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Time Series Analysis:Hands-On Projects & Advanced Techniques

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Time Series Analysis:Hands-On Projects & Advanced Techniques, available at $54.99, has an average rating of 5, with 95 lectures, based on 7 reviews, and has 1020 subscribers.

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You will learn about Import and clean time series data. Calculate common time series statistics. Create time series visualizations. Build time series models. Forecast time series data. Accessing, Manipulating, Visualizing Data. Build Projects. This course is ideal for individuals who are Beginners and intermediate Python programmers. or Students want to apply Python knowledge through Python Projects. or Students want to build the skills that is needed to become a Python Scripting Guru. or Data analysts & Data scientists. or Business analysts. or AI Engineers. or Financial Analysts. or Anyone who wants to learn how to analyze time series data. It is particularly useful for Beginners and intermediate Python programmers. or Students want to apply Python knowledge through Python Projects. or Students want to build the skills that is needed to become a Python Scripting Guru. or Data analysts & Data scientists. or Business analysts. or AI Engineers. or Financial Analysts. or Anyone who wants to learn how to analyze time series data.

Enroll now: Time Series Analysis:Hands-On Projects & Advanced Techniques

Summary

Title: Time Series Analysis:Hands-On Projects & Advanced Techniques

Price: $54.99

Average Rating: 5

Number of Lectures: 95

Number of Published Lectures: 95

Number of Curriculum Items: 95

Number of Published Curriculum Objects: 95

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

  • Import and clean time series data.
  • Calculate common time series statistics.
  • Create time series visualizations.
  • Build time series models.
  • Forecast time series data.
  • Accessing, Manipulating, Visualizing Data.
  • Build Projects.

Who Should Attend

  • Beginners and intermediate Python programmers.
  • Students want to apply Python knowledge through Python Projects.
  • Students want to build the skills that is needed to become a Python Scripting Guru.
  • Data analysts & Data scientists.
  • Business analysts.
  • AI Engineers.
  • Financial Analysts.
  • Anyone who wants to learn how to analyze time series data.

Target Audiences

  • Beginners and intermediate Python programmers.
  • Students want to apply Python knowledge through Python Projects.
  • Students want to build the skills that is needed to become a Python Scripting Guru.
  • Data analysts & Data scientists.
  • Business analysts.
  • AI Engineers.
  • Financial Analysts.
  • Anyone who wants to learn how to analyze time series data.

Time series analysis focuses on data collected over time, like stock prices, weather patterns, or sensor readings. It reveals hidden trends, patterns, and relationships within this data. By understanding these patterns, we can predict future values, make informed decisions, and gain insights into complex phenomena. Time series analysis is a powerful tool for various fields, including finance, economics, healthcare, and environmental science.

This course will teach you how to use Python to analyze time series data. You will learn how to:

  • Import and clean time series data.

  • Calculate common time series statistics.

  • Create time series visualizations.

  • Build time series models.

  • Forecast time series data.

  • Accessing, Manipulating, Visualizing Data.

  • Master Advanced Techniques.

  • Build Projects.

Whether you’re new to Python or have some programming experience, this course welcomes you to the world of time series analysis. No prior knowledge is required, as we’ll start from the basics and gradually introduce advanced techniques using Python.

Who this course is for:

  • Beginners and intermediate Python programmers.

  • Data analysts.

  • Data scientists.

  • Business analysts.

  • Anyone who wants to learn how to analyze time series data.

  • AI Engineers.

  • Financial Analysts.

Requirements:

  • No prior knowledge is required. So whether you’re new to Python or have some programming experience.

  • A computer with Python installed.

  • Welling to learn advanced Techniques.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Jupyter Shortcuts

Lecture 3: Understanding Data Types and Structures in Python.

Lecture 4: Understanding Python Data Structure Wrap up.

Chapter 2: Python Refresher

Lecture 1: String Functions in Python Part 1

Lecture 2: String Functions in Python Part 2

Lecture 3: String Functions in Python Part 3

Lecture 4: String Functions in Python Part 4

Lecture 5: String Functions in Python Part 5

Lecture 6: Lists.

Lecture 7: Tuples.

Lecture 8: Sets.

Lecture 9: Dictionaries.

Lecture 10: Control Flow IF.

Lecture 11: For Loop Part 1.

Lecture 12: For Loop Part 2.

Lecture 13: While Loop Part 1.

Lecture 14: While Loop Part 2.

Lecture 15: While Loop Best Practices.

Lecture 16: Introduction to Functions in Python.

Lecture 17: Functions in Python and Arguments.

Lecture 18: Function Tips & Tricks: Recursion.

Lecture 19: Function Tips & Tricks: Functions Decorators and Higher Order Functions.

Lecture 20: Functions Tips & Tricks: Lambda Functions.

Lecture 21: Function Tips & Tricks: Functions Caching & Memoization.

Lecture 22: Error Handling in Python.

Lecture 23: Files and Modules in Python.

Chapter 3: Object Oriented Programming (OOP) In Python Refresher.

Lecture 1: Creating Simple Class.

Lecture 2: Overviewing Constructor.

Lecture 3: Learning How to creating Dunder Methods?

Lecture 4: Learning about Inheritance.

Lecture 5: Knowing What is the Encapsulation?

Lecture 6: Learning also about Multiple Inheritance.

Lecture 7: Knowing What is the Overriding?

Lecture 8: Learning about Decorators.

Lecture 9: Learning How to use Build-in Decorators?

Chapter 4: Project 1: Python Pandas + PostgreSQL

Lecture 1: PostgreSQL Downloading & Installing.

Lecture 2: Create Database.

Lecture 3: Restore Database.

Lecture 4: Using CMD & Python pip.PyPi to Install Jupyter Lab & Pandas.

Lecture 5: Create a CSV File Using PostgreSQL

Lecture 6: Fetchmany and Fetchall

Lecture 7: Runnig SQL Query Using Python Panadas Module.

Lecture 8: Using Python Pandas Package to load PostgreSQL the Data Output file.

Lecture 9: Data Analysis Process Overview.

Lecture 10: Pandas Methods.

Lecture 11: Pandas data visualization.

Lecture 12: Pandas Data Analysis.

Lecture 13: Sampling Error.

Chapter 5: Project 2: Scrape the Web & Saving Data to a Database.

Lecture 1: How to Scrape a website???

Lecture 2: Scrape a Table inside a Webpage using Pandas and LXML Python Modules!

Lecture 3: Visualization of the Scarped Data.

Lecture 4: Save The Scraped Data to a Database.

Chapter 6: Project 3: Python Automation AFC (OS Python Module).

Lecture 1: Download & Install of Sublime Text Editor.

Lecture 2: Project Walkthrough.

Lecture 3: Project Arrange Folder Content.

Chapter 7: Project 4: Python Automation Project MPF (PyPDF2 Python Module).

Lecture 1: Project Walkthrough.

Lecture 2: Project Solution.

Chapter 8: Project 5: Python Automation Business Email List (smtplib Python Module).

Lecture 1: Part 1

Lecture 2: Part 2

Lecture 3: Part 3

Chapter 9: Python Numpy Library.

Lecture 1: Numpy Intro.

Lecture 2: Numpy.shape & Numpy.size

Lecture 3: Creating Numpy nd arrays using Numpy functions.

Lecture 4: Numpy.unique( ) & Array slicing.

Lecture 5: Numpy Calculations and Operators.

Lecture 6: Numpy Aggregations.

Lecture 7: Numpy Reshape and Transposing.

Lecture 8: Comparing Numpy Arrays.

Lecture 9: Numpy Arrays Images Processing.

Chapter 10: Accessing, Manipulating & Filtering DataFrames.

Lecture 1: Data manipulation using DataFrames.

Lecture 2: Accessing Data Using DataFrames.

Lecture 3: Data aggregation and summarization.

Lecture 4: Create New Columns, Drop Unnecessary Ones, and Perform Various Data Manipulation

Lecture 5: Essential Techniques for Peeking at & Describing our Data in Python.

Lecture 6: Filtering Data.

Chapter 11: Data Visualization in Python.

Lecture 1: Introduction to Data Visualization in Python.

Lecture 2: Histograms – a Powerful Tool for Visualizing the Distribution of Data.

Lecture 3: Visualizing Trends using a Real-World Financial Data.

Lecture 4: Determining and Choosing the Appropriate Plot Type.

Chapter 12: Time Series Data Analysis using Python.

Lecture 1: Datasets used in this Section.

Lecture 2: Introduction to Time Series Analysis.

Lecture 3: Creating, Converting Datetimes from Strings & Manipulating Datetime Data.

Lecture 4: Accessing Datetime Attributes, Comparing Datetimes, & Making Relative Datetime.

Lecture 5: Understanding Time Series Growth Rates & Comparing Stock Prices with a Benchmark

Lecture 6: Changing Time Series Frequency By Up-Sampling & Interpolation.

Lecture 7: Changing Time Series Frequency By Down-Sampling.

Lecture 8: Window Functions in Time Series Analysis.

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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