This course covers exploratory data analysis (EDA) approaches using Python. The topics include visualization techniques, clustering methods, distribution analysis, sampling, and summarization.
Exploratory data analysis (EDA) is crucial because it helps to uncover underlying patterns, spot anomalies, and test hypotheses in datasets. This provides a strong foundation for machine learning and AI. In this course, Exploratory Data Analysis Techniques in Python, you’ll learn a variety of methods and techniques to test your data using Python. First, you’ll explore visual exploration and plotting techniques, such as line charts, bar graphs, histograms, and heatmaps, using Python libraries like Matplotlib and Seaborn. You’ll also learn about visual clustering methods like K-means and hierarchical clustering. Next, you’ll delve into visualizing different data distributions, including normal and Poisson, using Python's SciPy and Matplotlib, and then learn advanced quantitative exploratory techniques such as Median Polish and Ordination. Finally, you’ll learn about summarizing data using descriptive statistical techniques and mastering sampling methods in Python, and also explore correlation in data science, covering various correlation coefficients, their calculation, interpretation, and visualization with Python libraries like pandas and Seaborn. Upon completing this course, you'll gain the skills and knowledge necessary for EDA using Python.
Exploratory data analysis (EDA) is crucial because it helps to uncover underlying patterns, spot anomalies, and test hypotheses in datasets. This provides a strong foundation for machine learning and AI. In this course, Exploratory Data Analysis Techniques in Python, you’ll learn a variety of methods and techniques to test your data using Python. First, you’ll explore visual exploration and plotting techniques, such as line charts, bar graphs, histograms, and heatmaps, using Python libraries like Matplotlib and Seaborn. You’ll also learn about visual clustering methods like K-means and hierarchical clustering. Next, you’ll delve into visualizing different data distributions, including normal and Poisson, using Python's SciPy and Matplotlib, and then learn advanced quantitative exploratory techniques such as Median Polish and Ordination. Finally, you’ll learn about summarizing data using descriptive statistical techniques and mastering sampling methods in Python, and also explore correlation in data science, covering various correlation coefficients, their calculation, interpretation, and visualization with Python libraries like pandas and Seaborn. Upon completing this course, you'll gain the skills and knowledge necessary for EDA using Python.
