Data Science

Aim of the Course:

The aim of this course is to provide participants with a robust foundation in Data Science while emphasizing practical proficiency in Python programming. With 20-30% of the curriculum dedicated to mastering Python tools and techniques and 70% focused on applying these skills to a wide range of data science methodologies—including regression, classification, clustering, reinforcement learning, natural language processing, deep learning, and model selection—participants will gain the expertise necessary to analyze and interpret complex data. This course combines an overview of Data Science and Python, offering a comprehensive learning experience.

For Whom:

The course is designed for all specialists interested in enhancing their skills in the domain of Data Science.

About the Trainer:

• More than 15 years of experience in IT, primarily in data analysis, processing, and architecture.
• Over 7 years of expertise in Data Science.
• Extensive experience in corporate finance, stock markets, pharmaceuticals, and medical technology.
• More than 10 years of mentoring in Data Science.
• Graduated in "Finance of Enterprises" and "State Regulation of the Economy."
• Certified specialist in Data Science and Data Analysis.

Duration of the Course:

• Duration: 4 months
• Frequency of Meetings: Twice a week
• Duration of Each Session: 1 hour 15 minutes

Learning Outcomes:

During the course, participants will learn to:

• Understand the basics of Python and Data Science.
• Preprocess data using Python techniques for cleaning and preparation.
• Apply regression analysis methods to predict continuous outcomes, including linear and polynomial regression.
• Implement classification techniques for categorical outcomes.
• Build and train deep learning models, including artificial neural networks and convolutional networks.
• Reduce dimensionality using techniques like PCA and LDA to simplify data while retaining essential features.
• Evaluate models through k-fold cross-validation, parameter tuning, and boosting with XGBoost.

Course Content:

• Module 1: Data Preprocessing with Python
• Module 2: Regression (Simple Linear, Multiple Linear, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression)
• Module 3: Classification (Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification)
• Module 4: Clustering (K-Means, Hierarchical Clustering)
• Module 5: Association Rule Learning (Apriori, Eclat)
• Module 6: Reinforcement Learning (Upper Confidence Bound, Thompson Sampling)
• Module 7: Natural Language Processing (Bag-of-words model and algorithms for NLP)
• Module 8: Deep Learning (Artificial Neural Networks, Convolutional Neural Networks)
• Module 9: Dimensionality Reduction (PCA, LDA, Kernel PCA)
• Module 10: Model Selection & Boosting (k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost)

Additional Features:

• The course includes home assignments, with questions discussed at the beginning of each session.
• Each participant will receive a certificate upon completion of the course.

Time:

• Days: To be determined.
  Format: Online
  Language: English

Requirements:

Participants must have at least an intermediate level of English.

Expected Results:

After completing the course, participants will:

• Enhance their data analysis skills.
• Clean and preprocess complex datasets using Python for effective analysis.
• Apply various regression and classification algorithms to solve real-world predictive problems.
• Implement clustering and association techniques to uncover patterns in data.
• Develop and deploy deep learning models for tasks involving images and text data.
• Evaluate and optimize machine learning models using best practices.
• Develop projects that can be showcased in their professional portfolios.