Learning Python for Data Analysis and Visualization Ver 1

Learning Python for Data Analysis and Visualization Ver 1, available at $159.99, has an average rating of 4.44, with 113 lectures, based on 19758 reviews, and has 202762 subscribers.

You will learn about Have an intermediate skill level of Python programming. Use the Jupyter Notebook Environment. Use the numpy library to create and manipulate arrays. Use the pandas module with Python to create and structure data. Learn how to work with various data formats within python, including: JSON,HTML, and MS Excel Worksheets. Create data visualizations using matplotlib and the seaborn modules with python. Have a portfolio of various data analysis projects. This course is ideal for individuals who are Anyone interested in learning more about python, data science, or data visualizations. or Anyone interested about the rapidly expanding world of data science! It is particularly useful for Anyone interested in learning more about python, data science, or data visualizations. or Anyone interested about the rapidly expanding world of data science!.

Enroll now: Learning Python for Data Analysis and Visualization Ver 1

Summary

Title: Learning Python for Data Analysis and Visualization Ver 1

Price: $159.99

Average Rating: 4.44

Number of Lectures: 113

Number of Published Lectures: 110

Number of Curriculum Items: 113

Number of Published Curriculum Objects: 110

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Have an intermediate skill level of Python programming.
Use the Jupyter Notebook Environment.
Use the numpy library to create and manipulate arrays.
Use the pandas module with Python to create and structure data.
Learn how to work with various data formats within python, including: JSON,HTML, and MS Excel Worksheets.
Create data visualizations using matplotlib and the seaborn modules with python.
Have a portfolio of various data analysis projects.

Who Should Attend

Anyone interested in learning more about python, data science, or data visualizations.
Anyone interested about the rapidly expanding world of data science!

Target Audiences

Anyone interested in learning more about python, data science, or data visualizations.
Anyone interested about the rapidly expanding world of data science!

This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science!

You’ll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data.

You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers!

By the end of this course you will:

– Have an understanding of how to program in Python.

– Know how to create and manipulate arrays using numpy and Python.

– Know how to use pandas to create and analyze data sets.

– Know how to use matplotlib and seaborn libraries to create beautiful data visualization.

– Have an amazing portfolio of example python data analysis projects!

– Have an understanding of Machine Learning and SciKit Learn!

With 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science!

Please make sure you read the entire page to understand if the course is the correct version for you.

Course Curriculum

Chapter 1: Intro to Course and Python

Lecture 1: Course Intro

Lecture 2: Course FAQs

Chapter 2: Setup

Lecture 1: Installation Setup and Overview

Lecture 2: IDEs and Course Resources

Lecture 3: iPython/Jupyter Notebook Overview

Chapter 3: Learning Numpy

Lecture 1: Intro to numpy

Lecture 2: Creating arrays

Lecture 3: Using arrays and scalars

Lecture 4: Indexing Arrays

Lecture 5: Array Transposition

Lecture 6: Universal Array Function

Lecture 7: Array Processing

Lecture 8: Array Input and Output

Chapter 4: Intro to Pandas

Lecture 1: Series

Lecture 2: DataFrames

Lecture 3: Index objects

Lecture 4: Reindex

Lecture 5: Drop Entry

Lecture 6: Selecting Entries

Lecture 7: Data Alignment

Lecture 8: Rank and Sort

Lecture 9: Summary Statistics

Lecture 10: Missing Data

Lecture 11: Index Hierarchy

Chapter 5: Working with Data: Part 1

Lecture 1: Reading and Writing Text Files

Lecture 2: JSON with Python

Lecture 3: HTML with Python

Lecture 4: Microsoft Excel files with Python

Chapter 6: Working with Data: Part 2

Lecture 1: Merge

Lecture 2: Merge on Index

Lecture 3: Concatenate

Lecture 4: Combining DataFrames

Lecture 5: Reshaping

Lecture 6: Pivoting

Lecture 7: Duplicates in DataFrames

Lecture 8: Mapping

Lecture 9: Replace

Lecture 10: Rename Index

Lecture 11: Binning

Lecture 12: Outliers

Lecture 13: Permutation

Chapter 7: Working with Data: Part 3

Lecture 1: GroupBy on DataFrames

Lecture 2: GroupBy on Dict and Series

Lecture 3: Aggregation

Lecture 4: Splitting Applying and Combining

Lecture 5: Cross Tabulation

Chapter 8: Data Visualization

Lecture 1: Installing Seaborn

Lecture 2: Histograms

Lecture 3: Kernel Density Estimate Plots

Lecture 4: Combining Plot Styles

Lecture 5: Box and Violin Plots

Lecture 6: Regression Plots

Lecture 7: Heatmaps and Clustered Matrices

Chapter 9: Example Projects.

Lecture 1: Data Projects Preview

Lecture 2: Intro to Data Projects

Lecture 3: Titanic Project – Part 1

Lecture 4: Titanic Project – Part 2

Lecture 5: Titanic Project – Part 3

Lecture 6: Titanic Project – Part 4

Lecture 7: Intro to Data Project – Stock Market Analysis

Lecture 8: Data Project – Stock Market Analysis Part 1

Lecture 9: Data Project – Stock Market Analysis Part 2

Lecture 10: Data Project – Stock Market Analysis Part 3

Lecture 11: Data Project – Stock Market Analysis Part 4

Lecture 12: Data Project – Stock Market Analysis Part 5

Lecture 13: Data Project – Intro to Election Analysis

Lecture 14: Data Project – Election Analysis Part 1

Lecture 15: Data Project – Election Analysis Part 2

Lecture 16: Data Project – Election Analysis Part 3

Lecture 17: Data Project – Election Analysis Part 4

Chapter 10: Machine Learning

Lecture 1: Introduction to Machine Learning with SciKit Learn

Lecture 2: Linear Regression Part 1

Lecture 3: Linear Regression Part 2

Lecture 4: Linear Regression Part 3

Lecture 5: Linear Regression Part 4

Lecture 6: Logistic Regression Part 1

Lecture 7: Logistic Regression Part 2

Lecture 8: Logistic Regression Part 3

Lecture 9: Logistic Regression Part 4

Lecture 10: Multi Class Classification Part 1 – Logistic Regression

Lecture 11: Multi Class Classification Part 2 – k Nearest Neighbor

Lecture 12: Support Vector Machines Part 1

Lecture 13: Support Vector Machines – Part 2

Lecture 14: Naive Bayes Part 1

Lecture 15: Naive Bayes Part 2

Lecture 16: Decision Trees and Random Forests

Lecture 17: Natural Language Processing Part 1

Lecture 18: Natural Language Processing Part 2

Lecture 19: Natural Language Processing Part 3

Lecture 20: Natural Language Processing Part 4

Instructors

Jose Portilla
Head of Data Science at Pierian Training
Pierian Training
Data Science and Programming Training Company
Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 347 votes
2 stars: 546 votes
3 stars: 2490 votes
4 stars: 7155 votes
5 stars: 9218 votes

Frequently Asked Questions

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