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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 R or Python – if this is not currently the case we suggest completing one of our R or Pythonfor 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 R that you will need as part of this journey.

Throughout the course you will engage with practical labs, activities, and discussions with one of our technical specialists. All modules involve the use of 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

Target Audience

Anyone wishing to expand their understanding of Maths and Statistics related to Data Science. This course will provide all the required pre-requisite statistical knowledge needed for our more in-depth programmes.

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
Apply methods for automating identification of outliers
Discuss appropriate handling of outliers
Practical Lab Activities with R

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 twsamples (e.g. taken at different times or from different locations)
- Determine the appropriate shape of a model and whether there are opportunities t linearise
Practical Lab Activities with R

Introduction to Probability

Interpret P() notation and calculate simple and conditional probabilities
Use Venn diagrams with set notation tcalculate probabilities
Use Tree diagrams and simple combinatorics tcalculate probabilities
Practical Lab Activities with R

Introduction to Distributions

Recognise what a probability or data distribution is
Identify when a distribution is considered tbe Binomial, Poisson, or Normal
Identify when a distribution can be treated as Normal and what this means for analytical methods
Practical Lab Activities with R Sampling
Critique different sampling techniques
Explain the impact a sampling or data gathering method may have on analytical model results
Recognise methods for estimating summary statistics for a population from a sample
Practical Lab Activities with R

Introduction to Hypothesis Testing

Recognise the steps required for a Hypothesis test from the set- up, 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 R

Linear Regression

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

Why have you chosen us?

I have attended a training from Bilginc IT Academy before and I was satisfied.

I have attended a training from a different provider and it was not helpful.

Other

How many employees do you have in your IT department?

0 – 50

50 – 250

250 – 1000

1000+

Statistics for Data Analysis in R Training Course in Netherlands

The Netherlands, which is also informally known as Holland, is a country located in Northwestern Europe with overseas territories in the Caribbean. The four largest cities are Amsterdam, Rotterdam, The Hague and Utrecht. Amsterdam is the country's most populous city and the nominal capital, while The Hague holds the seat of the States General, Cabinet and Supreme Court. Rotterdam is the second largest city and has the largest port in Europe. The importance of the Netherlands for Europe is so huge that it cannot be ignored. The country is a founding member of the European Union, Eurozone, G10, NATO, OECD, and WTO.

The tourist attractions of the Netherlands are windmills, canals, and tulips. Canals are an important part of Amsterdam's cityscape and Keukenhof, the Garden of Europe, is the largest public garden in the world. The National Museum Rijksmuseum and the Anne Frank House are must-see places in the Netherlands.

Enhance your IT skills with our comprehensive array of courses, spanning programming, software development, data science, and project management. Benefit from the convenience of choosing your preferred location in Netherlands as our experienced instructors deliver interactive training and real-world insights.

Statistics for Data Analysis in R Training in Netherlands

Target Audience

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Statistics for Data Analysis in R Training in Netherlands

Prerequisites

Target Audience

Outline

Upcoming Trainings