Data Transformation and Visualization In Python and MS Excel Hands-on

Learn how to transform, process and explore your data in Excel and Python.

Created by : Rated :4.2 11309 views

Last Updated: 2022-05-03 10:05:57 English Course Type: Self Learning

Python (installation and environment description)
Gathering the data into both platforms (data from Excel and CSV)
Data wrangling (preparation and transformation) I - Table, row transform, row filter, and split
Data wrangling (preparation and transformation) II - Column binning, column convert and replace, column filter, column split, column transform
Data wrangling (preparation and transformation) III - Other data types – date and time
Data exploration: Visualisation by creating a histogram, line plot, pie chart, scatter plot, box plot

In this course, we will focus on the most time-consuming part of the machine learning process which is the data exploration consisting of data visualization and data wrangling serving for preparing and understanding your data. The whole course is full of different hands-on exercises in two popular data analytics platforms:

Open-source and very progressive programming language Python
The most popular MS Excel

where we will read the data, transform them, and visualize them.

What will you learn in this course?

Python (installation and environment description)
Gathering the data into both platforms (data from Excel and CSV)
Data exploration: Data wrangling (preparation and transformation) I - Table, row transform, row filter, and split
Data exploration: Data wrangling (preparation and transformation) II - Column binning, column convert and replace, column filter, column split, column transform
Data exploration: Data wrangling (preparation and transformation) III - Other data types – date and time
Data exploration: Visualisation by creating a histogram, line plot, pie chart, scatter plot, box plot

Installed Python on your PC/Mac (e.g. Ananconda distribution)
Microsoft Excel available on your PC/Mac.

Everybody interested in new data trends and technologies

Section 1 : Welcome to our course 3 Lectures 00:09:01
Lecture 1 :
Introduction Preview
Lecture 2 :
Software description
Lecture 3 :
Anaconda installation (Python environment)
Section 2 : Acquire data 7 Lectures 00:14:39
Lecture 1 :
EXCEL - Acquire data into Excel
Lecture 2 :
PYTHON - Acquire data into Python
Lecture 3 :
Acquire data I
Lecture 4 :
Acquire data II
Lecture 5 :
Acquire data III
Lecture 6 :
Acquire data IV
Lecture 7 :
Acquire data V
Section 3 : Data manipulation I - tables 4 Lectures 00:18:58
Lecture 1 :
EXCEL - merging data
Lecture 2 :
EXCEL - transpose table
Lecture 3 :
PYTHON - merging data
Lecture 4 :
PYTHON - transpose table
Section 4 : Data manipulation II - rows 4 Lectures 00:18:04
Lecture 1 :
EXCEL - filters
Lecture 2 :
EXCEL - group and sort
Lecture 3 :
PYTHON - group and sort
Lecture 4 :
PYTHON - filters
Section 5 : Data manipulation III - Columns 10 Lectures 00:39:14
Lecture 1 :
EXCEL - binning
Lecture 2 :
EXCEL - conv I
Lecture 3 :
EXCEL - conv II
Lecture 4 :
EXCEL - column filter, splitter
Lecture 5 :
EXCEL - missing values
Lecture 6 :
PYHTON - binning
Lecture 7 :
PYTHON - conv I
Lecture 8 :
PYTHON - conv II
Lecture 9 :
PYTHON - column filter, splitter
Lecture 10 :
PYTHON - missing values
Section 6 : Data manipulation IV - Date data types 2 Lectures 00:07:28
Lecture 1 :
EXCEL - Date
Lecture 2 :
PYTHON - Date
Section 7 : Data visualization 11 Lectures 00:45:42
Lecture 1 :
Introduction
Lecture 2 :
EXCEL - column chart
Lecture 3 :
EXCEL - line plot
Lecture 4 :
EXCEL - pie chart
Lecture 5 :
EXCEL - scatter plot
Lecture 6 :
EXCEL - box plot
Lecture 7 :
PYTHON - column chart
Lecture 8 :
PYTHON - line plot
Lecture 9 :
PYTHON - pie chart
Lecture 10 :
PYTHON - scatter plot
Lecture 11 :
PYTHON - box plot
Section 8 : Conclusion 3 Lectures 00:01:20
Lecture 1 :
Conclusion
Lecture 2 :
Bonus I
Lecture 3 :
Bonus II

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Barbora Stetinova,

149223 Course Views

3 Courses

Experienced in Data Science and Business Intelligence (Machine Learning&Deep Learning projects, BI) for different fields, like Production, Logistics, Finance, Sales in automotive industry. Engaged as a business analyst consultant for different industries such as retail, sensorics, building, automotive etc., at Leadership Synergy Community. Publishing Data science e-learning courses under ElderberryData brand (e.g. Introduction to Big Data and Artificial Intelligence for managers, Machine Learning with KNIME analytics platform, Data manipulation and visualisation with Knime, Python and Excel, MS Excel crash course). Participating on educational e-learning projects and technical reviews of Python Machine learning and Deep learning courses with PACKT Publishing. Member of the DataCup 2019 winner team. I am motivated by learning new things and helping others.