Data Science : Begineer to Advance

Chapters

1Data Science Foundations and Workflow

2Python Programming Essentials for Data Science

3Working with Data Sources and SQL

4Data Wrangling with NumPy and Pandas

5Data Cleaning and Preprocessing

6Exploratory Data Analysis and Visualization

7Probability and Statistics for Data Science

8Machine Learning Foundations

9Supervised Learning Algorithms

10Unsupervised Learning and Dimensionality Reduction

11Model Evaluation, Validation, and Tuning

12Feature Engineering and ML Pipelines

13Time Series Analysis and Forecasting

Time Series Components Stationarity and Differencing Autocorrelation and Partial Autocorrelation AR MA and ARIMA SARIMA and Seasonal Decomposition Exponential Smoothing and ETS Prophet and Additive Models Feature Engineering for Time Series Cross Validation for Time Series Forecast Accuracy Metrics Multivariate and VAR Anomaly Detection in Time Series Hierarchical Forecasting Deep Learning for Time Series Productionizing Forecasts

14Natural Language Processing

15Deep Learning, Deployment, and MLOps

Courses/Data Science : Begineer to Advance/Time Series Analysis and Forecasting

Time Series Analysis and Forecasting

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Model temporal data with classical and modern methods. Evaluate forecasts and deploy scalable time series solutions.

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Stationarity and Differencing

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