# Visualization for Data Science using Python

#### 1: Visualization for Data Science using Python

Pandas, Matplotlib, Seaborn. Analyze Dozens of Datasets and Create Insightful Visualizations

#### This 60+ lesson course includes 15 hours of high-quality video and text explanations of everything under Statistics and Visualization. Topic is organized into the following sections:

• Data Type - Random variable, discrete, continuous, categorical, numerical, nominal, ordinal, qualitative and quantitative data types

• Visualizing data, including bar graphs, pie charts, histograms, and box plots

• Analyzing data, including mean, median, and mode, IQR and box-and-whisker plots

• Data distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and z-scores

• Chi Square distribution and Goodness of Fit

• Scatter plots - One, Two and Three dimensional

• Pair plots

• Box plots

• Violin plots

• End to end Exploratory Data Analysis of Iris dataset

• End to end Exploratory Data Analysis of Haberman dataset

• Principle Component Analysis and MNIST dataset

AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:

• We will start with basics and understand the intuition behind each topic

• Video lecture explaining the concept with many real life examples so that the concept is drilled in

• Walkthrough of worked out examples to see different ways of asking question and solving them

• Logically connected concepts which slowly builds up

Enroll today ! Can't wait to see you guys on the other side and go through this carefully crafted course which will be fun and easy.

YOU'LL ALSO GET:

• Friendly support in the Q and A section

• 30-day money back guarantee

#### Who this course is for:

• Anyone wanting to learn foundational visualization for Data Science
• Aspirants for Data Analyst Role

What you'll learn

• Visualizing data, including bar graphs, pie charts, histograms
• Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
• Analyzing data, including mean, median, and mode, plus range and IQR and box plots
• Univariate and Multivariate data visualization
• Code based implementation of different plots like scatter plot, pair plots, box plots, violin plots
• Matplotlib and seaborn visualization packages

Requirements

• Basic understanding of python commands
• Foundational Mathematics

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Most Popular Python Libraries For Data Visualization
1. Matplotlib. Matplotlib is one of the best python data visualization libraries for generating powerful yet simple visualization. ...
2. Plotly. ...
3. Seaborn. ...
4. GGplot. ...
5. Altair. ...
6. Bokeh. ...
7. Pygal. ...
8. Geoplotlib.
The following are the commonly used data visualization charts.
1. Distribution plot. A distribution plot is used to visualize data distribution. ...
2. 2. Box and whisker plot. This plot is used to plot the variation of the values of a numerical feature. ...
3. Violin plot. ...
4. Line plot. ...
5. Bar plot. ...
6. Scatter plot. ...
7. Histogram. ...
8. Pie chart.

Some of the best data visualization tools include Google Charts, Tableau, Grafana, Chartist, FusionCharts, Datawrapper, Infogram, and ChartBlocks etc. These tools support a variety of visual styles, be simple and easy to use, and be capable of handling a large volume of data.

Seaborn is more comfortable with Pandas data frames. It utilizes simple sets of techniques to produce lovely images in Python. Matplotlib is highly customized and robust. With the help of its default themes, Seaborn prevents overlapping plots.

Jupyter Notebooks provide a data visualization framework called Qviz that enables you to visualize dataframes with improved charting options and Python plots on the Spark driver.

matplotlib is the O.G. of Python data visualization libraries. Despite being over a decade old, it's still the most widely used library for plotting in the Python community

Data Visualization
• Get or create your data.
• Choose a chart type.
• Prepare data.
• Create chart.

Data Visualization Tools. 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. The tools can be broken down into two categories: 1) code free and 2) code based.

Common Types of Data Visualizations
• Bar Chart.
• Doughnut Chart or Pie Chart.
• Line Graph or Line Chart.
• Pivot Table.
• Scatter Plot.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Artificial Neural Networks (ANNs) ANNs are currently one of the best models to find non-linear patterns in data and to build really complex relationships between independent and dependent variables.

Python is one of the most popular simple universal languages ​​for data visualization. It is the best choice to solve the problem of Machine Learning, Deep Learning, Artificial Intelligence, and so on. Object-oriented and easy to use, is developed for a very easy-to-read code.

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