Time Series Analysis & Forecasting with Python in 2025

2025 Mastering Time Series Analysis and Forecasting with Python

Comprehensive guide to time series analysis and forecasting techniques with Python, covering ARIMA, SARIMA, Prophet

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Last Updated: 2025-01-31 05:01:22 English Course Type: Self Learning

Understand the fundamentals of time series analysis, including trends, seasonality, and noise.
Implement various time series forecasting methods such as ARIMA, SARIMA, and Prophet using Python.
Evaluate and tune time series models to improve accuracy and performance.
Apply time series analysis techniques to real-world datasets and interpret the results for actionable insights.
Students and researchers interested in applying time series techniques to their projects.
Data analysts and scientists looking to enhance their time series analysis skills.
Professionals working in fields like finance, economics, and operations who deal with time-series data.
Anyone curious about understanding and predicting patterns in time-dependent data.

Unlock the power of time series analysis and forecasting with Python! This course is designed to provide a thorough understanding of the key concepts, techniques, and tools needed to analyze and predict time series data effectively. Whether you're a data scientist, analyst, student, or professional, this course will equip you with the skills to tackle time series problems in various domains.

What You'll Learn:

Understand the fundamentals of time series analysis, including trends, seasonality, and noise.

Implement and apply popular time series forecasting methods such as ARIMA, SARIMA, and Prophet using Python.

Evaluate and tune time series models to improve their accuracy and performance.

Work with real-world datasets to gain hands-on experience and extract actionable insights.

Course Highlights:

Detailed Explanations: Comprehensive coverage of essential concepts and techniques in time series analysis.

Hands-On Projects: Practical exercises and projects to apply what you've learned.

Expert Guidance: Learn from an experienced data scientist with a proven track record in the field.

Community Support: Join a community of learners to discuss and share insights.

Requirements:

Basic knowledge of Python programming.

Familiarity with libraries such as pandas and matplotlib is beneficial.

A computer with internet access to follow along with coding exercises and access datasets.

Basic understanding of statistical concepts such as mean, variance, and correlation.

Willingness to learn and apply analytical thinking to solve time series problems.

Who Should Enroll:

Aspiring data scientists and analysts looking to specialize in time series analysis and forecasting.

Professionals in finance, marketing, operations, and other fields where time series data is commonly used for decision-making.

Students and researchers in academia who need to analyze time series data for their studies or projects.

Anyone interested in gaining practical skills in time series analysis to enhance their data science toolkit.

Join us on this exciting journey and master the art of time series analysis and forecasting with Python. Enroll today and start transforming data into meaningful insights!

Basic knowledge of Python programming. Familiarity with libraries such as pandas and matplotlib is beneficial.
Basic knowledge of Python programming. Familiarity with libraries such as pandas and matplotlib is beneficial.
Basic understanding of statistical concepts such as mean, variance, and correlation.
Willingness to learn and apply analytical thinking to solve time series problems.
A curious mind and willingness to learn!
Familiarity with statistical concepts (mean, median, standard deviation).
Basic understanding of Python programming.

Aspiring data scientists and analysts looking to specialize in time series analysis and forecasting.
Professionals in finance, marketing, operations, and other fields where time series data is commonly used for decision-making.
Students and researchers in academia who need to analyze time series data for their studies or projects.
Anyone interested in gaining practical skills in time series analysis to enhance their data science toolkit.

Section 1 : Foundations of Time Series Analysis 3 Lectures 00:30:45
Lecture 1 :
Introduction to Time Series Data Preview
Lecture 2 :
Understanding Time Series Components
Lecture 3 :
Stationarity and Its Importance
Section 2 : Time Series Modeling with ARIMA 3 Lectures 00:34:05
Lecture 1 :
ARIMA Model Fundamentals
Lecture 2 :
Building and Evaluating ARIMA Models
Lecture 3 :
Seasonal Time Series and Decomposition
Section 3 : Statistical Concepts for Time Series 3 Lectures 00:42:58
Lecture 1 :
Probability Distributions in Time Series
Lecture 2 :
Descriptive Statistics and Exploratory Data Analysis
Lecture 3 :
Hypothesis Testing and Confidence Intervals
Section 4 : Forecasting with Time Series Models 3 Lectures 00:32:14
Lecture 1 :
Forecasting with ARIMA Models
Lecture 2 :
Model Selection and Evaluation
Lecture 3 :
Practical Forecasting and Model Improvement
Section 5 : Advanced Time Series Topics and Applications 4 Lectures 00:25:31
Lecture 1 :
Data Visualization for Time Series
Lecture 2 :
Time Series in Python: Practical Implementation
Lecture 3 :
Real-world Case Studies and Applications
Lecture 4 :
Resources

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AKHIL VYDYULA,

2099 Course Views

19 Courses

Hello, I'm Akhil, a Senior Data Scientist at PwC specializing in the Advisory Consulting practice with a focus on Data and Analytics.

My career journey has provided me with the opportunity to delve into various aspects of data analysis and modelling, particularly within the BFSI sector, where I've managed the full lifecycle of development and execution.

I possess a diverse skill set that includes data wrangling, feature engineering, algorithm development, and model implementation. My expertise lies in leveraging advanced data mining techniques, such as statistical analysis, hypothesis testing, regression analysis, and both unsupervised and supervised machine learning, to uncover valuable insights and drive data-informed decisions. I'm especially passionate about risk identification through decision models, and I've honed my skills in machine learning algorithms, data/text mining, and data visualization to tackle these challenges effectively.

Currently, I am deeply involved in an exciting Amazon cloud project, focusing on the end-to-end development of ETL processes. I write ETL code using PySpark/Spark SQL to extract data from S3 buckets, perform necessary transformations, and execute scripts via EMR services. The processed data is then loaded into Postgres SQL (RDS/Redshift) in full, incremental, and live modes. To streamline operations, I’ve automated this process by setting up jobs in Step Functions, which trigger EMR instances in a specified sequence and provide execution status notifications. These Step Functions are scheduled through EventBridge rules.

Moreover, I've extensively utilized AWS Glue to replicate source data from on-premises systems to raw-layer S3 buckets using AWS DMS services. One of my key strengths is understanding the intricacies of data and applying precise transformations to convert data from multiple tables into key-value pairs. I’ve also optimized stored procedures in Postgres SQL to efficiently perform second-level transformations, joining multiple tables and loading the data into final tables.

I am passionate about harnessing the power of data to generate actionable insights and improve business outcomes. If you share this passion or are interested in collaborating on data-driven projects, I would love to connect. Let’s explore the endless possibilities that data analytics can offer!