Karlebovej 91, 3400 Hillerød | Krajbjergvej 3, 8541 Skødstrup
70 22 29 29
08:30 - 17:00

Predictive Modelling, Data Science and Big Data

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Kursusinfo

  • Dette kursus varer 2 dage
  • Der medfølger kursusmateriale til dette kursus
  • Dette kursus koster 3 klip på dit klippekort.
  • Fuld forplejning (Morgenmad, frokost, kage, kaffe og sodavand ad libitum)
  • Du har i alt adgang til din kursus-pc i 3 uger

Varighed

Dette kursus varer 2 dage

Materialer

Der medfølger kursusmateriale til dette kursus

Klip på klippekort

Dette kursus koster 3 klip på dit klippekort.

Forplejning

Fuld forplejning (Morgenmad, frokost, eftermiddagskage samt kaffe og sodavand ad libitum)

Remote adgang

Du har i alt adgang til din kursus-pc i 3 uger

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Dette kursus er en introduktion til en række af de fundamentale færdigheder og værktøjer for dig som aspirerer til at blive Data Scientist. Dette inkluderer Big Data og Machine Learning.

Kurset er en teoretisk gennemgang af de værktøjer og muligheder du kan opnår ved brug af blandt andet Big Data og Machine Learning. Et godt kursus at starte med, hvis du vil have et godt overblik inden du kaster dig fuldt ud emnet.

Summary

This course is an introduction to a range of fundamental skills, techniques and tools for those aspiring to become Data Scientists. These include Big Data & Machine Learning.

Data Science, Predictive Modelling and Big Data skills are of vital and growing importance in commercial, government, commercial and not-for-profit organisations. Those in the Management, Product, Risk and IT functions benefit from skills and literacy in this area.

This two-day course introduces a range of techniques as they are commonly used in business, and provides practical experience in their use.

Duration

2 Days

Objectives

Attendees should, by the end of the course:

  • Learn fundamentals of predictive modelling and experience using a range of methods.
  • Have improved their ability to assess the effectiveness and fitness for purpose of any predictive modelling tool or technique.
  • Have experience with a range of unsupervised data techniques.
  • Be exposed to Big Data.

Audience

This course is suitable for anyone in management, administrative, product, marketing, finance, risk and IT roles who work with data and want to become acquainted with modern data analysis tools.

Additional Notes

Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice and other services to build your organisation's strategic and operational analytics capability.

Our courses include:

  • Predictive Modelling, Data Science and Big Data
  • Forecasting and Trend Analysis
  • Data Visualization
  • Data Analytics for Fraud and Anomaly Detection in Forensics and Security
  • Data Analytics for Campaign Marketing, Targeting and Insights
  • Data Analytics for Insurance Claims analysis
  • Data Analytics for Retail Marketing and Pricing
  • Data Analytics for the Web
  • Working with Data : Analysis and Report Writing for Everybody

Outline

This course will provide a conceptual overview and practical hands-on experience of a wide range of key tools, techniques and processes.

At the heart of the data mining toolkit is the suite of predictive modelling methods. Accordingly, the course will develop attendees' literacy in the strengths, characteristics and correct application of a range of predictive modelling methods, from relatively simple linear models through to complex and powerful Random Forests, Support Vector Machines, Decision Trees, Gradient Boosting Machines and Neural Networks will be covered along the way.

It will also teach the correct framing of predictive modelling problems, suitably preparing data, evaluating model accuracy and stability, interpreting results and interrogating models.

The two key styles of predictive modelling - operational for targeting and explanatory for insights - will be described and distinguished.

As well as predictive modelling, the course will cover a range of other key data mining tools, including:

  • Data exploration and visualisation: univariate summaries, correlation matrices, heat maps, hierarchical clustering.
  • Principal Components Analysis – used to segment and interpret multivariate data.
  • Cluster analysis – used for customer segmentation and anomaly detection.
  • Other "unsupervised" outlier detection tools.
  • Frequent item set analysis.
  • Association analysis – used in retail market basket analysis and the assessment of risk groupings.
  • Link and network analysis visualisation – which provide a simple and compelling way to communicate and analyse relationships, and are commonly applied in forensics, human resources and law enforcement.

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