Exploratory Data Analysis
Uncover hidden insights and make data-driven decisions! Our Exploratory Data Analysis (EDA) course equips you with powerful statistical techniques and data visualization skills. Gain a clear understanding of your data and identify potential issues before diving into analysis.
No Time Limit
Submit evidence anytime, no deadlines
Self-Paced
Progress at your own pace and schedule
2 Submission Quotas
Revised your submission with feedback
Competency
Learners are able to summarize the characteristics of a dataset with multiple variables using statistical methods and data visualization.
How ?
Learners are able to summarize the characteristics of a dataset by applying univariate, multivariate, and non-graphical statistical methods, along with suitable data visualization techniques.
Method overview
Exploratory Data Analysis (EDA) includes the basic steps as follows;
- Step 1: Data Acquisition
- Step 2: Exploratory Data Analysis (EDA)
- Step 3: Data Analysis
- Step 4: Data Visualization
How can you achieve ?
You will demonstrates your ability to analyze data effectively by explore a dataset using Exploratory Data Analysis (EDA) techniques, present insights of your findings, and document your process.
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Guidelines are now available in English
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Unlimited Access to Competency Guidelines
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2 Submission Quotas to Verify Competency
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Personalized Feedback
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Digital Badge
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Shareable to Social Network
Learning unit will be coming soon!
Currently, this micro-credential is available only with the Verify plan.
What will you get ?
Digital Badge
Exploratory Data Analysis
Issued by KMUTT | You will earn this digital badge after you have completed this Micro-credential.
- Show your real works as the competency evidence
- Sharable with an attachment of assessor’s recommendation letter
- Verified by educational / industrial experts
Who is this for ?
- Students pursuing degrees in data science, statistics, or related fields.
- Individuals who want to transition into a data-related career.
- Anyone who…
- is looking for skill to build a strong foundation in data analysis techniques.
- want to improve their data interpretation and presentation skills.
Who developed this ?
Asst.Prof.Dr. Suthathip Maneewongvatana
Director of the KMUTT Library and a lecturer in the Department of Computer Engineering, Faculty of Engineering.
Expertise/Experiences:
- Project Manager for Data Linkage and Analysis for Poultry Disease Forecasting in the Food Industry
- Project Consultant for the Automated Library System Development (KMUTT-LM) in the LM-Recommendation Module