From Excel to Python | Knime: preprocess and visualize data

Name: From Excel to Python | Knime: preprocess and visualize data
Rating: 4.3 (41 reviews)

Learn to pre-process and visualize data frames by using popular analytical software: Python & Knime & Excel

Created byBarbora Stetinova, MBA

Last updated 5/2024

English

What you'll learn

Work with data frames (data wrangling | manipulation | visualisation) to prepare and understand your data
Work with Knime analytics platform environmnet
Undestand the Python's syntaxes and how to work in Jupyter Notebook environment
Use Pandas, Matplotlib, Seaborn API's enabling to transform data frames and create charts and plots in Python
Model and transform data
Visualise the data in charts and plots
Read data and work with more and different file types at one place
Join and merge different data
Group and pivot data by selected parameters
Modify, filter, resort, split, filter data, handle with missing values
Use basic math formulas on the columns
Use feature scaling to normalize your data under one common range
Visualise data by using different plots and charts (box plot, pie chart, scatter plot, line plot, histogram/column chart)
Transpose tables
Understand the Knime, Python and Excel environment

Course content

8 sections • 69 lectures • 5h 7m total length

Introduction3:04
Welcome :-)
Software description3:17
Learn basic information about software we will use - Python, Knime and Excel

Downloadable documents for our hands-on exercises0:20
Please download all 5 files in Excel and csv formats
EXCEL - Loading data from MS Excel and CSV6:51
We will load our data from several files of MS Excel and CSV.
KNIME - Loading data from MS Excel and CSV8:26
We will load our data from several files of MS Excel and CSV.
KNIME - how to work with Knime nodes7:24
Before we jump into the next section, I would like to describe you how to work with Knime nodes
PYTHON- Loading data from MS Excel and CSV7:51
We will load our data from several files of MS Excel and CSV.

EXCEL - Merging data5:43
We have uploaded all files. Now is time to join them into one data frame.
EXCEL - table1:49
We will learn information about the data frame and we will transpose it
EXCEL - filter3:04
We can filter our data frame by selected parameters.
EXCEL - group and sort4:36
Learn how to group the data and sort them
KNIME - Merging data10:18
We have uploaded all files. Now is time to join them into one data frame.
KNIME - table3:55
We will learn information about the data frame and we will transpose it
KNIME - filter9:07
We can filter our data frame by selected parameters.
KNIME - group and sort7:31
Learn how to group the data and sort them
PYTHON - Merging data9:00
We have uploaded all files. Now is time to join them into one data frame.
PYTHON - table2:28
We will learn information about the data frame and we will transpose it
PYTHON - filter5:20
We can filter our data frame by selected parameters.
PYTHON - group and sort5:14
Learn how to group the data and sort them

EXCEL - Data wrangling columns - Binning4:12
We will create numeric binners of our numeric data into groups according the boundaries we will set up
EXCEL - Data wrangling columns - Convert I3:48
After this lecture you will be able to convert the data types, rename the columns, add constant value and handle with problem when you need to go steps back and then execute whole procedure again.
EXCEL - Data wrangling columns - Convert II2:28
In this lecture we will count basic calculations by using the math expressions
EXCEL - Data wrangling columns - Filters2:48
Let's filter out columns we do not need anymore and split them
EXCEL - Data wrangling columns - Missing values2:39
How to handle with obvious problem - missing values?
EXCEL - Data wrangling date and time3:02
Quite commonly valuable information you can get from date and time stamps
KNIME - Data wrangling columns - Intro1:02
KNIME - Data wrangling columns - Binning5:41
We will create numeric binners of our numeric data into groups according the boundaries we will set up
KNIME - Data wrangling columns - Convert I9:26
After this lecture you will be able to convert the data types, rename the columns, add constant value and handle with problem when you need to go steps back and then execute whole procedure again.
KNIME - Data wrangling columns - Convert II5:15
In this lecture we will count basic calculations by using the expressions in the math formula node
KNIME - Data wrangling columns - Filters3:30
We will filter our data set by using column filter node and missing value column node, so we will learn how to filter out certain columns.
KNIME - Data wrangling columns - Splits8:52
We can split our columns by using several splitting nodes into more columns
KNIME - Data wrangling columns - missing values4:52
How to handle when having missing values? Use the missing values node and use more options in there
KNIME - Data wrangling date and time I.6:05
After this lecture you will be able to change the data types to date format and count the difference between two days
KNIME - Data wrangling date and time II.3:07
During this lecture you will see how easy is to extract different information from the date and time format, e.g. year, month, day etc.
KNIME - Export to Excel0:03
PYTHON - Data wrangling columns - Binning5:22
We will create numeric binners of our numeric data into groups according to the boundaries we will set up
PYTHON - Data wrangling columns - Convert I6:20
After this lecture you will be able to convert the data types, rename the columns, add constant value and handle with problem when you need to go steps back and then execute whole procedure again.
PYTHON - Data wrangling columns - Convert II4:35
In this lecture we will count basic calculations by using the math expressions
PYTHON - Data wrangling columns - Filters ans splits5:19
Let's filter out columns we do not need anymore
PYTHON - Data wrangling columns - Missing values3:42
How to handle with obvious problem - missing values?
PYTHON - Data wrangling date and time4:29
Quite commonly valuable information you can get from date and time stamps
PYTHON - Export to Excel0:12

Introduction to the data visualisation section1:17
EXCEL - Data visualisation - Column chart3:32
Column chart to compare values of different classes
EXCEL - Data visualisation - Line plot3:47
Let's check data over time
EXCEL - Data visualisation - Pie chart4:31
After this lecture you will be able to create pie and donut charts and so see the split of your values.
EXCEL - Data visualisation - Scatter plot4:05
The scatter plot plays very important role in the data analysing because it shows you the relationship between two items, thus if two items are correlated or not, and if the correlation is positive or negative.
EXCEL - Data visualisation - Box plot2:58
Not so common but so much useful box plot depicts the distribution of your numerical values into the statistical groups - mean, maximum, minimum, first and third quartile.
KNIME - Data visualisation - Column chart, Histogram6:09
After this video you will be able to create histogram and we will learn how to work with this node to get more information, options and details.
KNIME - Data visualisation - Line plot7:31
In this video we will crate line plot to see the trend of some of our numeric values, how they develop in time. Also, I will show you line plot java script, which offers a lot of visual effects and creates so nice pictures.
KNIME - Data visualisation - pie chart6:20
After this lecture you will be able to create pie and donut charts and so see the split of your values.
KNIME - Data visualisation - Scatter plot8:24
The scatter plot plays very important role in the data analysing because it shows you the relationship between two items, thus if two items are correlated or not, and if the correlation is positive or negative.
KNIME - Data visualisation - Box plot5:54
Not so common but so much useful box plot depicts the distribution of your numerical values into the statistical groups - mean, maximum, minimum, first and third quartile.
PYTHON - Data visualisation - Column chart5:11
Column chart to compare values of different classes
PYTHON - Data visualisation - Line plot5:44
Let's check data over time
PYTHON - Data visualisation - Pie chart4:20
After this lecture you will be able to create pie and donut charts and so see the split of your values.
PYTHON - Data visualisation - Scatter plot6:35
The scatter plot plays very important role in the data analysing because it shows you the relationship between two items, thus if two items are correlated or not, and if the correlation is positive or negative.
PYTHON - Data visualisation - Box plot3:43
Not so common but so much useful box plot depicts the distribution of your numerical values into the statistical groups - mean, maximum, minimum, first and third quartile.

Requirements

access to computer or laptop with Windows (32bit or 64 bit), Linux (64bit) or Mac (64bit) and with permission to download softwares (if not, ask your administrator to download it for you – it is common at company´s computers)
no prior skills required (basic data analyzing experience in different programs is an advantage)

Description

We will focus on the most time-consuming part of the machine learning process which is the data exploration consisting from data visualisation and data wrangling serving for preparing and understanding your data.

The whole course is full of different data manipulation and visualisation hands-on exercises in three popular data science platforms:

1. open-source and very progressive programming language Python

2. open-source, highly intuitive and effective analytics platform KNIME

3. the most popular for people working with data MS Excel,

where we we will load data, transform them and visualise them.

So, what will we cover during this course?

Start with the KNIME analytics platform (installation and environment description)
Start with Python (installation and environment description)

Gathering the data into all platforms (data from Excel and csv)
Data manipulation (preparation and transformation) I - Table, Row transform, Row filter and split
Data manipulation (preparation and transformation) II - Column Binning, Column Convert and replace, Column Filter, Column Split, Column Transform,
Data manipulation (preparation and transformation) III - Other data types – date and time.

Data manipulation - Feature scaling
Data visualisation - Histogram, Line plot, Pie chart, Scatter plot, Box plot

Who this course is for:

data analysts, data scientists and those of you willing to learn new things
anyone searching open-source, user-friendly, easily understandable and highly effective SW for data analyzing and machine learning tasks
people working with data (also with big data)

From Excel to Python | Knime: preprocess and visualize data

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 6min

Start with the KNIME analytics platform and Python4 lectures • 10min

Loading data into the Jupyter Notebook, Knime analytics platform and MS Excel5 lectures • 31min

Data manipulation - Data wrangling (preparation and transformation) PART I12 lectures • 1hr 8min

Data manipulation – Data wrangling (preparation and transformation) PART II23 lectures • 1hr 37min

Data exploration – Data visualisation16 lectures • 1hr 20min

Data normalization4 lectures • 12min

Conclusion3 lectures • 4min

Requirements

Description

Who this course is for: