Data Analysis and Visualization using Python

Data Analysis and Visualization using Python

• 1.
  A guide tomind your data. Data Analysis And Visualization using Python LibrariesSept 2017 Chariza Pladin Data Analyst - Accenture Inc. [email protected]
• 2.
  AGENDA ● Mind theData ● Data Analysis: 5 Steps to better decision making ● Why Visualize my Data? ● Introduction to Python3 and Jupyter Notebook ● Python libraries for Visualization ● Q/A
• 3.
  Mind the Data
• 4.
  Data Analysis andVisualization using Python Sept 2017 Data is EVERYWHERE.and it never sleeps.
• 5.
  Data Analysis andVisualization using Python Sept 2017 More than 90% of all the data in the globe was generated over the course of the past two years. Resource: (Business2Community)
• 6.
  Data Analysis andVisualization using Python Sept 2017 Resource: (Bernard Marr) Every 1 second = 40,000 Search queries (Google) which makes it 3.5 searches per day and 1.2 trillion searches per year.
• 7.
  Data Analysis andVisualization using Python Sept 2017 And this just happened. (While I’m busy talking…)
• 8.
  Data Analysis andVisualization using Python Sept 2017 20XX we will have over 6.1 billion smartphone users globally. 2020 Within five years there will be over 50 billion smart connected devices in the world, all developed to collect, analyze and share data 2017 nearly 80% of photos will be taken on smart phones. 2015 1 trillion photos taken and billions of them were shared online.
• 9.
  Data Analysis andVisualization using Python Sept 2017 That’s a lot of DATA.
• 10.
  Data Analysis andVisualization using Python Sept 2017 Data Analysis Resource: Doing Data Science", Cathy O'Neil and Rachel Schutt, 2013
• 11.
  Data Analysis andVisualization using Python Sept 2017 The Way to a Better Decision Making 5 InterpretResults 4 Analyze D ata 3 CollectD ata SetClearM easurem entPriorities 21 D efine YourQ uestions
• 12.
  Data Analysis andVisualization using Python Sept 2017 Why visualize ?
• 13.
  Visualize to Analyze Data Analysis andVisualization using Python Sept 2017
• 14.
  Data Analysis andVisualization using Python Sept 2017 Patterns Correlation Trends
• 15.
  Data Analysis andVisualization using Python Sept 2017 Make decision based on a massive dataset IN ONE LOOK.
• 16.
  Data Analysis andVisualization using Python Sept 2017
• 17.
  Visualize to Discover Data Analysis andVisualization using Python Sept 2017
• 18.
  Interactive data visualizations letyou mine data to discover information. Data Analysis and Visualization using Python Sept 2017
• 19.
  Visualize to Support a Story Data Analysisand Visualization using Python Sept 2017
• 20.
  Data Analysis andVisualization using Python Sept 2017 Show off your CV using visuals.
• 21.
  Data Analysis andVisualization using Python Sept 2017
• 22.
  Visualize to tell a Story Byitself Data Analysis and Visualization using Python Sept 2017
• 23.
  Data Analysis andVisualization using Python Sept 2017 I thought Hillary will be the 45th President...
• 24.
  Data Analysis andVisualization using Python Sept 2017 Visualize To Teach
• 25.
  Data Analysis andVisualization using Python Sept 2017 Introduction to Python3 and Jupyter Notebook
• 26.
  Data Analysis andVisualization using Python Sept 2017 beautiful notebook that lets you write and execute code, analyze data, embed content, and share reproducible work. Jupyter
• 27.
  Data Analysis andVisualization using Python Sept 2017 Install Jupyter Use $ pip install jupyter. Windows users can install with setuptools. Anaconda and Enthought allow you to download a desktop version of Jupyter Notebook. Microsoft Azure provides hosted access to Jupyter Notebooks.
• 28.
  Data Analysis andVisualization using Python Sept 2017 Power Python Libraries for Data Visualization
• 29.
  Data Analysis andVisualization using Python Sept 2017 matplotlib - Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. - Python forerunner library for data visualization. - “is extremely powerful but with that power comes complexity.”
• 30.
  Data Analysis andVisualization using Python Sept 2017
• 31.
  Data Analysis andVisualization using Python Sept 2017 seaborn - harnesses the power of matplotlib to create beautiful charts in a few lines of code. The key difference is Seaborn’s default styles and color palettes, which are designed to be more aesthetically pleasing and modern.
• 32.
  Data Analysis andVisualization using Python Sept 2017
• 33.
  Data Analysis andVisualization using Python Sept 2017 ggplot - plotting system for Python based on R's ggplot2 and the Grammar of Graphics. - layer components to create a complete plot.
• 34.
  Data Analysis andVisualization using Python Sept 2017
• 35.
  Data Analysis andVisualization using Python Sept 2017 Bokeh - is also based on The Grammar of Graphics, but unlike ggplot, it’s native to Python, not ported over from R. - supports streaming and real-time data.
• 36.
  Data Analysis andVisualization using Python Sept 2017
• 37.
  Data Analysis andVisualization using Python Sept 2017 pygal - offers interactive plots that can be embedded in the web browser. Its prime differentiator is the ability to output charts as SVGs. - Since each chart type is packaged into a method and the built-in styles are pretty, it’s easy to create a nice-looking chart in a few lines of code.
• 38.
  Data Analysis andVisualization using Python Sept 2017
• 39.
  Data Analysis andVisualization using Python Sept 2017 plotly making interactive plots, but it offers some charts you won’t find in most libraries, like contour plots, dendrograms, and 3D charts.
• 40.
  Data Analysis andVisualization using Python Sept 2017
• 41.
  Data Analysis andVisualization using Python Sept 2017 geoplotlib toolbox for creating maps and plotting geographical data. You can use it to create a variety of map-types, like choropleths, heatmaps, and dot density maps.
• 42.
  Data Analysis andVisualization using Python Sept 2017
• 43.
  Data Analysis andVisualization using Python Sept 2017 Q/A
• 44.
  A guide tomind your data. Thank you :)