In this course you will learn to create various types of Data Visualization charts using Microsoft Power BI, Python and Plotly. There are a wide range of professionals who require data visualization skills to plot various charts to find critical insights from the dataset. From Marketing and sales professionals to Developers, Analysts and Data Scientists, a large number of professionals require some kind of knowledge to adequately model and represent data into creative visuals that makes it easy and intuitive to understand the complex data value in form for comparable and easy to understand visual charts. Most of the basic charts such as bar, line, pie, tree map and other charts in Power BI and other visualization software are just inefficient to represent various kinds of data with complex information. Professionals just don't rely on few basic charts, rather they could create some custom chart to solve complex problem. Most of the custom or advanced visualization charts can be created by writing few lines of python code.

In this course, you will be learning following concepts and visualization charts using python libraries such as pandas, matplotlib and seaborn-

• Installing python packages and defining path

• Creating a Line chart with matplotlib

• Putting labels and creating dashed scatterplot

• Violin chart with seaborn

• More on Violin chart

• Stripplot

• Boxplot

• Lmplot or align plot

Data visualization make this task little bit more handy and fast. With the help of visual charts and graph, we can easily find out the outliers, nulls, random values, distinct records, the format of dates, sensibility of spatial data, and string and character encoding and much more.

Moreover, you will be learning different charts to represent different kind of data like categorical, numerical, spatial, textual and much more.

• Bar Charts (Horizontal and Vertical)

• Line Charts

• Pie Charts

• Donut Charts

• Scatter Charts

• Grouped Bar Chart (Horizontal and Vertical)

• Segmented Bar Chart (Horizontal and Vertical)

• Time and series Chart

• Sunburst Chart

• Candlestick Chart

• OHLCCharts

• Bubble Charts

• Dot Charts

• Multiple Line Charts and so on.

Most of the time data scientists pay little attention to graphs and focuses only on the numerical calculations which at times can be misleading. Data visualization is much crucial step to follow to achieve goals either in Data Analytics or Data Science to get meaningful insights or in machine learning to build accurate model. The skills you learn in this course can be used in various domains related to data science and data analytics to business intelligence and machine learning.