Technical Paper Presentation on data analytics.pptx

technical paper presentation on data analytics

1. DATA VISUALIZATION TECHNIQUES FOR EXPLORATORY ANALYSIS Presented By : Anusha Goyal

2. INTRODUCTION D ata V isualization Data visualization is a powerful tool that allows us to explore and analyze complex data sets in a way that is intuitive and easy to understand. By representing data visually, we can quickly identify patterns, trends, and outliers that might be missed by simply looking at raw numbers or tables of data. E xploratory A nalysis Exploratory analysis is the process of examining data to understand its underlying patterns and relationships. It involves summarizing and visualizing data to identify trends, outliers, and other insights that can help inform further analysis. For example, if an analyst notices a spike in sales during a particular month, they may investigate further to determine what factors contributed to the increase. In this presentation, we will discuss different types of data visualization techniques available for exploratory analysis , why data visualization is so important and how it can help us make sense of complex data sets

3. Data V isualization T echniques Types of data visualization T here are many different types of techniques available to help you gain insights from your data. Some of the most common types of data visualizations include bar charts, line charts, scatter plots, heat maps, histograms, box plots and pie charts . Each type of visualization has its own strengths and weaknesses, and choosing the right one for your data can make all the difference in your analysis. Data Visualization Principles There are some principles for effective data visualization: Clarity and simplicity Accuracy and honesty Consistency and context Appropriate chart selection For example, scatter plots are useful for identifying patterns and relationships between variables. Heat maps are ideal for visualizing large datasets. P opular data visualization tools and libraries are Tableau , ggplot2 (R) ,Matplotlib (Python) ,D3.js (JavaScript) ,Power BI and most commonly used excel.

4. Exploratory Data Analysis Process S teps in exploratory data analysis: Data cleaning and preprocessing. Univariate analysis (e.g., histograms, box plots). Bivariate analysis (e.g., scatter plots, correlation matrices). Multivariate analysis (e.g., heatmaps, parallel coordinates). Iterative data exploration and visualization. Designing Effective Visuals Choosing the right chart for the data type. Selecting appropriate color palettes and labels. For example, if you're trying to compare values across categories, a bar chart may be more effective than a pie chart. Similarly, if you're looking for trends over time, a line chart may be more appropriate than a scatter plot.

5. Case Study The Dataset The given dataset includes the network through which Ads are airing, types of network like Cable/ Broadcast and the show name also on which Ads got aired. Insight Based on the data from 2021, suggest a media plan to the CMO of xyz . Which audience should they target? On day 6 and day 7 that is Saturday and Sunday, xyz should increase its ads placement in Overnight daypart as it is a weekend and working population have time and love to watch cable till late nights on weekend thus xyz can have higher viewership of their ads at a lower spend.

6. Future Trends in Data Visualization Data Democratization Real-time Visualization Mobile and Social Data Visualization Video Visualization Artificial Intelligence and Machine Learning Data One of the trends we can expect to see in the future of data visualization is real-time data visualization. Real-time data visualization allows users to see data as it is being generated, providing instant insights and enabling quick decision-making.

7. Conclusion In conclusion, we have explored the importance of data visualization techniques in exploratory analysis. By using visual representations of data, we are able to identify patterns, that may not be apparent in raw data. This allows us to gain insights and make informed decisions based on our findings. Each technique has its own strengths and weaknesses and it is important to choose the right one for the given dataset and research question. In today's data-driven world, the ability to analyze and interpret data is crucial. Data visualization techniques provide a powerful tool for exploratory analysis and can help us uncover hidden insights that would otherwise go unnoticed References 1. Principles-of-Data-Visualization-for-Exploratory-Data-Analysis 2. MAO –Thesis-2015 THANK YOU!