Curriculum

12 Chapters · 12 Lessons

  1. 1

    Book Preview

    1. Mastering Time Series Analysis and Forecasting with Python Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Introduction to Time Series

    1. (Included in full purchase)

      Introduction to Time Series

  4. 4

    Chapter 2 : Overview of Time Series Libraries in Python

    1. (Included in full purchase)

      Overview of Time Series Libraries in Python

  5. 5

    Chapter 3 : Visualization of Time Series Data

    1. (Included in full purchase)

      Visualization of Time Series Data

  6. 6

    Chapter 4 : Exploratory Analysis of Time Series Data

    1. (Included in full purchase)

      Exploratory Analysis of Time Series Data

  7. 7

    Chapter 5 : Feature Engineering on Time Series

    1. (Included in full purchase)

      Feature Engineering on Time Series

  8. 8

    Chapter 6 : Time Series Forecasting – ML Approach Part 1

    1. (Included in full purchase)

      Time Series Forecasting – ML Approach Part 1

  9. 9

    Chapter 7 : Time Series Forecasting – ML Approach Part 2

    1. (Included in full purchase)

      Time Series Forecasting – ML Approach Part 2

  10. 10

    Chapter 8 : Time Series Forecasting - DL Approach

    1. (Included in full purchase)

      Time Series Forecasting - DL Approach

  11. 11

    Chapter 9 : Multivariate Time Series, Metrics, and Validation

    1. (Included in full purchase)

      Multivariate Time Series, Metrics, and Validation

  12. 12

    Index

    1. (Included in full purchase)

      Index

About the course

"Mastering Time Series Analysis and Forecasting with Python" is an essential handbook tailored for those seeking to harness the power of time series data in their work. The book begins with foundational concepts and seamlessly guides readers through Python libraries such as Pandas, NumPy, and Plotly for effective data manipulation, visualization, and exploration. Offering pragmatic insights, it enables adept visualization, pattern recognition, and anomaly detection. Advanced discussions cover feature engineering and a spectrum of forecasting methodologies, including machine learning and deep learning techniques such as ARIMA, LSTM, and CNN. Additionally, the book covers multivariate and multiple time series forecasting, providing readers with a comprehensive understanding of advanced modeling techniques and their applications across diverse domains. Readers develop expertise in crafting precise predictive models and addressing real-world complexities. Complete with illustrative examples, code snippets, and hands-on exercises, this manual empowers readers to excel, make informed decisions, and derive optimal value from time series data.

About the Author

Sulekha Aloorravi is a professional with a diverse background and several key roles. She is currently the Vice President of the Banking industry, where she also specializes as a Data Scientist. In addition to her corporate role, Sulekha is also a mentor with Great Learning. Her contributions to the academic field have been recognized and cited. Her expertise extends into the realm of engineering and data science, with a noted deep understanding of various technologies and systems. This technical proficiency is further exemplified through her work as an author. Sulekha has written ""Metaprogramming with Python,"" a guide for programmers on writing reusable code to build smarter applications. This combination of roles in both the corporate and academic sectors, along with her contributions to the field of programming through her publication, highlights Sulekha’s multifaceted expertise and significant presence in the fields of data science, business management, and technology.