We can host this training at your preferred location. Contact us!

This two day course is designed for those who analyse data or who are creating machine learning models, but who wish to firm their understanding in core concepts as well as expanding into types of data distributions, inferential statistics (hypothesis tests), statistical significance, and a deeper understanding of how linear regression works. It is expected that you will have experience with a programming language used for data analysis such as Python or R – if this is not currently the case we suggest completing one of our Python or R for Data Handling courses.

As well as providing a business context to using core concepts such as averages, spread, and interpreting analyst visualisations, you will take this knowledge further and learn how distributions, sampling, and hypothesis testing can be used to analyse data in an organisation and in automatically highlighting significant results or anomalies.

If you are on a learning journey with Machine Learning and AI this course will give you a strong starting point in the statistical methods that underpin a large number of algorithms without overloading you with too many mathematical formulae or notations that are otherwise commonly used to communicate advanced mathematics. Your focus will be on business problems and applying tools such as Python or R that you will need as part of this journey.

If you wish to expand your understanding of Maths and Statistics related to Data Science then this course will give you all the required pre-requisite statistical knowledge needed for our more in depth programmes.

Throughout the course you will engage with practical labs, activities, and discussions with one of our technical specialists. All modules involve the use of Python or R to practice the techniques taught – setting you up to succeed in analysing, interpreting, and getting value from your data.

Minimum of GCSE Maths or equivalent
Experience with Python or R for Data Handling

Central Tendency, Variation, and Outliers

Using an appropriate software tool, calculate:
- Mean, Mode, Median, Mid-range
- Population and Sample Standard Deviation & Variance
- Inter-Quartile Range
Discuss when the above measures are appropriate
Apply methods for automating identification of outliers
Discuss appropriate handling of outliers
Practical Lab Activities with Python

Visualisations and Skew

Using an appropriate software tool, create:
- Histograms
- Scatter Plots
Use these to:
- Identify skew and the effect this may have on modelling
- Identify the location of the averages
- Compare two samples (e.g.taken at different times or fromdifferent locations)
- Determine the appropriate shape of a model and whetherthere are opportunities to linearise
Practical Lab Activities with Python

Introduction to Probability

Interpret P() notation and calculate simple and conditionalprobabilities
Use Venn diagrams with set notation to calculate probabilities
Use Tree diagrams and simple combinatorics to calculateprobabilities
Practical Lab Activities with Python

Introduction to Distributions

Recognise what a probability or data distribution is
Identify when a distributionis considered to beBinomial, Poisson,or Normal
Identify when a distribution can be treated as Normal and whatthis means for analytical methods
Practical Lab Activities with Python

Sampling

Critique different sampling techniques
Explain the impact a sampling or data gathering method mayhave on analytical model results
Recognise methods for estimating summary statistics for apopulation from a sample
Practical Lab Activities with Python

Introduction to Hypothesis Testing

Recognise the steps required for a Hypothesis test from thesetup, assumptions, testing, and interpretation of p-values
Identify a variety of tests and when they are used
Evaluate the output of tests from an appropriate software tool
Practical Lab Activities with Python

Linear Regression

Recognise when a linear regression is an appropriate method touse
Interpreting y = mx + c
Evaluate linear models
Practical Lab Activities with Python

Statistics for Data Analysis 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.

Statistics for Data Analysis in Python Training in Norway

Upcoming Trainings

Statistics for Data Analysis in Python Training in Norway

Prerequisites

Outline

Upcoming Trainings