Data Visualization with Python: The Complete Guide
Data Visualization with Python: The Complete Guide, available at $19.99, has an average rating of 3.8, with 59 lectures, based on 11 reviews, and has 701 subscribers.
You will learn about Learn to create graphical visualization for your data Learn about data analysis and data clustering Master the mathematical foundation of Data science Learn advance concepts such as Gradient descent and Data munging This course is ideal for individuals who are Beginner python developers who want to learn Data science will find this course very useful It is particularly useful for Beginner python developers who want to learn Data science will find this course very useful.
Enroll now: Data Visualization with Python: The Complete Guide
Summary
Title: Data Visualization with Python: The Complete Guide
Price: $19.99
Average Rating: 3.8
Number of Lectures: 59
Number of Published Lectures: 59
Number of Curriculum Items: 59
Number of Published Curriculum Objects: 59
Original Price: $39.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn to create graphical visualization for your data
- Learn about data analysis and data clustering
- Master the mathematical foundation of Data science
- Learn advance concepts such as Gradient descent and Data munging
Who Should Attend
- Beginner python developers who want to learn Data science will find this course very useful
Target Audiences
- Beginner python developers who want to learn Data science will find this course very useful
Data
is becoming a force to recon 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.
However,
we’ve done everything we can to make it as simple as possible. In
this beginner course to data visualization, you’ll get started with
important concepts of data science. The course will help you
understand exactly where to begin
in this lucrative field.
Starting
at the very beginning, this course will help you understand the
importance of Data Science, along with becoming familiar with
Matplotlib, Python’s very own visualization library. From there you
will learn about the linear general statistics and data analysis.
We’ll
also go over
important concepts such as data clustering, hypothesis gradient
descent and advanced data visualizations.
The
course will cover a number of different concepts
such as 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
Chapter 1: Introduction to Course
Lecture 1: Introduction
Lecture 2: Overview of Course
Lecture 3: Understanding Concepts of Data Science
Lecture 4: Python as a Tool
Lecture 5: Crash Course of Python
Lecture 6: Sample Scripts with Loops in Python
Lecture 7: Object Oriented Programming
Lecture 8: Functional Tools
Chapter 2: Data Visualization
Lecture 1: Understanding Data Visualization
Lecture 2: Matplotlib library
Lecture 3: Bar Charts
Lecture 4: Line Charts
Lecture 5: Scatter Plots
Lecture 6: A1. Activity for Data Visualization
Chapter 3: Linear Algebra
Lecture 1: What are Vectors. Various operations of vectors
Lecture 2: Vectors
Lecture 3: Understanding Matrices
Lecture 4: Matrices
Lecture 5: A2. Activity for Vectors Implementation
Lecture 6: A3. Activity for Matrix Implementation
Chapter 4: Statistics
Lecture 1: A. Single Set of Data
Lecture 2: Single set of data
Lecture 3: Concepts of Central Tendencies
Lecture 4: Central Tendencies
Lecture 5: Dispersion
Lecture 6: A4. Activity for implementation of statistics
Chapter 5: Probability
Lecture 1: Probability Concepts
Lecture 2: The Normal Distribution
Lecture 3: Central Limit Theorem
Lecture 4: A5.Activity for understanding
Chapter 6: Data Analysis
Lecture 1: Understanding Data Analysis
Lecture 2: Exploring One dimensional Data
Lecture 3: Exploring Two dimensional data
Lecture 4: Exploring many dimensions
Lecture 5: Bubble charts representation
Lecture 6: Data Munging
Lecture 7: A6. Activity for understanding data analysis
Chapter 7: Advanced Data Visualization
Lecture 1: Visualizing the contecnt of a 2D array
Lecture 2: Adding a colormap legend to th figure
Lecture 3: Visualizing nonuniform 2D data
Lecture 4: Visualizing a 2D scalar Field
Lecture 5: Visualizing contour lines
Lecture 6: Polar charts
Lecture 7: Plotting log charts for research
Chapter 8: Export Feature – Data Visualization
Lecture 1: Generating a PNG picture
Lecture 2: Generating PDF documents
Lecture 3: Multiple graph plotting and export
Lecture 4: Inserting sub figures
Chapter 9: Hypothesis and Gradient Descent
Lecture 1: Understanding Hypothesis
Lecture 2: Implementation of hypothesis in Python
Lecture 3: Gradient Descent
Lecture 4: Implementation of Gradient Descent
Lecture 5: A7. Activity for illustration of Gradient Descent
Lecture 6: A7. Output for Gradient Descent Activity
Chapter 10: Data Clustering
Lecture 1: Data Clustering concepts
Lecture 2: Developing a data cluster model
Lecture 3: Illustration of data clustering
Lecture 4: A8 Activity for understanding data clusters
Lecture 5: Bonus Lecture: More Interesting Stuff, Offers and Discounts
Instructors
-
Eduonix Learning Solutions
1+ Million Students Worldwide | 200+ Courses -
Eduonix-Tech .
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 1 votes
- 3 stars: 4 votes
- 4 stars: 2 votes
- 5 stars: 3 votes
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