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What you'll learn?
- How-to install Python and Anaconda - the worlds largest Data Science platform.
- How-to create a virtual environment using Conda.
- How-to setup the free Atom Text Editor.
- How to clone a GitHub Repository in Atom Text Editor.
- How-to create a new branch in Atom Text Editor.
- Use Python Pandas to read in large data-sets such as stock price information, customer information, purchase information and more.
- Use Pandas DataFrames to work with tabular data.
- Inspect datasets to gain quick valuable insights.
- Use conditional filtering to select relevant information from datasets.
- Using NumPy and Pandas together.
- Create Pandas DataFrames from scratch.
- Create DataFrames from Python dictionaries.
- Using Broadcasting with DataFrames.
- Correctly labeling data and columns.
- Data cleansing techniques.
- Using Python Pandas to create graphical plots such as bar, line, area, scatter etc.
- How-to analysis datasets using statistical methods such as min, mas, mean, std.
- Create filters in your code to extract targeted data from large datasets.
- How-to manage time data in Python with Pandas.
- Correctly index time data and create DateTime indexes.
- Partial String Indexing and slicing.
- Resampling Pandas Time Data.
- Method Chaining.
Course Overview
Python Pandas are one of the most used libraries in Python when it comes to data analysis and manipulation. Whether in finance, scientific fields, or data science, a familiarity with Pandas is a must have. This course teaches you how to work with real-world data sets for analyzing data in Python using Pandas. Not only will you learn how to manipulate and analyse data you will also learn powerful and easy to use visualization techniques for representing your data.
This course kicks off by showing you how to get up and running using GitHub, an essential skill in your coding career. Ideally, to get the best from this course you should have some Python programming experience.
Every piece of code and dataset used in this course is available to download for free from GitHub.
Without doubt this course will teach you the necessary skills to apply basic data science techniques which are use the world over by experienced data scientists and those who spend their working day in spreadsheets.
Pre-requisites
- You will need a desktop computer or laptop with Internet connection.
- Some prior coding experience with Python would be beneficial or maybe have done a Python introduction course.
- This course will walk you through installing all the necessary software and tools. Included is, how-to setup Python and Anaconda, how-to setup Atom Text Editor, how-to setup a virtual environment, how-to use GitHub and clone a repository.
Target Audience
- Software Developers who have basic Python experience/knowledge and are looking to up-skill into the high demand area of Data Science.
- Software Developers who work with spreadsheets and data-sets and would like to learn how to produce valuable insights from them.
- Data Analysts operating in business who are looking to transition into Data Science by learning how to produce informative data-sets and graphs.
Curriculum 32 Lectures 02:18:28
-
Section 1 : Course Introduction
- Lecture 2 :
- How to access the source code for this course
-
Section 2 : Setting up Python, Anaconda, Atom and GitHub
- Lecture 1 :
- Setting Python up with Anaconda
- Lecture 2 :
- Setting up Atom Text Editor
- Lecture 3 :
- Creating Virtual Environments
- Lecture 4 :
- How to Clone a GitHub Repository
- Lecture 5 :
- How to use the Atom Text Editor to push code to GitHub
-
Section 3 : Introduction to Python Pandas
- Lecture 1 :
- Introduction to Pandas
- Lecture 2 :
- Introduction to DataFrames
- Lecture 3 :
- Inspecting Data
- Lecture 4 :
- Conditional Filtering
- Lecture 5 :
- Using NumPy and Pandas Together
- Lecture 6 :
- Creating DataFrames from NumPy
- Lecture 7 :
- Creating DataFrames from Dictionaries
- Lecture 8 :
- Using Broadcasting in Pandas
- Lecture 9 :
- Labeling data in a DataFrame
- Lecture 10 :
- Building DataFrames with Broadcasting
- Lecture 11 :
- Cleansing, Importing and Exporting Data
- Lecture 12 :
- Creating Plots with Pandas
-
Section 4 : Visual Data Analysis
- Lecture 1 :
- Creating Graphs with Pandas Plot Lines
- Lecture 2 :
- Creating Graphs with Pandas Scatter Plots
- Lecture 3 :
- Creating Graphs with Pandas Bar Plots
- Lecture 4 :
- Statistical Exploratory Data Analysis
- Lecture 5 :
- Filtering Data
-
Section 5 : Managing Dates and Times with Python Pandas
- Lecture 1 :
- Introduction to Pandas DateTime
- Lecture 2 :
- Indexing Pandas Time Series
- Lecture 3 :
- Creating and using a DateTimeIndex
- Lecture 4 :
- Resampling Pandas Time Data
- Lecture 5 :
- Method Chaining
- Lecture 6 :
- Separating and Resampling
- Lecture 7 :
- Additional Filtering Methods
- Lecture 8 :
- Visualizing Pandas Time Data
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Instructor
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Hi! I'm Tony. I love to read, write and teach and you could definitely call me a bit of geek. I love all things to do with programming, productivity, books, and the internet.
Previously I have run my own software business and won several awards from, most innovative startup to best product. After finding out the hard way just how stressful growing a startup into a business can be I have researched all things to do with productivity and I love helping others become more productive and avoid the mistakes I have made.
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