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This three day course is aimed at those wishing to learn how to use Python to work with and handle Data. When combined with our Introduction to Data Science course you would be set up well to follow a Python learning journey into Data Science, Machine Learning, and Artificial Intelligence.

During the programme you will be introduced to Python and specific development environments and packages for working with Data, with a focus on NumPy, Pandas, Matplotlib, and Seaborn.

Along the way you will see how to clean and manipulate tabular data, apply simple statistical techniques and data visualisations, and learn about how to control the flow of your program in order to automate processes.

Throughout the course you will engage with activities and discussions with one of our Data Science technical specialists and complete technical lab activities to practice the techniques you have learnt and develop ideas for further practice.

No prior experience with Python is necessary, though it is assumed that you will be familiar with core data concepts such as simple table structures and data types – all the pre-requisites you need are covered by our Data Fundamentals course.

To apply your knowledge of data practically using Python for handling data in roles that involve data analysis, data engineering, data science, machine learning and AI, and Data related Ops roles.
If you are in a Software or IT related role where you work with Python, this course will support you in learning how to work with Data.
To ensure you have the necessary pre-requisite knowledge when combined with Introduction to Data Science should you wish to progress onto Data Science and Machine Learning with Python.

1. Introduction to Programming for Data Handling

Describe the pros and cons of using programming languages to work with data
Identify the languages most suitable for data handling
Explain the challenges of using programming languages versus data analysis tools

2. Introduction to Python and IDEs

Describe the key attributes of the Python programming language.
Explain the role of the Jupyter IDE for Python programming.
Use the Jupyter IDE to write a basic Python program.
Write a program which uses string, integer, float and boolean data types.

3. Data Structures, Flow Control, Functions, and Basic Types

Construct collections to solve data problems.
Utilise selection and iteration syntax to control the flow of a Python program.
Write reusable functions which can be used to alter data & automate repetitive tasks.
Use Python's built-in open function to create, read, and edit files.

4. Mathematical and Statistical Programming with NumPy

Describe the core features of NumPy arrays.
Create, index, and manipulate NumPy arrays to solve data problems.
Use masking and querying syntax to retrieve desired values.
Use vectorised ufuncs.

5. Introduction to Pandas

Create, manipulate, and alter Series and DataFrames with Pandas.
Define and change the indices of Series & Dataframes.
Use Pandas' functions and methods to change column types, compute summary statistics and aggregate data.
Read, manipulate, and write data from csv, xlsx, json and other structured file formats.

6. Data Cleaning with Pandas

Identify missing data and apply techniques to deal with it.
Deduplicate, transform and replace values.
Use DataFrame string methods to manipulate text data.
Write regular expressions which munge text data.

7. Data Manipulation with Pandas

Construct Pivot tables in Pandas.
Time series manipulation.
Stream data into Pandas to handle data size problems.

8. Methods for Visualising Data

Construct and tailor basic data visualisations using Matplotlib & Seaborn for both numeric & non-numeric data.
Meaningfully visualise aggregate data using Matplotlib and Seaborn.

Data Handling in Python Training Course in Norway

The Nordic country Norway, is in Northern Europe. Known for its stunning natural beauty, including fjords, mountains, and forests, Norway is also famous for its high standard of living and strong social welfare system. Norway's capital and largest city is Oslo. Tromsø, Bergen, Trondheim and Stavanger are the other tourist attracting cities of Norway.

Norway is a constitutional monarchy with King Harald V as the head of state. The country has a population of 5,425,270 as of January 2022. Norway is a relatively small country and has a relatively low population density, with much of its land area covered by forests, mountains, and fjords. Despite its small size, Norway is known for its rich cultural heritage, strong economy, and stunning natural beauty, which attracts millions of visitors every year. This Nordic country is also known for its winter sports, such as skiing and snowboarding, and is a popular destination for outdoor enthusiasts.

Norway has a long history of invention and is home to numerous more top-tier tech firms and research facilities, such as; Kongsberg Gruppen, Telenor, Atea, Evry and Gjensidige Forsikring.

Due to the country's high latitude, there are large seasonal variations in daylight. From late May to late July, the sun never completely descends beneath the horizon. Which attracts many tourists around the world to see the "Land of the Midnight Sun". Tourists mainly visit Sognefjord, Norway's Largest Fjord, Pulpit Rock, one of the most photographed sites in Norway and of course the capital; Oslo.

Oslo is considered the business center of Norway. It is the country's largest city and the capital of Norway. The city is home to many of Norway's largest and most important companies, as well as several international organizations and research institutions. Additionally, the city is a popular tourist destination, known for its scenic location on the Oslo Fjord, its many museums and cultural attractions, and its vibrant nightlife and dining scene. Some of the most popular museums in Oslo are The Norwegian Museum of Cultural History, The Nobel Peace Center, The National Museum of Art, Architecture, and Design, The Munch Museum and The Vigeland Museum.

Data Handling in Python Training in Norway

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Data Handling in Python Training in Norway

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