| 1 | Python Programming Fundamentals
History and philosophy of Python; Installation of the development environment; Basic syntax and program structure; Conditional structures: if, else, elif; Repetition structures: for, while; Lists, tuples, and sets; Dictionaries and string manipulation; Functions: definition, parameters, and return values; Modules and code organization. | 8pm |
| 2 | Python Programming for Data Science
Data manipulation with NumPy and Pandas; Introduction to NumPy (arrays, mathematical operations); DataFrame manipulation with Pandas; Reading and writing data (CSV, Excel, SQL); Indexing and filtering data; Data visualization with Matplotlib and Seaborn; Creating simple graphs; advanced statistical graphs with Seaborn; Chart customization (titles, labels, colors); Visualizing time series; Building subplots and complex visualizations. | 8pm |
| 3 | Statistics for Data Science
Introduction to Descriptive Statistics; Basic concepts: mean, median, mode, variance, standard deviation; Data visualization: histograms, boxplots; Probability for Data Science; Basics of probability; Probability distributions: normal, binomial, Poisson; Central Limit Theorem; Sampling and Estimation; Sampling methods; Estimation of population parameters; Confidence intervals. | 8pm |
| 4 | Hypothesis Testing and Statistical Inference
Introduction to Statistical Inference; Fundamental Concepts; Population, Sample, and Statistical Parameters; Types of Inference: Estimation and Hypothesis Testing; Parametric Tests: Student's t-Test (one- and two-sample), ANOVA; Proportion Tests; Nonparametric Tests: Wilcoxon, Mann-Whitney, Kruskal-Wallis; Chi-Square; Regression and Correlation Analysis; Construction of Confidence Intervals; Interpretation of Intervals; Type I and Type II Errors; Strategies for Minimizing Errors. | 8pm |
| 5 | SQL and NoSQL Databases
Introduction to Databases; Fundamental Database Concepts; Data Modeling: Entities, Attributes, Relationships; Differences Between SQL and NoSQL Databases; Relational Databases (SQL); Table Structure and Data Types; SQL Queries: SELECT, WHERE, JOIN; Data Manipulation: INSERT, UPDATE, DELETE; Indexes and Query Optimization; Relational Data Modeling; Table Normalization; Primary and Foreign Keys; Relationships: One-to-One, One-to-Many, Many-to-Many; NoSQL Databases; Types of NoSQL Databases (Document, Key-Value, Columnar, Graph); Advantages and Disadvantages Compared to SQL Databases; Examples of Popular NoSQL Databases. | 8pm |
| 6 | Local, Remote and Social Media Data Collection
Introduction to ETL (Extraction, Transformation, and Load); Definition and importance of ETL; ETL process in data projects; Local Data Extraction; Importing data from local sources; Reading data from CSV files, Excel, and local databases; Using APIs for data extraction; Remote Data Extraction; Accessing APIs and web services for data extraction; Integration with cloud data sources; Security and authentication in remote environments; Social Media Data Extraction; Collecting data from platforms such as Twitter, Facebook, and LinkedIn; Using social media APIs; Processing unstructured data. | 8pm |
| 7 | Data Processing – Feature Engineering, Outliers
Introduction to Data Processing; Importance of data processing in Data Science; Difference between raw data and prepared data; Initial data exploration; Feature Engineering; Selection and creation of predictor variables; Variable transformation: normalization and standardization; Creation of dummy variables for categorical data; Treatment of missing values; Outlier detection and treatment; Identification of outliers: statistical and graphical methods; Strategies for dealing with outliers; Impact of outliers on statistical modeling; Advanced outlier detection techniques; Advanced Transformations on Variables; Application of log, square root, and other transformations; Discretization of continuous variables; Creation of composite variables; Feature Scaling and Normalization; Min-Max and Z-score normalization; Data Imputation Techniques; Methods for filling in missing values; Statistical and model imputation; Evaluation of the effectiveness of imputation techniques. | 8pm |
| 8 | Exploratory Data Analysis – Univariate Analysis
Differentiation between qualitative and quantitative data; Identification of categorical and numerical variables; Transformations of variables for univariate analysis; Descriptive statistics and measures of central tendency; Calculation and interpretation of mean, median, and mode; Use of measures of dispersion such as standard deviation and interquartile range; Practical applications in real data sets; Graphical visualization for univariate analysis; Histograms and density plots; Box plots and their insights; Bar and sector charts for categorical variables; Scatterplots for identifying patterns; Outlier analysis; Univariate analysis in categorical data; Frequency and percentage tables; Bar charts and sector diagrams for categorical data. | 8pm |
| 9 | Exploratory Data Analysis – Bivariate Analysis
Types of Variables and Measures of Association; Categorical and Quantitative Variables; Correlation Coefficient for Quantitative Variables; Contingency Tables and Association Tests for Categorical Variables; Visualization of Relationships; Scatterplots for Quantitative Variables; Bar Charts and Heatmaps for Categorical Variables; Box Plots and Violin Plots for Comparing Distributions. | 8pm |
| 10 | Supervised Learning – Trees and Forests
Decision Trees; Basic Concepts of Decision Trees; Tree Construction: Splitting Criteria, Information Gain; Tree Pruning to Avoid Overfitting; Random Forest Algorithms; Understanding the Concept of Ensemble Learning; Structure and Operation of Random Forests; Parameters and Adjustments for Model Optimization; Gradient Boosting; Overview of the Boosting Algorithm; Gradient Boosting and its Variants (XGBoost, LightGBM); Hyperparameter Tuning and Interpretation of Results; Practical Applications of Trees and Forests; Implementation of Tree and Forest Models in Python; Feature Selection and Feature Engineering; Performance Evaluation and Evaluation Metrics; Handling Imbalanced Data; Strategies for Dealing with Imbalanced Data Sets; Sampling and Weighting Techniques for Minority Classes; Model Interpretation; Tools for Interpreting Tree Models; Feature Importance Analysis; Visualization of Decision Trees. | 8pm |
| 11 | Supervised Learning – Linear Regression
Simple Linear Regression; Formulation of simple linear regression; Parameter estimation: least squares method; Interpretation of coefficients; Multiple Linear Regression; Extension of linear regression to multiple independent variables; Assessment of multicollinearity; Interpretation of results in multiple regression; Evaluation of Regression Models; Performance metrics: R², mean squared error (MSE), mean absolute error (MAE); Cross-validation; Overfitting and underfitting; Regularization in Regression; Ridge (L2) and Lasso (L1) regression; Selection of regularization parameters; Comparison between regularized and non-regularized models. | 8pm |
| 12 | Unsupervised Learning – Clustering
Introduction to Unsupervised Learning; Definition of unsupervised learning; Differences between supervised and unsupervised learning; Practical applications in Data Science; Basic concepts of clustering; Definition of clustering; Types of clustering algorithms; Similarity and dissimilarity measures; Hierarchical clustering algorithms; Agglomerative vs. Divisive; Linkage methods (single, complete, average); Construction and interpretation of dendrograms; 4. K-Means and K-Medoids; Principles of the K-Means algorithm; Selection of the optimal number of clusters (K); Adaptation of K-Medoids to non-Euclidean data; 5. Mixture of Gaussians algorithms; Probabilistic clustering model; Expectation-Maximization (EM) for parameter estimation; Applications in modeling complex distributions. | 8pm |
| 13 | Artificial Neural Networks and Deep Learning
Introduction to Artificial Neural Networks (ANNs); Neurons and their interconnections; Basic architecture of a neural network; Activation functions and their importance; Neural Network Training; Backpropagation: fundamental training algorithm; Weight and bias optimization; Gradient descent and its variations; Convolutional Neural Networks (CNNs); Basic principles of convolution; Convolutional, pooling, and fully connected layers; Applications in image processing; Recurrent Neural Networks (RNNs); Temporal structure in data; Short-term and long-term memory in RNNs; Applications in sequence processing; Deep Learning and Advanced Architectures; Overview of the concept of Deep Learning; Deep Neural Networks (DNNs); Advanced architectures such as autoencoders and generative networks; Frameworks and Libraries in Deep Learning; Use of popular frameworks such as TensorFlow and PyTorch; Building and training models with specific APIs. | 8pm |
| 14 | Sampling – Downsampling and Upsampling
Definition of sampling in Data Science contexts; Importance of sampling in data analysis; Comparison between population and sample; Downsampling and Upsampling; Concepts of downsampling (subsampling) and upsampling (oversampling); Applications and suitable scenarios for each technique; Advantages and disadvantages of downsampling and upsampling; Downsampling techniques; Random downsampling; Stratified downsampling; Clustering-based downsampling; Upsampling techniques; Simple data duplication; Synthetic upsampling (SMOTE – Synthetic Minority Over-sampling Technique) and Interpolation-based upsampling. | 8pm |
| 15 | Natural Language Processing
Introduction to Natural Language Processing (NLP); Definition and scope of NLP; Practical applications in different sectors; Challenges and complexities in natural language processing; Text preprocessing with NLTK; Text tokenization; Stop word removal; Stemming and lemmatization; Lowercase text transformation; Word frequency analysis; Construction of word histograms; Identification of most frequent words; Visualization of word clouds; Text feature extraction; Identification of named entities; Sentiment analysis; Extraction of key terms; Topic modeling; Use of models such as Latent Dirichlet Allocation (LDA); Application to textual documents; Interpretation and evaluation of results; Text classification with NLTK; Data preparation for classification; Implementation of classifiers using NLTK; Performance evaluation; Natural Language Processing in practical applications; Development of simple chatbots; Sentiment analysis in social networks; Applications in text-based recommendation systems; Combining NLTK with other libraries like NumPy and Pandas. | 8pm |
| 16 | Ensembles and Model Performance Evaluation
Basics of model training and testing; Ensembles: Basic Concepts; Definition of ensembles and their importance; Types of ensembles: bagging, boosting, stacking; Random Forest as an example of an ensemble; Bagging and Random Forest; Bagging: Bootstrap Aggregating; Random Forest: construction and application; Advantages and disadvantages; Boosting and Gradient Boosting; Principles of boosting; Gradient Boosting: algorithm and implementation; XGBoost and LightGBM as popular variants; Stacking and Advanced Ensemble Models; Stacking: model combination; Advanced ensemble models (e.g., Ensemble Neural Networks); Strategies for building custom ensembles; Model Performance Evaluation; Common metrics: accuracy, precision, recall, F1-score; ROC and AUC curves; Confusion Matrix and interpretation of results; Cross-Validation and Overfitting; Cross-validation techniques; Identification and mitigation of overfitting. | 8pm |
| 17 | Optimization and Tuning
Introduction to Hyperparameter Optimization; Definition of hyperparameters and their importance; The role of optimization in improving model performance; Manual Hyperparameter Selection; Evaluation of hyperparameters based on intuition; Manual experimentation for fine-tuning; Automatic Hyperparameter Optimization; Optimization algorithms such as Grid Search and Random Search; Python tools and libraries for automatic optimization; | 8pm |
| 18 | Bayesian optimization; Fundamentals of Bayesian optimization; Practical implementation with libraries such as scikit-optimize or hyperopt; Hyperparameter tuning for specific algorithms; Hyperparameter tuning in popular algorithms (e.g., Random Forest, SVM, Neural Networks); Specific strategies for different types of models; Feature Engineering and Optimization; Automation of the Tuning Process; Development of automated hyperparameter tuning pipelines.
Business Intelligence and Executive Reporting
Data interpretation techniques for extracting insights; Analysis of business-relevant trends and patterns; Use of analytical tools to uncover valuable information; Preparation of strategic recommendations; Development of recommendations based on data analysis; Identification of areas for improvement and growth opportunities; Formulation of data-driven strategies to drive business success; Presentation of results for decision-making; Strategies for persuasive data presentation; Use of effective data visualizations; Practical approach to transforming insights into tangible actions. | 8pm |
