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Data Analytics in Python using Pandas

Let's get to grips with the Python Pandas library for data analytics / analysis

Instructed by Kieran Keene

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  • 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

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.

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

  • Basic Python
  • The Pandas library should be installed - which you can do using 'pip install python'
  • Python should already be installed
  • Beginner Python Developers
  • Those interested in data analytics
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Section 1 : Introduction

  • Lecture 1 :

Section 2 : A project to get us started

  • Lecture 1 :
  • Project introduction: What's the data & what do we want to achieve?
  • Lecture 2 :
  • Ingesting data & cleaning it up
  • Lecture 3 :
  • Initial insight from the data
  • Lecture 4 :
  • Extracting a little more insight

Section 3 : The Pandas Essentials

  • Lecture 1 :
  • Importing data
  • Lecture 2 :
  • Inspecting our dataframe - what do we have to work with?
  • Lecture 3 :
  • Handling missing values
  • Lecture 4 :
  • Removing duplicated data
  • Lecture 5 :
  • Where Statements
  • Lecture 6 :
  • Selecting specific fields from the dataframe
  • Lecture 7 :
  • Group By
  • Lecture 8 :
  • Ranking our dataframes on a specific field value
  • Lecture 9 :
  • The Apply function: data cleanup & additional insight
  • Lecture 10 :
  • Write dataframes back to files

Section 4 : Practicing what we have learned

  • Lecture 1 :
  • Your challenge, if you accept it
  • Lecture 2 :
  • Challenge solution

Section 5 : Conclusion

  • Lecture 1 :
  • A real world example of using Pandas
  • Lecture 2 :
  • Thank you
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Kieran Keene,

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