Skills in using Python libraries for data exploration.
- Confidence in preparing data for visualization, dashboards, and machine learning.
