Applied Data Science and Machine Learning with Python Training: Strategic Boot Camp for Data Analysts

Module 1: Python Programming Fundamentals for Data Science

ü  Introduction to Python syntax and PEP 8 standards

ü  Data structures: Lists, Dictionaries, Sets, and Tuples

ü  Control flow, loops, and conditional statements

ü  Writing reusable functions and Lambda expressions

ü  Practical Session: Automating a manual data formatting task using Python scripts

Module 2: Numerical Computing with NumPy

ü  Understanding N-dimensional arrays (ndarrays)

ü  Array broadcasting and vectorized operations

ü  Mathematical and statistical functions in NumPy

ü  Handling large datasets efficiently in memory

ü  Practical Session: Implementing matrix operations for custom data transformations

Module 3: Data Manipulation and Wrangling with Pandas

ü  Working with Series and DataFrames

ü  Loading data from CSV, Excel, and SQL databases

ü  Data cleaning: Handling duplicates and outliers

ü  Multi-indexing and pivoting tables

ü  Practical Session: Cleaning and merging disparate sales datasets into a master file

Module 4: Exploratory Data Analysis (EDA) and Statistical Foundations

ü  Descriptive statistics: Mean, Median, Mode, and Variance

ü  Probability distributions and Central Limit Theorem

ü  Hypothesis testing and p-values

ü  Correlation vs. Causation analysis

ü  Practical Session: Performing a full EDA on a financial dataset to find growth drivers

Module 5: Data Visualization with Matplotlib and Seaborn

ü  Creating line, bar, and pie charts

ü  Statistical plotting: Histograms, Boxplots, and Heatmaps

ü  Customizing plot aesthetics for executive reports

ü  Interactive plotting basics

ü  Practical Session: Creating a visual data story regarding customer churn trends

Module 6: Introduction to Machine Learning Frameworks

ü  The Machine Learning workflow: Training, Validation, and Test sets

ü  Types of ML: Supervised, Unsupervised, and Reinforcement Learning

ü  Scikit-Learn API structure and estimators

ü  Bias-Variance Tradeoff

ü  Practical Session: Setting up a standard ML environment and loading baseline datasets

Module 7: Linear Regression: Building Predictive Models

ü  Simple and Multiple Linear Regression

ü  Understanding Coefficients and Intercepts

ü  Assumptions of Linear Regression

ü  Interpreting R-squared and Adjusted R-squared

ü  Practical Session: Predicting real estate prices based on historical market data

Module 8: Classification Techniques: Logistic Regression and KNN

ü  Binary vs. Multi-class classification

ü  The Sigmoid function and Decision Boundaries

ü  K-Nearest Neighbors (KNN) algorithm mechanics

ü  Choosing the optimal 'K' value

ü  Practical Session: Building a credit risk classifier for loan applicants

Module 9: Decision Trees and Random Forests

ü  Information Gain and Gini Impurity

ü  Pruning trees to prevent overfitting

ü  Ensemble methods: Bagging and Boosting

ü  Feature importance in Random Forests

ü  Practical Session: Building a Random Forest model to predict employee attrition

Module 10: Support Vector Machines (SVM) and Kernel Methods

ü  Linear and Non-linear decision boundaries

ü  The Margin and Hyperplanes

ü  Kernel trick: RBF, Polynomial, and Sigmoid

ü  C and Gamma parameters

ü  Practical Session: Image recognition basics using SVM on a digit dataset

Module 11: Unsupervised Learning: K-Means and Hierarchical Clustering

ü  Identifying hidden structures in unlabeled data

ü  The Elbow Method for choosing clusters

ü  Centroid-based vs. Density-based clustering

ü  Dendrograms and Hierarchical approaches

ü  Practical Session: Segmenting customers based on purchasing behavior patterns

Module 12: Principal Component Analysis (PCA) and Dimensionality Reduction

ü  The curse of dimensionality

ü  Eigenvalues and Eigenvectors

ü  Explained variance ratio

ü  Reducing noise in high-dimensional data

ü  Practical Session: Reducing a 50-variable dataset into 3 components for visualization

Module 13: Feature Engineering and Feature Selection Strategies

ü  One-Hot Encoding and Label Encoding

ü  Feature Scaling: Min-Max Scaling and Standardization

ü  Creating derived features from timestamps

ü  Recursive Feature Elimination (RFE)

ü  Practical Session: Engineering 10 new features from a raw e-commerce log file

Module 14: Model Evaluation Metrics and Cross-Validation

ü  Confusion Matrix: Precision, Recall, and F1-Score

ü  ROC Curves and AUC (Area Under the Curve)

ü  K-Fold Cross-Validation techniques

ü  Mean Absolute Error (MAE) and RMSE for regression

ü  Practical Session: Validating a healthcare diagnostic model to minimize False Negatives

Module 15: Hyperparameter Tuning and Optimization

ü  Manual tuning vs. Automated tuning

ü  GridSearchCV vs. RandomizedSearchCV

ü  Tuning tree depth and learning rates

ü  Pipelines for streamlined optimization

ü  Practical Session: Optimizing a Gradient Boosting model to achieve 95% accuracy

Module 16: Handling Imbalanced Datasets and Missing Data

ü  Oversampling (SMOTE) and Undersampling techniques

ü  Advanced Imputation strategies (KNN Imputer)

ü  Evaluation metrics for imbalanced classes

ü  Weight adjustment in algorithms

ü  Practical Session: Building a fraud detection model with highly skewed transaction data

Module 17: Natural Language Processing (NLP) Essentials

ü  Tokenization, Stemming, and Lemmatization

ü  Bag of Words and TF-IDF vectors

ü  Sentiment Analysis using NLTK and TextBlob

ü  Introduction to Word Embeddings

ü  Practical Session: Analyzing social media sentiment for a brand launch

Module 18: Final Capstone Project: End-to-End Data Science Pipeline

ü  Defining a business problem and data collection

ü  Full pipeline: Cleaning, EDA, Modeling, and Tuning

ü  Presenting findings and business recommendations

ü  Exporting models for production use

ü  Practical Session: Complete end-to-end development and presentation of a personal ML project

About Our Trainers

Our training faculty comprises Lead Data Scientists and Machine Learning Engineers with over 15 years of industry experience across the Finance, Healthcare, and Tech sectors. They are proficient in deploying production-grade Python models and have contributed to open-source data science libraries. Each trainer holds advanced certifications and has a proven track record of mentoring analysts into high-level data roles.

Quality Statement

Phoenix Center for Policy, Research and Training is committed to technical excellence. Our courses are updated monthly to reflect the latest versions of libraries like Scikit-Learn and Pandas. We guarantee a high-impact learning environment with a focus on code efficiency, ethical AI practices, and actionable business intelligence.

Tailor-Made Courses

We understand that every organization has unique challenges and opportunities as well as unique training needs. Phoenix Training Center offers tailor-made courses designed to address specific requirements and challenges faced by your team or organization. Whether you need a customized curriculum, a specific duration, or on-site delivery, we can adapt our expertise to provide a training solution that perfectly aligns with your objectives. We can customize this Course to focus on your industry, specific risk profile, or internal stakeholder dynamics. Contact us to discuss how we can create a bespoke training program that maximizes value and impact for your team. For further inquiries, please contact us on Tel: +254720272325 / +254737296202 or Email training@phoenixtrainingcenter.com

Admission Criteria

ü  Participants should be reasonably proficient in English. 

ü  Applicants must live up to Phoenix Center for Policy, Research and Training admission criteria.

Terms and Conditions

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

ü  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.

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

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

ü  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@phoenixtrainingcenter.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 / +254737296202

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:

ü  Groups of 5 People and Above – Cheque Payments to: Phoenix Center for Policy, Research and Training Limited should be paid in advance, 5 days to the training.

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

ü  Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

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

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

ü  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.

Accommodation and Airport Transfer

For physical training attendees, we can assist with recommendations for accommodation near the training venue. Airport pick-up services can also be arranged upon request to ensure a smooth arrival. Please inform us of your travel details in advance if you require these services. For reservations contact the Training Officer on Email: training@phoenixtrainingcenter.com or on Tel: +254720272325 / +254737296202