Visualization for Data Science using Python.
Visualization for Data Science using Python., available at $79.99, has an average rating of 5, with 79 lectures, based on 5 reviews, and has 71 subscribers.
You will learn about 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 This course is ideal for individuals who are Anyone wanting to learn foundational visualization for Data Science or Aspirants for Data Analyst Role It is particularly useful for Anyone wanting to learn foundational visualization for Data Science or Aspirants for Data Analyst Role.
Enroll now: Visualization for Data Science using Python.
Summary
Title: Visualization for Data Science using Python.
Price: $79.99
Average Rating: 5
Number of Lectures: 79
Number of Published Lectures: 79
Number of Curriculum Items: 79
Number of Published Curriculum Objects: 79
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- Anyone wanting to learn foundational visualization for Data Science
- Aspirants for Data Analyst Role
Target Audiences
- Anyone wanting to learn foundational visualization for Data Science
- Aspirants for Data Analyst Role
VISUALIZATION FOR DATA SCIENCE USING PYTHON IS SET UP TO MAKE LEARNING FUN AND EASY
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:
-
Lifetime access to the course
-
Friendly support in the Q&A section
-
Udemy Certificate of Completion available for download
-
30-day money back guarantee
Course Curriculum
Chapter 1: Basics of Statistics
Lecture 1: Quick Introduction
Lecture 2: What is a random variable
Lecture 3: Nominal and Ordinal Data
Lecture 4: Central tendency – Introduction
Lecture 5: Central tendency – Examples
Lecture 6: Data Visualization
Lecture 7: Types of Quartile, Inter Quartile Range
Lecture 8: Types of Quartile, Inter Quartile Range – Example
Lecture 9: Standard Deviation & Variance
Lecture 10: Sample Standard Deviation
Lecture 11: Co Variance
Lecture 12: Normal Distribution
Lecture 13: Chi Square Distribution
Lecture 14: Chi Square Goodness of Fit
Lecture 15: Association between Categorical variables
Lecture 16: Correlation
Chapter 2: Visualization of Iris Dataset using Seaborn and Matplotlib
Lecture 1: Introduction to EDA
Lecture 2: Iris Dataset
Lecture 3: Scatter Plot
Lecture 4: Two dimensional Scatter plot
Lecture 5: Three dimensional scatter plot
Lecture 6: Pair plots
Lecture 7: One dimensional scatter plot
Lecture 8: Histogram, PDF, CDF
Lecture 9: Kde plots
Lecture 10: Kde plot – Intuition
Lecture 11: PDF and its properties
Lecture 12: CDF – Code snippet
Lecture 13: Mean, Median, Standard deviation, MAD – Code snippet
Lecture 14: Box plots
Lecture 15: Violin plot
Chapter 3: Visualization of Haberman dataset
Lecture 1: Haeberman Data – Introduction
Lecture 2: Data Overview
Lecture 3: Univariate Analysis
Lecture 4: Bivariate Analysis
Chapter 4: Linear Algebra
Lecture 1: Introduction to Linear Equations
Lecture 2: Application of Linear Algebra
Lecture 3: What is a scaler
Lecture 4: What is a point and distance between 2 points
Lecture 5: What is a vector
Lecture 6: Row and Column Vector
Lecture 7: Transpose of a Matrix
Lecture 8: Unit Vector
Lecture 9: Vector Addition and Subtraction
Lecture 10: Inverse of a vector
Lecture 11: Dot Product between two vectors
Lecture 12: Multiplication of a vector with a scaler
Lecture 13: Angle between 2 vectors – Part 1
Lecture 14: Angle between 2 vectors – Part 2
Lecture 15: Orthogonal Vectors
Lecture 16: Orthonormal vectors
Lecture 17: Equation of a line – Part 1
Lecture 18: Equation of a line – Part 2
Lecture 19: Equation of a line – Part 3
Lecture 20: Equation of a line – Part 4
Lecture 21: Projection of a point on a line
Lecture 22: Distance of a point from a line
Lecture 23: How to determine point on the negative and positive side of a line
Lecture 24: Matrix Introduction
Lecture 25: Matrix Operations
Lecture 26: Symmetric, Square, Identity and Diagonal Matrix
Lecture 27: Orthogonal Matrix
Lecture 28: Minor, Cofactor and Determinant of a Matrix (Optional)
Lecture 29: Inverse of a matrix (Optional)
Chapter 5: Principal Component Analysis
Lecture 1: Preface for Dimensionality Reduction – Part 1
Lecture 2: Preface for Dimensionality Reduction – Part 2
Lecture 3: Preface for Dimensionality Reduction – Part 3
Lecture 4: Preface for Dimensionality Reduction – Part 4
Lecture 5: Preface for Dimensionality Reduction – Part 5
Lecture 6: Gometric Intuition of PCA
Lecture 7: Mathematical formulation of PCA – Part 1
Lecture 8: Mathematical formulation of PCA – Part 2
Lecture 9: Mathematical formulation of PCA – Part 3
Lecture 10: Failure cases of PCA
Lecture 11: Connecting Colab to Gdrive
Lecture 12: Understanding MNIST dataset
Lecture 13: Visualizing MNIST single digit
Lecture 14: MNIST Visualization – Method 1
Lecture 15: MNIST Visualization – Method 2
Instructors
-
Newton Academy
Data and Machine Learning Expert
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 0 votes
- 5 stars: 5 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024