Visualization for Data Science using Python
1: Visualization for Data Science using Python
Pandas, Matplotlib, Seaborn. Analyze Dozens of Datasets and Create Insightful Visualizations
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VISUALIZATION FOR DATA SCIENCE USING PYTHON IS SET UP TO MAKE LEARNING FUN AND EASY
This 60+ lesson course includes 15 hours of highquality 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 boxandwhisker plots

Data distributions, including standard deviation, variance, coefficient of variation, Covariance and Normal distributions and zscores

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.
Visualization for Data Science using Python  Udemy
YOU'LL ALSO GET:

Lifetime access to the course

Friendly support in the Q and A section

Udemy Certificate of Completion available for download

30day 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 zscores
 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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Visualization for Data Science using Python  Udemy
 Matplotlib. Matplotlib is one of the best python data visualization libraries for generating powerful yet simple visualization. ...
 Plotly. ...
 Seaborn. ...
 GGplot. ...
 Altair. ...
 Bokeh. ...
 Pygal. ...
 Geoplotlib.
 Distribution plot. A distribution plot is used to visualize data distribution. ...
 2. Box and whisker plot. This plot is used to plot the variation of the values of a numerical feature. ...
 Violin plot. ...
 Line plot. ...
 Bar plot. ...
 Scatter plot. ...
 Histogram. ...
 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
 Develop your research question.
 Get or create your data.
 Clean your data.
 Choose a chart type.
 Choose your tool.
 Prepare data.
 Create chart.
Visualization for Data Science using Python  Udemy
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.
 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 nonlinear 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. Objectoriented and easy to use, is developed for a very easytoread code.
INSTRUCTOR
Newton Academy