Data Visualization with Python Online Course
Data Visualization with Python: The Complete Guide
Data is becoming a force to reckon with. With the amount of data that is being generated every minute, dealing with data has become more important. The importance of data lies in the fact that it allows us to look at our history and predict the future. Data science is the field that deals with collecting, sorting, organizing and also analyzing huge amounts of data. This data is then used to understand the current and future trends. This field borrows techniques and theories from across multiple fields such as mathematics, statistics, computer science, information science, etc. It also aids other domains such as machine learning, data mining, databases and visualization.
Data scientists are gaining importance and are also earning higher salaries, which means this is the right time to become a data scientist. While, it might seem easy, sorting data, these scientists are responsible for writing important algorithms and programs to help sort and analyze the data – and this isn’t an easy task. The course will cover a number of different concepts such as an introduction to data science including concepts such as linear algebra, probability and statistics, Matplotlib, charts and graphs, data analysis, visualization of non uniform data, hypothesis and gradient descent, data clustering and so much more. That’s not all, we’ll also include projects to help you show exactly how to build visuals using Python. You can learn all this and tons of interesting stuff in this unique data science course. Enroll now and start building next generation interfaces for your data.
Course Curriculum
Introduction to Course
- Introduction
- Overview of Course
- Understanding Concepts of Data Science
- Python as a Tool
- Crash Course of Python
- Sample Scripts with Loops in Python
- Object Oriented Programming
- Functional Tools
Data Visualization
- Understanding Data Visualization
- Matplotlib library
- Bar Charts
- Line Charts
- Scatter Plots
- A1. Activity for Data Visualization
Linear Algebra
- What are Vectors? Various operations of vectors
- Vectors
- Understanding Matrices
- Matrices
- A2. Activity for Vectors Implementation
- A3. Activity for Matrix Implementation
Statistics
- A. Single Set of Data
- Single set of data
- Concepts of Central Tendencies
- Central Tendencies
- Dispersion
- A4. Activity for implementation of statistics
Probability
- Probability Concepts
- The Normal Distribution
- Central Limit Theorem
- A5.Activity for understanding
Data Analysis
- Understanding Data Analysis
- Exploring one dimensional Data
- Exploring Two dimensional data
- Exploring many dimensions
- Bubble charts representation
- Data Munging
- A6. Activity for understanding data analysis
Advanced Data Visualization
- Visualizing the content of a 2D array
- Adding a colormap legend to the figure
- Visualizing nonuniform 2D data
- Visualizing a 2D scalar Field
- Visualizing contour lines
- Polar charts
- Plotting log charts for research
Export Feature - Data Visualization
- Generating a PNG picture
- Generating PDF documents
- Multiple graph plotting and export
- Inserting sub figures
Hypothesis and Gradient Descent
- Understanding Hypothesis
- Implementation of hypothesis in Python
- Gradient Descent
- Implementation of Gradient Descent
- A7. Activity for illustration of Gradient Descent
- A7. Output for Gradient Descent Activity
Data Clustering
- Data clustering concepts
- Developing a data cluster model
- Illustration of data clustering
- A8 Activity for understanding data clusters
