Advanced Data Analytics Training Using Python

 

Training Objective

The Advanced Data Analytics Training Using Python is designed for individuals with a strong foundation in Python programming, data manipulation, and basic machine learning. This course covers more sophisticated techniques and tools for high-level data analysis, including advanced machine learning algorithms, deep learning, big data analytics, and advanced statistical modeling. The focus is on developing advanced analytical skills that can be applied to real-world, large-scale datasets.

Recap of Python for Advanced Analytics

• Advanced Python programming (Generators, Decorators, Context Managers)
• Efficient Data Handling with Pandas (Memory optimization, Chunking large data)
• Advanced NumPy for large-scale data manipulation (Broadcasting, Vectorized operations)
• Working with JSON, XML, and APIs in Python

Advanced Data Wrangling and Preprocessing

• Working with Complex Data Types (Nested JSON, XML, and CSV)
• Advanced Feature Engineering techniques (Polynomial features, Binning, Scaling, Encoding)
• Handling Imbalanced Data (Resampling techniques, SMOTE)
• Dealing with Outliers and Anomalies (Isolation Forest, Z-Score, IQR-based methods)
• Advanced Missing Data Imputation methods (KNN, MICE)

Advanced Exploratory Data Analysis (EDA)

• Univariate and Multivariate Analysis using advanced visualization tools (Seaborn, Plotly)
• Feature Selection and Dimensionality Reduction (Variance Threshold, Recursive Feature Elimination)
• Identifying patterns using Principal Component Analysis (PCA), t-SNE, and LDA (Linear Discriminant Analysis)
• Correlation Analysis with Multicollinearity detection (VIF, Correlation Matrices)
• Advanced time-series analysis with advanced decomposition and forecasting methods (SARIMA, Holt-Winters)

Machine Learning – Advanced Techniques

• Random Forests, Gradient Boosting Machines (GBM), XGBoost, LightGBM, CatBoost
• Stacking, Bagging, Boosting, and Voting classifiers
• Support Vector Machines (SVM): Theory, Hyperparameter tuning, and Kernels (Linear, Polynomial, Radial Basis Function)
• Cross-validation techniques (K-fold, Stratified K-fold)
• Model calibration (ROC-AUC, Precision-Recall Curve, Calibration curves)
• Hyperparameter Tuning with Grid Search, Randomized Search, and Bayesian Optimization
• Advanced Classification Models: Decision Trees, k-Nearest Neighbors (k-NN), Naive Bayes, Logistic Regression
• Advanced Regression Models: Ridge, Lasso, ElasticNet
• Support Vector Regression (SVR)
• Time Series Regression (ARIMA, SARIMA, VAR models)
• Deep Learning Foundations (optional): Introduction to neural networks (feedforward, backpropagation)
• Activation functions (ReLU, Sigmoid, Tanh)
• Overview of frameworks like TensorFlow/Keras for deep learning models

Natural Language Processing (NLP)

• Advanced text preprocessing (Word embeddings, TF-IDF, Lemmatization, Stemming)
• Text Vectorization (Bag of Words, Word2Vec, GloVe)
• Text Classification models (Naive Bayes, SVM, Deep Learning models)
• Topic Modeling (Latent Dirichlet Allocation - LDA, Latent Semantic Analysis - LSA)
• Sentiment Analysis with advanced techniques (Deep Learning, BERT)
• Named Entity Recognition (NER) and Text Summarization

Big Data Analytics with Python

• Working with Big Data frameworks (Apache Spark, Dask, Hadoop)
• PySpark for distributed computing and parallel processing
• Handling large datasets and big data workflows in Python
• Data lakes, cloud-based analytics, and working with Hadoop ecosystem
• Working with NoSQL databases (MongoDB, Cassandra) and integrating them with Python

Deep Learning for Advanced Analytics (Optional)

• Building Neural Networks from scratch using Keras/TensorFlow
• Convolutional Neural Networks (CNN) for image data and classification
• Recurrent Neural Networks (RNN) for sequence data (e.g., time-series, text)
• Generative Adversarial Networks (GANs) and their applications
• Transfer Learning (using pre-trained models for new tasks)

Model Deployment and Operationalization

• Model deployment on cloud platforms (AWS, GCP, Azure)
• Deploying models using Flask/Django for web applications
• Building and deploying APIs for predictive services
• Introduction to Docker and Kubernetes for containerization
• Model monitoring and updating strategies post-deployment

Advanced Time Series Forecasting

• Handling seasonal and trend components
• ARIMA and SARIMA for univariate forecasting
• Multivariate Time Series Forecasting (Vector Autoregression - VAR)
• Advanced deep learning for time series: LSTM (Long Short-Term Memory)
• Forecasting with Prophet for business forecasting

Study Project

• End-to-End project applying advanced data analytics techniques to solve a real-world business problem
• Data collection, preprocessing, exploratory analysis, modeling, and evaluation
• Model deployment and reporting, using interactive dashboards (e.g., Streamlit, Dash)
• Presentation of insights with visualizations and clear business recommendations

Learning Outcomes:

• Master advanced data preprocessing, feature engineering, and manipulation techniques.
• Develop expertise in implementing advanced machine learning algorithms and fine-tuning models.
• Gain the ability to apply advanced statistical analysis and predictive modeling to complex datasets.
• Build, train, and deploy machine learning and deep learning models to solve real-world problems.
• Gain hands-on experience with big data tools, cloud services, and model operationalization.
• Learn how to analyze and visualize text data, time-series data, and unstructured