Exploratory Data Analysis (EDA) for Data Science and ML
Exploratory Data Analysis (EDA) is a vital first step for any data science or machine learning project. Learn how to perform effective EDA for regression and classification! In this beginner-friendly, hands-on project you learn how basic EDA can provide vital insights into your data, and how you can use this information to improve your models.
4.6 (103 Reviews)
Language
- English
Topic
- Data Science
Enrollment Count
- 628
Skills You Will Learn
- Python, Machine Learning, General Statistics, Data Science, Data Analysis, Pandas
Offered By
- IBMSkillsNetwork
Estimated Effort
- 45 minutes
Platform
- SkillsNetwork
Last Update
- May 17, 2024
A look at the project ahead
In this project, you learn:
- How to perform EDA using a set of very simple and easy-to-memorize Python commands
- How to interpret key EDA plots and statistics
- How to improve prediction models by using information obtained through EDA
- How to perform basics of feature engineering
- How to detect and handle outliers
- How to deal with missing data
What you'll need
Language
- English
Topic
- Data Science
Enrollment Count
- 628
Skills You Will Learn
- Python, Machine Learning, General Statistics, Data Science, Data Analysis, Pandas
Offered By
- IBMSkillsNetwork
Estimated Effort
- 45 minutes
Platform
- SkillsNetwork
Last Update
- May 17, 2024
Instructors
Wojciech "Victor" Fulmyk
Data Scientist at IBM
As a data scientist at the Ecosystems Skills Network at IBM and a Ph.D. candidate in Economics at the University of Calgary, I bring a wealth of experience in unraveling complex problems through the lens of data. What sets me apart is my ability to seamlessly merge technical expertise with effective communication, translating intricate data findings into actionable insights for stakeholders at all levels. From modeling to storytelling, I bring a holistic approach to data science. Leveraging machine learning algorithms, I construct predictive models tailored to both real-world challenges as well as old, well-understood problems. My knack for data-driven storytelling ensures that the insights uncovered resonate with both technical and non-technical audiences. Open to collaboration, I'm eager to take on new challenges and contribute to transformative data-driven endeavors. Whether you seek to extract insights, enhance predictive models, or explore untapped potential within your datasets, I'm here to help. Feel free to connect to me via my LinkedIn profile. Let's learn from each other!
Read moreContributors
Lucy Xu
Data Scientist
I am a Data Scientist Intern at IBM. I am also currently in my fourth year at the University of Waterloo studying Statistics with a minor in Computing.
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