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What you'll learn?
- 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
Course Overview
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
Pre-requisites
- Installed Python on your PC/Mac (e.g. Ananconda distribution)
- Microsoft Excel available on your PC/Mac.
Target Audience
- Everybody interested in new data trends and technologies
Curriculum 44 Lectures 02:34:26
-
Section 1 : Welcome to our course
- Lecture 2 :
- Software description
- Lecture 3 :
- Anaconda installation (Python environment)
-
Section 2 : Acquire data
- 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
- 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
- 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
- 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
- Lecture 1 :
- EXCEL - Date
- Lecture 2 :
- PYTHON - Date
-
Section 7 : Data visualization
- 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
- Lecture 1 :
- Conclusion
- Lecture 2 :
- Bonus I
- Lecture 3 :
- Bonus II
Our learners work at
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Instructor
149127 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.
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