Fundamentals of Data Analytics - (Remote Instructor-Led Series)

Session 1 - 4 hours

• Introduction to Data Analytics
• Exploratory Data Analysis (EDA) (Visualization and Descriptive Statistics)
• Hands-on EDA

On the first day of the course the participants will be able to get a bird-eye view of the data analytics process. They will explore one of the four data analytics modules called exploratory data analysis, a necessary step to get the feel of the data. Learning will be reinforced via hands-on training using the DELFI Data Science Profile and / or Orange.

Session 2 - 4 hours

Data Preprocessing

• Data Preprocessing (including Principal Component Analysis)
• Hands-on Data Preprocessing
  
  

Supervised Machine Learning

• Decision Tree
• Hands-on Decision Tree

The second day will start with data preprocessing, a necessary step to clean and format the data before building machine learning models. Later in the day, supervised machine learning concepts will be introduced, and participants will be able to build data-driven models via a supervised machine learning algorithm called Decision Tree. Learning will be reinforced via hands-on training using the DELFI Data Science Profile and / or Orange.

Session 3 - 4 hours

Supervised Machine Learning

• Model Evaluation
• Hands-in Model Evaluation
• Regression (Linear and Logistics)

On the third day of the course, the participants will learn about model evaluation matrices (e.g., confusion matrix, ROC, AUC, etc.) and model evaluation methods (e.g., 10-fold cross validation) will also be discussed. Later in the day, regression analysis, particularly linear regression and logistic regression will be introduced. Learning will be reinforced via hands-on training using the DELFI Data Science Profile and / or Orange.

Session 4 - 4 hours

Ensemble Methods

• Hands-on Regression
• Ensemble Methods (Bagging, Boosting and Random Forest)
• Hands-on Ensemble Methods

On the fourth day of the course, participants will complete a hands-on session on regression analysis, after which they will be introduced to more advanced machine learning techniques of ensemble methods. They will be able to implement the complete data mining pipeline including model building and model evaluation using the DELFI Data Science Profile and / Orange.

Session 5 - 4 hours

Unsupervised Machine Learning

• Cluster Analysis (k-Means and Hierarchical)
• Hands-on Cluster Analysis
• Class feedback and wrap-up

On the last day of the course, the participants will be introduced to unsupervised machine learning algorithms. The course will end with a hands-on session on clustering using the DELFI Data Science Profile or Orange.