Data Visualizations using Python with Data Preparation

Name: Data Visualizations using Python with Data Preparation
Rating: 3.0 (6 reviews)

Data Visualization using Python

Created byEGMHAcademy EGMHAcademy

Last updated 2/2019

English

What you'll learn

Applied Statistics using Python

Course content

1 section • 46 lectures • 1h 23m total length

Getting Started10:48
Begin with getting started in data visualizations using Python, installing and using the Spyder IDE, and applying core libraries for data manipulation, transformation, and data preparation.
Getting Started 22:04
Begin your data preparation journey with Python, as this getting started module covers initial setup and practical steps for data-driven visualizations.
Getting Started 32:52
Get started by installing Python tools, launching a Python interpreter, using Unix for commands, and exploring a spider idea with Jupiter and ide for data preparation and visualizations.
Getting Started 45:40
Learn how to start Python in the console, run a simple script, and print hello as you explore core data handling through chapter 2.
Data Mining Process5:37
Explore the data mining process from business understanding through deployment. Highlight data preparation, cleaning, transformation, modeling, evaluation, and visualization for decision support.
Download Dataset1:11
Learn how to locate and download the iris dataset for statistical learning, and understand data preparation steps for Python visualizations.
Read CSV2:03
Discover how to read csv data in python by importing modules, loading a file into variables, and appending data for visualization with basic libraries.
Bar Chart5:19
Learn to create a bar chart in python to visualize data understanding, using descriptive and inferential statistics to inform data preparation and interpretation.
Bar CHart1:12
Demonstrate bar chart visualizations in python, highlighting the difference between horizontal and standard bar charts and the role of data preparation.
Histogram1:35
Learn to create histograms in python to visualize data distributions, read data, and customize visuals with color and transparency.
LIne CHart1:25
Learn how to create a line chart in Python with data preparation, including setting up columns, applying colors, and rendering the chart.
Multiple Line Chart0:41
Learn to create a multiple line chart in Python with data preparation, using color variations to emphasize changes across data series.
Pie Chart1:38
Learn how to create a pie chart in Python, setting data, sizes, and colors, and configuring labels for clear visual representation.
Scatterplot2:21
Explore building scatterplots in Python, applying data preparation steps, color by transparency and color rules, and using a color key to reveal patterns and biases in data.
Boxplot0:53
Explore boxplots in data visualizations using Python with data preparation, featuring practical examples and key insights from the lecture topic.
Boxplot0:20
Learn how boxplots in Python help visualize data during preparation, showing basic boolean comparisons and how running code reveals differences in values.
Scatterplot Matrix1:28
Create a scatterplot matrix by setting up data entries with PD tools, adjusting figure sizes, and incorporating equal histograms and diagonal elements.
Save To Image0:53
Learn how to save figures as images while preparing data for visualizations in Python, using practical steps to generate quality visuals.
Bar CHart with SeaBorn1:53
Learn to create bar charts with seaborn in Python, visualizing categorical data such as species, applying color and style options, and iterating through a batch of ideas for data preparation.
Histogram with SeaBorn1:15
Create a histogram in Python using the seaborn library and run the code to inspect a distribution.
LIne CHart with SeaBorn0:59
Learn to create line charts in Python using Seaborn to visualize data effectively. Build practical plotting skills with Seaborn line charts for clear visual insights.
Scatterplot with SeaBorn0:20
Learn to create a scatterplot with seaborn using Python, applying data preparation steps to visualize real data sets and demonstrate programming techniques for clean analysis.
Categorical PLot with SeaBorn0:46
Learn to create categorical plots in SeaBorn with Python, covering data preparation concepts and exploring categorical data and species categories through practical plotting steps.
Boxplot with SeaBorn0:35
Explore creating a boxplot using seaborn in Python, using a categorical data approach to visualize distribution across categories and identify trends in the data.
Scatterplot Matrix with SeaBorn0:53
Explore setting up data entries and preparing documentation as you build a scatterplot matrix with SeaBorn in Python, within data visualizations and data preparation.
Save Image for Seaborn0:54
Save Seaborn plots by exporting images and set up the workflow on the drive for data preparation.
INteractive Chart6:07
Explore building an interactive chart in Python by loading data, importing components, and configuring the chart layout and labels to create a dynamic visualization.
INteractive Chart3:51
Create interactive charts using traces, scatter plots, and markers, and configure layouts to visualize data effectively while preparing data for Python-based visualizations.
INteractive Chart1:54
Develop and customize an interactive chart by setting up the chart, copying and adjusting trees, and comparing ideas within a graph setup in python.
INteractive Chart2:13
Explore interactive charts in Python, from scatter and bar charts to pie, Sankey, and population pyramid visualizations, and learn how to customize themes and present data online.
Data Processing: DF.Head()1:13
Perform essential data processing after importing data in Python. Learn to select data, handle missing values, inspect datasets, and view the first 10 rows with df.head().
Data Processing: DF.Tail()0:16
Master data processing with DF.Tail() to retrieve the last 10 rules, supporting data preparation for visualizations in Python.
Data Processing: DF.Describe()0:21
Investigate descriptive statistics for your data by using the describe function to obtain a concise overview of the hard data.
Data Processing: Select Variable or Column0:24
Learn how to select a variable or column in Python, using simple examples to call a name and set other variable names, with a separate line illustrating the result.
Data Processing: Select Variable or Column0:25
Learn how to select a variable or column during data preparation in Python for robust data visualizations, focusing on practical techniques for reliable data processing.
Data Processing: Select Rows0:41
Use Python for data processing to select rows, and adjust the range by changing numbers.
Data Processing: Select Rows and Variables0:51
Practice selecting rows and variables in Python to prepare data for visualizations, mastering multi-variable data handling for effective data preparation.
Data Processing: Remove Variables0:27
Drop columns in a dataset using Python to remove variables, streamlining data preparation for visualizations.
Data Processing: Append Rows1:26
Append rows to data using pd in Python, and practice data processing techniques for preparing datasets for visualizations.
Data Processing: Sort Variable1:10
Learn how to use slot values and variables in Python to assign, modify, and sort data, including testing descending order in data processing.
Data Processing: Rename Variables2:39
Explore data processing in Python by renaming variables and columns to standardize names for data preparation and subsequent visualizations.
Data Processing: GroupBy1:58
Learn to perform data processing in Python using groupby to segment data into groups, then apply aggregations such as mean to reveal insights from species, lengths, and other group-level metrics.
Data Processing: Remove Missing Values0:37
Learn to remove missing values in data preparation by dropping them with a simple function, ensuring clean data for Python-based data visualizations.
Data Processing: Is there Missing Values0:32
Learn how to detect and remove missing values in Python using a function, streamlining data preparation for visualizations.
Data Processing: Replace Missing Values0:22
Learn to replace missing values as part of data preparation for Python data visualizations in this course.
Data Processing: Remove Duplicates0:39
Learn how to remove duplicates in Python using the drop duplicates function, assign the result to a variable, and run the code to clean the dataset.

Requirements

Fundamentals Python programming

Description

Master Python for Data Visualization & Data Processing

Data is everywhere, and organizations urgently need professionals who can translate complex numbers into compelling visual stories. According to SAS, building analytics skills gives you a massive career advantage by sharpening your problem-solving abilities, opening doors to high-demand roles, and paving the way into cutting-edge fields like the Internet of Things (IoT) and Smart Cities.

This bite-sized course focuses on Data Visualization and Data Processing, mapping directly to the Data Understanding and Data Preparation stages of the industry-standard CRISP-DM framework.

Why Take This Course?

Hands-On Visuals: Master Matplotlib, Seaborn, and interactive plotting tools to build publication-ready charts.
CRISP-DM Alignment: Ground your skills in real-world data science workflows from raw data ingestion to polished visualization.
Certification Ready: Prepares you to sit for the exam at EMHAcademy to earn your official SVBook Certified Data Miner using Python credential.

Recommended Learning Path

To maximize your learning experience, take these courses in sequence:

Create Your Calculator: Learn Python Programming Basics Fast
Applied Statistics using Python with Data Processing
Advanced Data Visualizations using Python with Data Processing (This Course)
Machine Learning with Python (Modeling & Evaluation)

Prerequisite Note: Basic familiarity with Python is recommended. Beginners should start with "Create Your Calculator" first.

What You Will Learn

Data Mining Process & Workflow Setup

Understanding the CRISP-DM Framework
Downloading, loading, and inspecting datasets in Python

Core & Advanced Data Visualizations

Standard Python Charts: Bar Charts, Histograms, Line & Multi-Line Charts, Pie Charts, Box Plots, Scatterplots, and Scatterplot Matrices
Seaborn Statistical Plots: Seaborn Bar Charts, Histograms, Line Charts, Categorical Plots, Box Plots, and Pairplot Grids
Interactive Visualizations: Building dynamic charts for deep-dive exploratory data analysis
Exporting Work: Saving high-resolution figures to image files for reports and dashboards

Data Processing & Cleansing (Pandas)

Data Inspection: DF.head(), DF.tail(), DF.describe()
Slicing & Filtering: Selecting specific variables, rows, and targeted subsets
Data Cleansing: Detecting, removing, and replacing missing values (NaN), plus removing duplicates
Transformations: Renaming columns, appending rows, dropping unwanted variables, sorting, and aggregating with GroupBy

Requirements

Basic knowledge of Python programming (e.g., variables, basic syntax).
A computer (Windows, Mac, or Linux) with Python and Jupyter Notebook/IDE installed.

Who This Course Is For

Beginners in Data Science looking to master data visualization and processing.
Students and professionals who want to learn how to present data insights clearly using Python.
Anyone preparing for the SVBook Certified Data Miner using Python credential.

Who this course is for:

Beginner Data Scientist or Analyst interested in Python programming