Python Data Analytics

Name: Python Data Analytics
Rating: 4.3 (248 reviews)

4.3248 ratings at Coursera

Overview

This course introduces the use of the Python programming language to manipulate datasets as an alternative to spreadsheets. You will follow the OSEMN framework of data analysis to pull, clean, manipulate, and interpret data all while learning foundational programming principles and basic Python functions. You will be introduced to the Python library, Pandas, and how you can use it to obtain, scrub, explore, and visualize data.

By the end of this course you will be able to:

• Use Python to construct loops and basic data structures

• Sort, query, and structure data in Pandas, the Python library

• Create data visualizations with Python libraries

• Model and interpret data using Python

This course is designed for people who want to learn the basics of using Python to sort and structure data for data analysis.

You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate.

Syllabus 5

Introduction to Python 2 hours
In this module you will be introduced to Python and how it can be used in data analytics. You will also learn how to use the Jupyter Notebook programming environment.
Basic Python Concepts 5.5 hours
In this module, you will learn basic programming principles such as variables and variable types using Python. You’ll also delve into basic Python statements such as Booleans and conditional statements.
Obtaining and Scrubbing Data with Pandas 5 hours
This week is focused on using a Python library called Pandas. You will learn how to use Pandas to load, select, and clean data.
Exploring Data with Python 5.5 hours
This week you will further explore and analyze datasets with Python. You will learn how to calculate basic statistics and create data visualizations with Pandas and Matplotlib, another Python library.
Modeling and Interpreting Data with Python 4.5 hours
This week you will focus on modeling data with Python and interpreting the model results. You complete a data analytics challenge that applies the knowledge of Python and the application of the OSEMN framework you have gained throughout the course.

Skills you'll practise

Python Programming Data Analysis Analysis Matplotlib Analytics Computer Programming

Advantages and disadvantages

Advantages

Structured courses with graded quizzes, assignments and deadlines you can reset.
A shareable certificate from the university or company when you pay.
Financial aid is often approved for students in India (apply 15 days before you need it).
From Meta, a well-regarded name.
Self-paced: start any time.
A clear syllabus (5 parts) you can see before you start.

Disadvantages

Paid after a 7-day free trial (Coursera Plus or per course).
Some courses can be audited for free, but graded work and certificates need payment.

Some points apply to every course of this kind; see how we rank.

This course is paid. Here is how to take it for free

Free trials: You can start a 7-day free trial for many individual courses, Specializations, or a Coursera Plus subscription to test full course features. Cancel before the seventh day if you do not want to be charged.
Financial aid: If you cannot afford the fee for a certificate, you can apply for financial aid through the link on the course home page by filling out an application about your background and goals.