📊 Data Science Studies Repository

Welcome to my Data Science studies repository! Here you will find all the material, projects, and notes from the courses I am taking to deepen my knowledge in Data Science.

📚 Courses

1. [Formação Cientista de Dados: O Curso Completo]

• Status: ⏳ Doing
• Institution: Escola de Inteligência Artificial (Fernando Amaral)
• Description: Data Science course in which you will get to know and learn to apply all the main concepts and techniques to qualify and act as a Data Scientist, with explanatory and detailed videos for laypeople, practical examples of coding in R and Python using real data, explanations of solving formulas step by step
• Content: Introduction to R and Python Programming Languages, Data Cleaning and Processing, Graphs, Data Visualization and Dashboards, Statistics I(Sampling, Measures of Centrality and Variability, Probabilities, Normal Distribution), Statistics II(Confidence Intervals, Hypothesis Tests, Student's t Distribution, Binomial Distribution, Poisson Distribution, Chi Square, Anova), Linear Regression and Correlation, Time Series with Arima, Machine Learning(Applications, concepts, Classification, Feature Sizing, Category Coding, Clustering, Association Rules), Artificial Neural Networks and Deep Learning, Graphs and Social Networks, Natural Language Processing and Text Mining, including Transformers and GPT (ChatGPT), SQL and NoSQL, Spark with Databricks, Cloud Computing with AWS
• Projects: Links to projects developed in the course

2. [O curso completo de Banco de Dados e SQL, sem mistérios!]

• Status: ⏳ Doing
• Institution: Unidados (Felipe Mafra)
• Description: Brief description of the course
• Content: List of main topics covered
• Projects: Links to projects developed in the course

3. [Programação em Python do básico ao avançado]

• Status: ⏳ Doing
• Institution: Geek University
• Description: Brief description of the course
• Content: List of main topics covered
• Projects: Links to projects developed in the course

4. [Python para Finanças Análise de Dados e Machine Learning]

• Status: ✅ Finished
• Institution: IA Expert Academy(Jones Granatyr)
• Description: In this course you'll get a theoretical and mainly practical step-by-step overview of the main concepts of Finance and Investments, as well as implementation in the Python programming language and applications of Machine Learning to financial databases. What sets this course apart is that we will be using databases of Brazilian companies taken from the São Paulo Stock Exchange (BOVESPA), as well as solving exercises in every section. This way, you'll be able to practice immediately after learning the concepts! The content is divided into two parts: in the first you will learn the basic concepts of finance and in the second part we will apply machine learning to databases with financial information.
• Content: Extraction of financial databases from the Internet, Creation of dynamic charts for visualizing financial information, Analysis of histograms, boxplots, and line charts for interpreting databases, Calculation of simple return and logarithmic return, Risk calculation using statistical metrics such as standard deviation, variance, covariance, and correlation coefficient, Analysis of similar companies using the correlation coefficient, Calculation of Sharpe ratio and Markowitz for stock portfolio analysis, Asset allocation in a portfolio to reduce risks and increase profits, Use of intelligent optimization algorithms to select the best assets in a portfolio. We will implement the following algorithms: hill climb, simulated annealing, and genetic algorithms, Calculation of the famous CAPM (Capital Asset Pricing Model) for asset pricing, Implementation of Monte Carlo Simulations for stock price forecasting, Generation of best and worst-case price scenarios with Monte Carlo Simulations, Use of ARIMA and Facebook Prophet algorithms for stock price forecasting, Complete preprocessing of a database with characteristics of more than 300 BOVESPA companies, aiming to predict the best companies for long-term investment, Application of the k-means algorithm for clustering companies with similar characteristics, Visualization and exploration of Twitter texts about finance, as well as extracting the companies people are talking about and generating the most frequent topics/words, Creation of a sentiment classifier to indicate whether a text about finance is positive or negative
• Projects: Links to projects developed in the course

🔧 Technologies and Tools

• Languages: Python, R, SQL
• Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, TensorFlow, PyTorch
• Tools: Jupyter Notebook, Google Colab, Git, Tableau

🎯 Objectives

• Enhance Data Science skills: Exploring different courses and applying the knowledge acquired.
• Develop practical projects: Applying theories in real projects to strengthen understanding.

📧 Contact

For questions, suggestions, or collaborations, contact me:

• Email: [email protected] and [email protected].
• LinkedIn: LinkedIn
• GitHub: GitHub