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Mastering Time Series Analysis and Forecasting with Python

person icon AKHIL VYDYULA

4.2

Mastering Time Series Analysis and Forecasting with Python

Mastering Time Series Analysis and Forecasting with Python

updated on icon Updated on Feb, 2025

language icon Language - English

person icon AKHIL VYDYULA

category icon Development ,Programming Languages,Python

Lectures -15

Duration -2.5 hours

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4.2

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Course Description

The course you will undergo is extensive to give you the tools necessary for analyzing, modeling, and forecasting time-series data. This course empowers data scientists, analysts, and business people to make informed choices from time-dependent data.

Dive into time series analysis while we explore basic ideas, techniques for practice and complex modeling techniques. The various time series patterns that include trends, seasonality, and cyclicality will be handled by you as well as what to do when such stationary data is converted for analysis.

Find out how to visualize time series graphs uncovering buried patterns there in. Thus Python libraries such as Pandas, NumPy or Matplotlib should be your go-to when manipulating or exploring data effectively. Hands-on project work using real data sets will help consolidate your knowledge on different kinds of data relations and finally set up a strong base on time series modeling.

Unleash the magic of predicting with time series. Learn about different types of forecasting models including ARIMA and SARIMA plus exponential smoothing; understand how to choose the right model based on your dataset; enhance precision by way of improving forecasts.

By the end of this course, you'll be proficient in handling time series data, building robust models, and making accurate predictions. You'll gain the confidence to tackle real-world challenges and contribute significantly to data-driven decision-making.

Goals

* Hands-on exercises and real-world projects

* Practical applications across various industries

* Expert instruction and support

* Lifetime access to course materials


Don't miss this opportunity to master time series analysis and forecasting with Python. Enroll now and embark on a journey to unlock the potential of your time-series data!

Prerequisites

No prerequisites required.

Mastering Time Series Analysis and Forecasting with Python

Curriculum

Check out the detailed breakdown of what’s inside the course

Foundations of Time Series Analysis

3 Lectures
  • play icon Introduction to Time Series Data 10:01 10:01
  • play icon Understanding Time Series Components 12:38 12:38
  • play icon Stationarity and Its Importance 08:06 08:06

Time Series Modeling with ARIMA

3 Lectures
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Statistical Concepts for Time Series

3 Lectures
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Forecasting with Time Series Models

3 Lectures
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Advanced Time Series Topics and Applications

3 Lectures
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Instructor Details

AKHIL VYDYULA

AKHIL VYDYULA

Data Scientist | Data & Analytics Specialist | Entrepreneur

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!

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