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Explore how predictive models can help businesses use data to identify risks and discover new opportunities.
|👨💼 Sponsor: Sungkyunkwan University (SKKU)|
|📚 Field: AI and Artificial Learning|
|🌐 Host: FutureLearn|
|🎓 Mode: Online|
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Discover how predictive analytics could transform your business
As businesses accrue more and more data about their customers – from their behavioural history to their transactions – being able to use ‘Big Data’ is becoming increasingly key to low-term business success.
In this course, you’ll understand how predictive analytics can be used to get useful information out of your data. You’ll identify how techniques like machine learning and data mining can help you become a data-centred project manager who can solve business problems, and drive revenue.
Learn how predictive analytics helps business
Predictive analytics encompasses a variety of statistical techniques in big data analysis and machine learning.
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Alongside data science experts at Sungkyunkwan University (SKKU), you’ll get foundational training in the concepts and principles that underlie data science and statistical modelling techniques.
See how data science can boost business performance
The course will take you through a series of real-world business problems, solving each by using key predictive analytics methods.
These include statistical methodologies and algorithms such as artificial neural networks, clustering, text mining, decision trees, and natural language processing.
Explore the relationship between predictive analysis and machine learning
You’ll discover the essentials of machine learning, which can be a vital tool in analysing and making use of data.
Discover the difference between supervised machine learning – where you can collect data based on previous experience – and unsupervised machine learning – where you can find new data patterns.
Gain practical big data skills
Along the way, you’ll learn how to use predictive tools to extract knowledge from data, connect actual business problems with data science solutions, and lead a data science-orientated team.
- The concept of data-driven decision making
- Supervised learning vs. Unsupervised learning
- Solving customer churning problem using classification techniques
- Identifying the same group of customers using clustering
- Forecasting the real estate price using an artificial neural network
Who is the course for?
This course would benefit an aspiring manager who wants to align their understanding of business and technical solutions and to lead comprehensive business solutions including data science teams.
What software or tools do you need?
Learners will require access to Microsoft Excel (or Excel Online) to open and edit datasheet files.
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