Training on Python for Advanced Data Analysis and Machine Learning

Course Objectives

By the end of this course, participants will be able to:

• Enhance Python programming skills for advanced data analysis.
• Master the use of key Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
• Develop proficiency in data cleaning, transformation, and preprocessing techniques.
• Perform advanced statistical analyses and hypothesis testing.
• Implement machine learning models for predictive analysis.
• Visualize complex datasets using advanced plotting techniques.
• Understand and apply time series analysis and forecasting methods.
• Optimize data analysis workflows for efficiency and scalability.
• Integrate Python with other data tools and environments.
• Prepare participants to handle real-world data analysis challenges with confidence.

Course Outline:

Module 1: Python Fundamentals for Data Analysis

• Deep dive into NumPy: array operations, linear algebra, random number generation
• Pandas: advanced data manipulation, time series analysis, performance optimization

Module 2: Exploratory Data Analysis (EDA) and Feature Engineering

• In-depth EDA techniques: correlation analysis, hypothesis testing, outlier detection
• Feature selection, creation, and transformation for model building

Module 3: Statistical Modeling with Python

• Linear regression, logistic regression, and model evaluation
• Time series analysis: ARIMA, forecasting
• Hypothesis testing and statistical inference

Module 4: Machine Learning Foundations

• Supervised and unsupervised learning overview
• Model evaluation metrics and cross-validation
• Hyperparameter tuning and model selection

Module 5: Classification Algorithms

• Decision trees, random forests, support vector machines
• Model interpretation and explainability

Module 6: Clustering Algorithms

• K-means, hierarchical clustering, DBSCAN
• Cluster evaluation and visualization

Module 7: Natural Language Processing (NLP)

• Text preprocessing, tokenization, stemming, and lemmatization
• Sentiment analysis, text classification, and topic modeling

Module 8: Deep Learning with Python

• Introduction to neural networks and deep learning
• Building and training neural networks using TensorFlow/Keras
• Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)

Module 9: Big Data Processing with Python

• Introduction to Apache Spark and PySpark
• Distributed data processing and analysis
• Handling large datasets efficiently

Module 10: Data Visualization and Communication

• Advanced data visualization techniques with Plotly and Seaborn
• Interactive dashboards and storytelling
• Effective communication of data insights to stakeholders

Customized Training

This training can be tailored to your institution needs and delivered at a location of your choice upon request.

Requirements

Participants need to be proficient in English.

Training Fee

The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.

Certification

A certificate from Ideal Sense & Workplace Solutions is awarded upon successful completion.

Accommodation

Accommodation can be arranged upon request. Contact via email for reservations.

Payment

Payment should be made before the training starts, with proof of payment sent to outreach@idealsense.org.
For further inquiries, please contact us on details below:

Email: outreach@idealsense.org

Mobile: +254759708394