Intermediate Data Analytics Training Using Python

 

Training Overview

This intermediate-level training is designed for individuals who have basic knowledge of Python and data analysis. It focuses on advanced techniques and tools to handle, analyze, and visualize complex datasets. The course will cover deeper aspects of data wrangling, statistical analysis, and machine learning, empowering you to conduct more sophisticated analyses.

Recap of Python Fundamentals

• Quick review of Python data structures (Lists, Dictionaries, Tuples)
• Functions, Lambdas, and List Comprehensions
• Working with Libraries (NumPy, Pandas, Matplotlib, Seaborn)
• Error handling and debugging

Advanced Data Manipulation with Pandas

• Advanced DataFrame operations (merge, join, concatenate)
• MultiIndex and hierarchical indexing
• Pivot tables and cross-tabulations
• GroupBy operations and aggregation techniques
• Working with Time Series Data (date parsing, resampling, rolling windows)
• Advanced data cleaning techniques (handling duplicates, outliers, and missing values)

Exploratory Data Analysis (EDA)

• Statistical summaries and measures of central tendency
• Correlation and covariance
• Visual exploration of data distributions and relationships (Pair plots, Violin plots, KDE)
• Anomaly detection and outlier analysis
• Handling categorical data (One-hot encoding, Label encoding)
• Data Visualization (Advanced)
• Advanced plotting with Matplotlib and Seaborn
• Heatmaps, Correlation Plots
• Advanced Customization (subplots, styling, colors)
• Interactive visualization with Plotly and Bokeh
• Creating dynamic plots and dashboards
• Storytelling with data: Presenting your findings effectively through visualization

Statistical Analysis and Hypothesis Testing

• Introduction to probability distributions (Normal, Binomial, Poisson)
• Statistical testing (T-tests, Chi-squared, ANOVA)
• P-values, confidence intervals, and error rates
• Regression analysis: Linear regression, Multiple regression
• Building and interpreting statistical models

Advanced Machine Learning Techniques

• Introduction to supervised and unsupervised learning
• Regression Algorithms (Linear Regression, Polynomial Regression)
• Classification Algorithms (Logistic Regression, k-NN, Decision Trees)
• Model evaluation: Confusion matrix, ROC curve, F1 score
• Hyperparameter tuning and Cross-validation using GridSearchCV

Clustering and Dimensionality Reduction

• Clustering techniques (K-means, DBSCAN, Agglomerative clustering)
• Dimensionality reduction techniques (PCA - Principal Component Analysis, t-SNE)
• Visualizing high-dimensional data

Introduction to Text Data Analytics (Natural Language Processing - NLP)

• Text preprocessing (Tokenization, Lemmatization, Stop words removal)
• Word embeddings (TF-IDF, Word2Vec, GloVe)
• Text classification using machine learning models
• Sentiment analysis with Python

Time Series Analysis and Forecasting

• Time Series decomposition (Trend, Seasonality, Residuals)
• Forecasting models (ARIMA, SARIMA)
• Exponential Smoothing (Holt-Winters Method)
• Evaluating forecast accuracy (RMSE, MAE)

Study Project

• Real-world project where you will apply advanced techniques on a dataset of your choice
• Data cleaning, EDA, visualization, and advanced machine learning modeling
• Model evaluation, interpretation of results, and presenting insights

Expected Learning Outcomes

• Master advanced techniques in data manipulation, analysis, and visualization using Python.
• Gain proficiency in statistical analysis, hypothesis testing, and regression.
• Learn to build machine learning models for classification, regression, and clustering tasks.
• Explore time series forecasting and anomaly detection.
• Develop an understanding of text data analysis and natural language processing.
• Handle complex datasets and perform in-depth data exploration, analysis, and reporting.