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Data Wrangling & Data Cleaning – Preparing Data for Analytics & AI
Data Analysis & Visualization – Turning Data into Insights
Course Prerequisite(s)
- Please note that this course has the following prerequisites which must be completed before it can be accessed
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Data Analysis & Visualization – Turning Data into Insights
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Statistics for Data Science – Probability, Distributions & Data Insights
About Course
Raw data is rarely clean, structured, or ready for analysis. In real-world data science and analytics projects, most of the effort goes into cleaning, transforming, and preparing data for meaningful insights.
This course introduces the essential techniques used to clean and prepare datasets for analytics, machine learning, and artificial intelligence applications. Learners will explore how to handle missing values, remove duplicates, transform data formats, and standardize datasets.
Using modern data analysis tools and Python libraries such as Pandas and NumPy, students will gain hands-on experience working with messy datasets and converting them into structured, analysis-ready data.
By the end of this course, learners will understand how to transform raw data into reliable datasets suitable for data analytics, machine learning models, and business intelligence applications.
Benefits of the course
- Understand the importance of data preparation in analytics and AI
- Clean and preprocess raw datasets
- Handle missing values and incomplete data
- Remove duplicates and inconsistent records
- Transform and normalize datasets
- Merge and combine datasets from multiple sources
- Prepare structured data for machine learning models
- Build reliable and high-quality datasets for analysis
Course Content
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