Python for Data Analysis

Python for Data Analysis, available at Free, has an average rating of 4.26, with 12 lectures, based on 729 reviews, and has 23271 subscribers.

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You will learn about You will learn the most commonly used tools for data analysis with python including JupyterLab, Numpy and Pandas. You will learn to create visualizations from your data using Matplotlib and Seaborn. This course is ideal for individuals who are Students who have just finished Survival Python. or Developers who are familiar with Python but have never worked in data science and want to learn the most commonly used tools. or Statisticians looking to migrate from R to Python. It is particularly useful for Students who have just finished Survival Python. or Developers who are familiar with Python but have never worked in data science and want to learn the most commonly used tools. or Statisticians looking to migrate from R to Python.

Enroll now: Python for Data Analysis

Summary

Title: Python for Data Analysis

Price: Free

Average Rating: 4.26

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

You will learn the most commonly used tools for data analysis with python including JupyterLab, Numpy and Pandas.

You will learn to create visualizations from your data using Matplotlib and Seaborn.

Who Should Attend

Students who have just finished Survival Python.

Developers who are familiar with Python but have never worked in data science and want to learn the most commonly used tools.

Statisticians looking to migrate from R to Python.

Target Audiences

Students who have just finished Survival Python.

Developers who are familiar with Python but have never worked in data science and want to learn the most commonly used tools.

Statisticians looking to migrate from R to Python.

You know Python. You know Excel. You may even know how to crunch numbers in R using the Tidyverse if you have a statistics background.

But when it comes to applying all this knowledge to the world of data science, you know you need more than these tools to be successful. What makes matters worse is that you are not exactly sure of what order you should be learning which data science tools. It can be a challenge to know exactly where to focus, and how to apply what you do know.

At Mass Street University, we guide statisticians and developers interested in exploring how to process and analyze data—efficiently. In Python for Data Analysis, we focus you on precisely what you need to know, and teach you how best to utilize what you already do know.

In the course, we will teach you how to combine your existing knowledge of Python with tools like Pandas and Numpy. If you have only worked with the basic Python data types, approaching some of the higher order data types can be intimidating. The structure of our course takes you from the simplest tools to the more complex to ensure you stay focused on what you need while you build on your font of data science knowledge.

JupyterLab is one tool you may not be familiar with, and it is a popular data analysis notebook that supports many languages, including Python. Notebook technology is relatively new to the world of data science, and we will go over how JupyterLab will allow you to write much smaller amounts of code efficiently.

There are a ton of data science tools that interact very well with Python to make data science a breeze when explored and taught properly. And at Mass Street University, we make sure that this dynamic is managed as efficiently as possible. Enroll today in Python for Data Analysis to stay focused on what you need to excel in data analysis.

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Instructor Introduction

Lecture 2: Course Overview

Lecture 3: Why You Should Take This Course

Lecture 4: How To Get Help With This Course

Lecture 5: Getting The Course Material

Chapter 2: Processing Data

Lecture 1: Processing Data Section Introduction

Lecture 2: JupyterLab Orientation

Lecture 3: Working With Numpy

Lecture 4: Working With Pandas

Chapter 3: Visualizing Data

Lecture 1: Visualizing Data Section Introduction

Lecture 2: Plotting With Matplotlib

Lecture 3: Plotting With Seaborn

Instructors

Bob Wakefield
Data Management Expert

Rating Distribution

1 stars: 16 votes

2 stars: 27 votes

3 stars: 117 votes

4 stars: 264 votes

5 stars: 305 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!