Module 1: Foundations of Data Mining and Python Setup

• Introduction to the Data Mining process (CRISP-DM methodology)
• Key Python libraries for data mining: Pandas, NumPy, and Matplotlib
• Installing and setting up the Python environment (Anaconda, Jupyter)
• Basic data structures and operations in Pandas (DataFrame, Series)
• Practical session: Setting up the Python environment and performing initial data loading and inspection using Pandas on a dataset.

Module 2: Data Preprocessing and Feature Engineering

• Handling missing values (imputation techniques: mean, median, mode)
• Data transformation: Normalization, standardization, and scaling
• Encoding categorical variables (One-Hot Encoding, Label Encoding)
• Advanced feature engineering: Creating interaction terms and polynomial features
• Practical session: Implementing various missing data imputation and scaling techniques on a raw customer dataset using Scikit-learn's preprocessing tools.

Module 3: Introduction to Classification (Decision Trees & k-NN)

• Understanding Supervised Learning and the classification task
• Decision Trees: Principles, Gini Impurity, and Information Gain
• k-Nearest Neighbors (k-NN): Distance metrics and optimal k selection
• Model training, prediction, and basic cross-validation techniques
• Practical session: Building and visualizing a Decision Tree model to classify customer churn, and optimizing the tree depth.

Module 4: Advanced Classification Techniques (Ensemble Methods)

• Introduction to Ensemble Learning (Bagging, Boosting, Stacking)
• Implementing the Random Forest algorithm for increased accuracy and stability
• Understanding Gradient Boosting Machines (GBM) and XGBoost/LightGBM
• Handling imbalanced datasets (SMOTE, class weighting)
• Practical session: Comparing the performance of a single Decision Tree against a Random Forest on a credit risk dataset and tuning hyperparameters.

Module 5: Regression for Predictive Modeling

• Understanding Linear Regression and its assumptions
• Implementing regularization techniques: Ridge and Lasso Regression
• Evaluating regression models: Mean Squared Error (MSE), R^2, and interpretation
• Applying Polynomial Regression for non-linear relationships
• Practical session: Building a predictive model using Regularized Linear Regression to forecast house prices based on various features.

Module 6: Clustering Analysis (Unsupervised Learning)

• Understanding Unsupervised Learning and the clustering task
• Implementing K-Means Clustering: Choosing the optimal number of clusters (Elbow method)
• Hierarchical Clustering: Agglomerative and Divisive methods
• Evaluating cluster quality using Silhouette Score and interpretability
• Practical session: Applying K-Means clustering to a retail dataset to segment customers based on purchasing behavior.

Module 7: Association Rule Mining and Market Basket Analysis

• Foundations of Association Rule Mining (Support, Confidence, Lift)
• Implementing the Apriori Algorithm to find frequent item sets
• Generating and interpreting association rules for business insights
• Applying the technique to transactional and sequence data
• Practical session: Performing Market Basket Analysis on a simulated e-commerce transaction log to uncover product cross-selling opportunities.

Module 8: Text Mining and Sentiment Analysis

• Introduction to Natural Language Processing (NLP) and Text Mining concepts
• Text preprocessing: Tokenization, stemming, lemmatization, and stop word removal
• Feature extraction from text: Bag-of-Words and TF-IDF vectors
• Building a basic Sentiment Analysis classifier using text data
• Practical session: Using Python's NLP libraries to clean a set of social media reviews and classify them as positive, negative, or neutral.

Module 9: Dimensionality Reduction and Evaluation Metrics

• Understanding the Curse of Dimensionality and its impact on models
• Implementing Principal Component Analysis (PCA) for data reduction and visualization
• Key classification evaluation metrics: Confusion Matrix, Precision, Recall, -Score, and ROC-AUC
• Model persistence: Saving and loading trained models
• Practical session: Applying PCA to a high-dimensional dataset to reduce the feature space while retaining 95% of the variance.

Module 10: Deployment and Ethical Considerations in Data Mining

• Introduction to basic model deployment concepts (e.g., using Flask for API)
• Model monitoring: Detecting concept drift and ensuring model freshness
• Understanding bias and fairness in data mining algorithms
• Data privacy and regulatory compliance (e.g., anonymization techniques)
• Practical session: Creating a simple, simulated prediction function that accepts new data, loads a pre-trained model, and returns a classification prediction.

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588