Unlocking Insights Data Analysis Visualization
- 1. UNLOCKING INSIGHTS: DATA ANALYTICS AND VISUALIZATION A Guide to EDA and Visualization OCTOBER 19, 2024 By: Anyika, Lorreta
- 2. INTRODUCTION DATA ANALYTICS OVERVIEW What is Data Analytics?
- 3. DATA ANALYTICS The systematic computational analysis of data to discover meaningful patterns, trends, and insights that can inform decision-making and improve processes. KEY COMPONENTS • Data Collection • Data Cleaning and preparation • Exploratory Data Analysis (EDA) • Data Visualization • Reporting and Interpretation • Implementation and Decision- making
- 4. Definition, Goals & Overview 01 02 03 Data Cleaning Handle missing values, outliers, duplicates Feature Engineering Create new features for analysis EXPLORATORY DATA ANALYSIS Examine patterns, outliers, correlations A step in data analysis where we explore the dataset, understand its structure and discover initial insights.
- 5. Data Cleaning, Aggregation, Visualization Clean Data Prepare data for analysis Aggregate Data Summarize information Visualize Data Create impactful visuals PANDAS TECHNIQUES
- 6. Create Interactive Visualizations Powerful plotting library Matplotlib Statistical data visualization Seaborn INTERACTIVE VISUALS
- 7. Viz Essentials (Seaborn and Matplotlib INTRODUCTION Introduction •Comprehensive libraries for creating static, animated, and interactive visualizations in Python. •Often used in conjunction with NumPy and Pandas for data analysis. Key Components: •Figure: The overall window or page on which everything is drawn. •Axes: The area where data is plotted (could have multiple axes in one figure). •Plot Types: Line plots, scatter plots, bar charts, histograms, etc. Basic Usage: •Importing Matplotlib: import matplotlib.pyplot as plt. •Creating a simple plot: plt.plot(x, y). •Showing the plot: plt.show().
- 8. INTRODUCTION Common Plot Types: •Line Plots: Used for continuous data, showing trends over time. •Scatter Plots: Used to show the relationship between two variables. •Bar Plots: Used for categorical data comparison. •Histograms: Used to show the distribution of numerical data. Customization: •titles and labels: plt.title(), plt.xlabel(), plt.ylabel(). •ChaAddingnging colors, line styles, and markers. •Adding legends with plt.legend(). Saving Plots: •Exporting a plot to a file (PNG, JPG, etc.): plt.savefig('filename.png'). Integration with Pandas: •Easy plotting of data directly from DataFrames using .plot() method. Viz Essentials (Seaborn and Matplotlib
- 9. Style, color, axis Enhance visual appeal Matplotlib Seaborn Captivate audience with creativity Data visualization CUSTOMIZING VISUALS
- 10. Data Visualization Techniques Line Plots Show trends over time Bar Charts Compare categories visually Scatter Plots Display correlations and patterns VISUALIZING TRENDS
- 11. Histograms, Box Plots, Pie Charts Box Plots Display of data distribution and outliers COMPARISON PLOTS Histograms Visual representation of data distribution Pie Charts Illustration of proportions and percentages
- 13. [email protected] 18 OCTOBER 2024 Closing Thoughts
- 14. It’s a wrap! THANK YOU ANYIKA, LORRETA U. 09071234510 Follow on X, LinkedIn, FB & IG @ Anyika Lorreta