Big data mining for financial institutions and marketers
Profiles that can prepare this certification contents:
Financiers, Banqueters, Marketers, Managers, Administrative and financial directors, Data engineer, statistical engineer, Developer engineer, Applied Mathematics engineer and more.
Global knowledge to be acquired to pass this certification:
+ Dominate the basics and foundations of data mining; from information theory to machine learning.
+ Dominate a variety of tools, perspectives and approaches to be able to identify the most appropriate methods and models to use to solve each specific case.
+ Ability to use different types of data in real time to make complex predictions and large-scale calculations.
+ Ability to apply all these techniques in marketing, finance etc.
Detailed plan of preparation:
1) Data mining Vs machine learning Vs Big Data
2) Unsupervised learning
– Hierarchical ascending classification (Scipy library (python)).
– K-means (Scikit-learn library (python)).
– Choice of the number of clusters (Scikit-learn library (python)).
3) Supervised learning
– Decision tree (Scikit-learn library (Python)).
– Performance evaluation of classification models (confusion matrix, accuracy, recall, precision, f1-measure)
– Random forest (Scikit-learn library (Python))
– Artificial neural networks (Scikit-learn library (Python))
– Deep learning (DFFNN, CNN, RNN) (Keras library (Python), Tensorflow library (Python)).
4) Big Data Machine Learning with Apache Spark (pyspark)
– RDD Vs DataFrame
– Spark ML library