Data Analysis, Data Science & Visualization: Python & Pandas

Data Analysis, Data Science & Visualization: Python & Pandas

Data Analysis, Data Science & Visualization: Python & Pandas

Get a JOB in Data Analysis, Data Science, Data Visualization & Data Analytics Python & Pandas become a data analytics

Language: english

Note: 4.4/5 (265 notes) 29,850 students

Instructor(s): Homework Helper Proz

Last update: 2022-03-05

What you’ll learn

  • Perform data operations like as grouping, pivoting, joining, and more with Python’s popular “pandas” module.
  • Learn how to manipulate 1D, 2D, and 3D data sets
  • Discover hundreds of pandas methods and characteristics
  • Troubleshoot faulty or missing data sets
  • Learn to program in Python well.
  • You will learn about integer, float, logical, string, and other Python types.
  • Learn how to install packages in Python
  • Learn how to code in Jupiter Notebooks
  • Successfully perform all steps in a complex Data Science project
  • Learn to build statistical models, use Backward Elimination, Forward Selection, and Bidirectional Elimination techniques.
  • Create Basic Tableau Visualizations
  • Have an intermediate skill level of Python programming.
  • Use the numpy library to create and manipulate arrays.
  • Have a portfolio of many different data analysis projects.
  • Use the pandas module with Python to create and structure data.
  • Create data visualizations using matplotlib and the seaborn modules with python.

 

Requirements

  • Basic math skills
  • Basic to Intermediate Python Skills
  • Have a computer (either Mac, Windows, or Linux)
  • Motivate to learn

 

Description

Why Data Analysis?

As organizations seek to create insights and push their businesses forward with the assistance of data, the field of data analytics is expanding at a fast pace. Learn what data analytics is, why it is important, the many kinds of data analytics, and the numerous data analytics applications in this Data Analytics Complete Course. You will also learn how to use data analytics.


Why Enroll in our course?

  1. 9Hours Intense content

  2. Full of practices and Hands on Projects

  3. FREE Textbook

  4. Community of Students and Experts

  5. Udemy Certificate

  6. 30 Days Money Back Grantee

What will we do in the course?

We’ll go through hundreds of various methods, characteristics, features, and functions that are hidden away inside this incredible library during this session. We’ll delve into a slew of various datasets, both short and lengthy, broken and immaculate, in order to show the amazing flexibility and effectiveness of this tool.


Data Analysis with Pandas and Python comes includes a slew of sample datasets that you may experiment with. Explore Pandas from the beginning and follow along with my tutorials to discover how simple it is to get started with pandas!


The Data Analysis with pandas and Python course is an excellent introduction to one of the most powerful data toolkits available today, whether you’re a novice data analyst or have spent years (*cough* far too long *cough*) in Microsoft Excel.


Topics:

  • Introduction to Python course


  • Intermediate Python- Functions, Modules, Classes and Exceptions


  • Introduction Data Analysis in Python


  • Applied Data Analysis in Python – Machine learning and Data science


In data analysis using python python’s ability to create and manage data structures quickly, for example, is one of the most common applications of the language in data analysis — Pandas, for example, provides a plethora of tools for manipulating, analyzing, and even representing complex datasets — and this is one of the most common applications of Python in data analysis.


We had a team people editing and marketing the course, the editing was done by Mohammad Chowdhury and the marketing was done by Mohammad Fahmid Chowdhury.


The course was created by professors with years of Python experience. The course content was created by Matt Williams, he is a professor with years of Python and Data Science experience, under the CC Attribution license.


Attributions:

Music: from Bensound

Thumbnail: by Isaac Smith on Unsplash

Content creator: Matt Williams from University of Bristol

Created under CC attribution license

 

Who this course is for

  • Python, data science, and data visualization enthusiasts are all encouraged to apply.
  • All curious individuals who are interested in the fast growing field of data science!

 

Course content

  • Introduction to Python
    • Join the Community
    • FREE Textbook
    • Introduction Python
    • Download Anaconda and JuperLAB on your system
    • How to access content for this section?
    • Python Variable using Data Analysis
    • Types of Data in Python using Data Analysis
    • Doing Calculations in Python using Data Analysis
    • Python Error Message using Data Analysis
    • Python Lists using Data Analysis
    • Python Loops using Data Analysis
    • Python Conditional Statement using Data Analysis
    • Python Dictionaries using Data Analysis
    • Python Files using Data Analysis
    • FREE TEXTBOOK
    • Next lecture
    • Complete Video of Section 1
  • Intermediate Python- Functions, Modules, Classes and Exceptions
    • Introduction to intermediate Python in Data Analysis
    • Accessing the content for this section- Advance Python Course for Data Analysis
    • IPython Console in Python Data Analysis
    • Python Functions and Modules in Python Data Analysis
    • String Formatting with Fstrngs Python Data Analysis
    • Testing Using Python Data Analysis
    • Classes and Class Method In Python Data Analysis
    • Handling Errors Python Data Analysis
    • Next Lecture
    • Intermediate Python- Functions, Modules, Classes and Exceptions
  • Introduction Data Analysis in Python
    • Accessing the content for this section- Introduction of Data Analysis
    • Getting Started and Jupyter Notebook
    • Pandas for Data Analysis
    • Reading CSV Files
    • Plotting Data in Data Analysis
    • Next lecture
    • Introduction to Data Analysis with Python
  • Applied Data Analysis in Python – Machine learning and Data science
    • Introduction to applied data analysis
    • Accessing the content for this section- Applied Data Analysis with Python
    • Fitting data in data analysis
    • Machine Learning in Data Analysis
    • Correlation in Applied Data Analysis
    • Clustering in Data Analysis
    • Image analysis in Data Analysis
    • Next lecture – What to expect?
    • Applied Data Analysis Complete Video
  • Deep Learning and Machine Learning (Optional)
    • Intro to Deep Learning
    • Neural Network
    • 3. Training Networks
    • Classifying Irises
    • Image Analysis
    • Convolution Neural Network
    • Handwriting Recognition
    • Ethics of Machine Learning
    • What to expect in the next lecture
    • Deep Learning- Complete Lecture
    • Deep Learning Complete Video

 

Data Analysis, Data Science & Visualization: Python & PandasData Analysis, Data Science & Visualization: Python & Pandas

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