alltrinbruno(DS2) data science visualisation.pptx

Data science visualisation techniques

1. M.A.M. COLLEGE OF ENGINEERING AND TECHNOLOGY Siruganur , Tiruchirappalli – 621 105. ocs353-data science fundamentals Register Number Name of student 812021114001 AAKASH.R 812021114002 AJAY.D 812021114003 ALLTRIN BRUNO.A 812021114004 AMEEN THAKIR.G.M 812021114006 BALAMURUGAN.N ASSIGNMENT TITLE :usage of data visulaization Techiniques In data science workflow

2. SYNOPSIS INTRODUCTION IMPORTANCE OF DATA VISUALIZATION TOOLS OF DATA VISUALIZATION VISUALIZATION WITH CODES PYHTON PACKAGES MERITS

3. INTRODUCTION Data visualization may be described as graphically representing data. It is the act of translating data into a visual context, which can be done using charts, plots, animations, infographics, etc.

4. Why is data visualization so important for data scientists? Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data. The challenge data scientists face is that it is not always possible to connect the dots when faced with raw data; this is where data visualization is extremely valuable.

5. Data professionals, such as data scientists and data analysts, would typically leverage data visualization tools as this helps them to work more efficiently and communicate their findings more effectively. Data Visualization Tools

6. VISUALIZATION WITH CODES The tools can be broken down into two categories: 1) code free 2) code based If you are more tech-savvy, you may prefer to use a programming language to visualize your data. The increase in data production has boosted the popularity of Python and R due to their various packages that support data processing.

7. PYHTON PACKAGES Python is a high-level, interpreted, general-purpose programming language. It offers several great graphing packages for data visualization, such as: Matplotlib Seaborn Plotly ggplot Bokeh Geoplotlib .

8. Common Graphs Used for Data Visualization Frequency Table Bar Graph Pie Charts Line Graphs and Area Charts Histograms Scatter Plots Heatmaps Treemaps

9. MERITS Simplifies complex data Reveals patterns and trends Improves retention and engagement Increases accessibility Real-time monitoring Identify areas that need attention or improvement Predictive analysis Increases productivity Risk management

10. THANK YOU