1st Step to Working with Big Data Using Python


  • 1st Step to Working with Big Data Using Python, Singapore elarning online course
  • 1st Step to Working with Big Data Using Python, Singapore elarning online course
  • 1st Step to Working with Big Data Using Python, Singapore elarning online course
  • 1st Step to Working with Big Data Using Python, Singapore elarning online course
  • 1st Step to Working with Big Data Using Python, Singapore elarning online course
  • 1st Step to Working with Big Data Using Python, Singapore elarning online course

Course Description

Python programmers are some of the most sought-after employees in the tech world, and Python itself is fast becoming one of the most popular programming languages. One of the best applications of Python however is data analysis; which also happens to be something that employers can't get enough of. Gaining skills in one or the other is a guaranteed way to boost your employability – but put the two together and you'll be unstoppable!

More and more people are realizing the vast benefits and uses of analyzing big data. However, the majority of people lack the skills and the time needed to understand this data in its original form. That's where data visualization comes in; creating easy to read, simple to understand graphs, charts and other visual representations of data. Python 3 and Matplotlib are the most easily accessible and efficient to use programs to do just this.

Become an expert data analyst with Python made simple

- Learn efficient python data analysis and python data mining
- Manipulate data sets quickly and easily
- Gain a skillset in Python that can be used for various other applications
- Visualize multiple forms of 2D and 3D graphs; line graphs, scatter plots, bar charts, etc.


Course Objectives

1. Data Analysis with Python and Pandas
2. Learn efficient python data analysis
3. Manipulate data sets quickly and easily
4. Master python data mining
5. Gain a skillset in Python that can be used for various other applications
6. Understanding Matplotlib and getting set up
7. Learning the different types of basic Matplotlib charts
8. Mastering the basic customization options for Matplotlib
9. Customizing ticks, add/remove & customize spines, styles with Matplotlib

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Content

Data Visualization - Course Introduction

Preview Available

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Promo

Introduction 

Getting Matplotlib And Setting Up

Download Source Code

Different types of basic Matplotlib charts

Preview Available

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Section Intro

Bar Charts

Histograms

Scatter Plots

Basic matplotlib graph

Legends

Stack Plots

Pie Chart

Loading data with NumPy

Labels, titles and window buttons

Loading data from a CSV

Section Outro

Basic Customization Options

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Section Intro

Getting Stock Prices For Our Data Set

Parsing stock prices from the internet

Plotting basic stock data

Modifying labels and adding a grid

Converting from unix time and adjusting subplots

Customizing ticks

Fills and Alpha

Add, remove, and customize spines

Candlestick OHLC charts

Styles with Matplotlib

Creating our own Style

Live Graphs

Adding and placing text

Annotating a specific plot

Dynamic annotation of last price

Section Outro

Advanced Customization Options

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Section Intro

Basic subplot additions

Subplot2grid

Incorporating changes to candlestick graph

Creating moving averages with our data

Adding a High minus Low indicator to graph

Customizing the dates that show

Label and Tick customizations

Share X axis

Multi Y axis

Customizing Legends

Section Outro

Geographical Plotting with Basemap

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Basic basemap example

Customizing the projection

More customization, like colors, fills, and forms of boundaries

Plotting Coordinates

Connecting Coordinates

Downloading and installing Basemap

Section Intro

Section Outro

3D graphing

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Section Intro

Basic 3D graph example using wire_frame

3D scatter plots

3D Bar Charts

More advanced Wireframe example

Section outro

Course Conclusion

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Conclusion

Data Analysis - Introduction to the Course

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Course Introduction

Getting pandas and fundamentals

Section Outro

Download Source Code

Introduction to Pandas

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Section intro

Creating and Navigating a Dataframe

Slices, head and tail

Indexing

Visualizing The Data

Converting To Python List Or Pandas Series

Section Outro

IO Tools

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Section intro

Read Csv And To Csv

io operations

Read_hdf and to_hdf

Read Json And To Json

Read Pickle And To Pickle

Section Outro

Pandas Operations

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Section intro

Column Manipulation (Operatings on columns, creating new ones)

Column and Dataframe logical categorization

Statistical Functions Against Data

Moving and rolling statistics

Rolling apply

Section Outro

Handling for Missing Data / Outliers

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Section Intro

Drop Na

Filling Forward and Backward Na

Detecting outliers

Section Outro

Combining Dataframes

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Section Intro

Concatenation

Appending data frames

Merging dataframes

Joining dataframes

Section Outro

Advanced Operations

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Section Intro

Basic Sorting

Sorting by multiple rules

Resampling basics time and how (mean, sum etc)

Resampling to ohlc

Correlation and Covariance Part 1

Correlation and Covariance Part 2

Mapping custom functions

Graphing percent change of income groups

Buffering basics

Buffering into and out of hdf5

Section Outro

Working with Databases

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Finishing Manipulation And Graph

Section Intro

Writing to reading from database into a data frame

Resampling data and preparing graph

Section and course outro

1st Step to Working with Big Data Using Python

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