The training is for professional development purposes, and the certificate obtained does not replace the MYK authorization certificate in trainings where the MYK authorization certificate is mandatory.

Introduction and Basic Python Knowledge:

Introduction to Python: Basics of the Python programming language, data structures, and syntax.
Python Libraries: Introduction and usage of popular libraries such as NumPy, Pandas, Matplotlib, Seaborn, and SciPy.
Data Structures: Exploring Python data structures like lists, dictionaries, sets, and tuples.

Data Analysis and Manipulation:

Data Manipulation with Pandas: DataFrame and Series structures, reading, writing, and editing data.
Data Cleaning: Working with missing data, handling erroneous data, performing operations with data types, and data cleaning techniques.
Data Transformation and Processing: Filtering, grouping, creating pivot tables, and transformation operations.
Data Visualization: Creating visualizations and graphs using Matplotlib and Seaborn.

Statistical Analysis and Modeling:

Basic Statistics: Key statistical concepts such as data distribution, mean, median, variance, standard deviation, and correlation.
Statistical Tests: Applying common statistical tests like t-test, chi-square test, ANOVA, and regression analysis.
Basics of Machine Learning: What is machine learning? Differences between supervised and unsupervised learning.

Machine Learning Algorithms:

Regression Models: Linear regression, logistic regression, polynomial regression, and their implementation in Python.
Classification Algorithms: Decision trees, random forests, k-nearest neighbors (KNN), support vector machines (SVM), and their implementation in Python.
Clustering Algorithms: Introduction to K-means, hierarchical clustering, and DBSCAN algorithms, along with their applications.
Model Evaluation: Calculating and analyzing model evaluation metrics such as accuracy, error rate, F1 score, and ROC-AUC.

Data Mining and Deep Learning:

Data Mining Techniques: Data mining processes, pattern recognition, and classification in data mining applications.
Introduction to Deep Learning: Artificial neural networks, backpropagation algorithm, deep learning libraries (TensorFlow, Keras), and their applications.

Project and Hands-on Work:

Real-World Projects: Applying all the techniques learned during the course by working on real-world datasets.
Data Science Applications: Data science applications in various sectors such as healthcare, finance, e-commerce, and social media.

Skills Acquired by the End of the Training:

• Develop data analysis and machine learning projects using Python.
• Skills in data cleaning, processing, and visualization.
• Implement and evaluate machine learning algorithms.
• Create deep learning and AI applications.
• Gain experience in developing projects with real-world datasets.

Certifications and Career Opportunities:

• Participants will receive a certificate demonstrating their proficiency in Python for Data Science at the end of the training.
• Career opportunities in data science, machine learning, and artificial intelligence.