Python for Data Science

Course Content:

Introduction to Python and its Applications in Data Science:
Installation and configuration of the Python development environment.
Introduction to key libraries for data science: NumPy, pandas, Matplotlib and Seaborn.
Python applications in data science, including exploratory analytics and predictive modeling.

Fundamentals of Python for Data Science:
Python data structures: lists, dictionaries, sets, and tuples.
Flow control and error handling: conditionals, loops and exceptions.
Functions and modules: creating and using functions, importing modules and packages.

Data manipulation and cleaning with Pandas:
Use of pandas for data loading, cleaning and manipulation.
Advanced data manipulation techniques, including grouping, pivoting and merging of datasets.
Handling missing data, duplicates and transformations.

Data visualization with Matplotlib and Seaborn:
Creating static and dynamic charts using Matplotlib and Seaborn.
Customization of visualizations: styles, labels, legends and annotations.
Development of interactive dashboards with Plotly and Dash.

Exploratory Data Analysis (EDA):
Exploratory analysis techniques to identify patterns, trends and anomalies in data.
Application of descriptive statistics and correlation analysis.
Use of visualization tools to discover insights and prepare data for modeling.

Introduction to Machine Learning with Scikit-Learn:
Basic principles of machine learning and its implementation in Python using Scikit-Learn.
Creation and evaluation of supervised and unsupervised learning models.
Cross-validation techniques, hyperparameter tuning and feature selection.

Development of Predictive Models:
Implementation of predictive models for regression and classification problems.
Evaluation of model performance using metrics such as accuracy, recall, F1-score and ROC-AUC.
Model regularization and optimization techniques to improve generalization.

Textual Data Processing and NLP:
Introduction to natural language processing (NLP) and its application in Python.
Text analysis techniques, including tokenization, lemmatization and sentiment analysis.
Use of NLP libraries such as NLTK and SpaCy for textual data processing.

Integration and Automation:
Python integration with SQL and NoSQL databases for data management and analysis.
Script development and automation of data science workflows.
Creation of applications and APIs using Flask to expose machine learning models.

Final Project:
Development of a comprehensive project covering data collection, manipulation, analysis and modeling using Python.
Presentation and evaluation of the project with feedback from data science experts.

Additional Benefits:

Certification in Python for Data Science:
Upon completion of the course, you will receive an industry-recognized certification that validates your proficiency in using Python for Data Science.

Access to Exclusive Tools and Resources:
You’ll gain access to development environments, datasets and advanced analysis tools to facilitate your learning and practical application.

Mentoring and Networking:
During the course, you will have the opportunity to interact with expert data science mentors and participate in networking events with other industry professionals.

Course duration: 4 months

Modality: 100% online, with live sessions and access to recorded content.