Crash course: Data analytics in Python using Pandas

Let's get to grips with the Python Pandas library for data analytics / analysis
Rating: 4.4 out of 5 (60 ratings)
7,470 students
Crash course: Data analytics in Python using Pandas
Rating: 4.4 out of 5 (60 ratings)
7,483 students
Perform data analysis with the Pandas library
Learn about dataframes and how to conduct data analysis in Python
Understand how to handle missing values in your data
Understand how to handle & clean up messy data

Requirements

  • Basic Python - from my FREE No Nonsense Python Course
  • The Pandas library should be installed - which you can do using 'pip install python'
  • Python should already be installed
Description

The demand for data engineers is greater than ever. So, there is no better time to upskill; learn Python and specifically data engineering.

I'll take you through the core concepts of dataframes, which are a key data structure within Pandas. We'll learn to ingest, clean and analyse the data and by the end of the course, you'll be in a position to use Python & Pandas on your own data to extract valuable insight.

The idea isn’t to become an expert through this course. The idea is to become confident in the core concepts of Python and Pandas so you can solve real-world problems today and so you can continue your learning by doing.

Because, nobody becomes an expert through taking a course (no matter how long they are), you only truly become an expert by getting out there & solving problems.

For this course, you'll need some basic Python knowledge, which you can gain from my FREE No Nonsense Python course here on Udemy.

You will need to have Python installed and the Pandas library installed - which you can do using 'pip install pandas'.

Who this course is for:
  • Beginner Python Developers
  • Those interested in data analytics
Curriculum
5 sections • 21 lectures • 59m total length
  • Introduction
  • Project introduction: What's the data & what do we want to achieve?
  • Ingesting data & cleaning it up
  • Initial insight from the data
  • Extracting a little more insight
  • Importing data
  • Inspecting our dataframe - what do we have to work with?
  • Handling missing values
  • Removing duplicated data
  • Where Statements
  • Selecting specific fields from the dataframe
  • Replacing values in our dataframe
  • Group By
  • Ranking our dataframes on a specific field value
  • The Apply function: data cleanup & additional insight
  • Write dataframes back to files
  • Your challenge, if you accept it
  • Challenge solution
  • A real world example of using Pandas
  • Where do I go from here?
  • Thank you

Instructor
Data Engineer at Kodey
Kieran Keene
  • 4.5 Instructor Rating
  • 156 Reviews
  • 12,475 Students
  • 3 Courses

Hey guys! I am a data engineer by trade and specialize in Python, SQL, Spark, Hive, MongoDB and more. I've come on Udemy to try and make simple, short crash courses into these technologies as I personally find the longer courses too drawn out & I often lose interest. The idea is to keep it short and sharp!


For loads of advanced Spark, Python & Big Data topics, please visit my website (the button on this page will take you there) - where I talk about scaling up to enterprise grade solutions.