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Complete Time Series Analysis With Python

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Complete Time Series Analysis With Python, available at $64.99, has an average rating of 4.47, with 54 lectures, based on 956 reviews, and has 6092 subscribers.

You will learn about Master Time Series Data In Python & Become Proficient In Time Series Data Analysis LEARN How To Use Python-based Packages For Time Series Analysis APPLY Python Data Science Techniques To REAL LIFE Data IMPLEMENT Common Data Processing And Visualisation Techniques For Time Series Data in Python BE ABLE To Read In, Pre-process & Visualize Time Series Data The Basic Conditions Time Series Data Must Fulfill & How To Check For These MODEL Time Series Data To Forecast Future Values USE Machine Learning Regression For Forecasting Future Values This course is ideal for individuals who are Anyone Who Wants Master Time Series Data In Python or Anyone Who Wants To Become Proficient In Time Series Data Analysis Working With Real Life Data or People Interested in Applying Machine Learning Techniques to Time Series Data or Anyone Who Wants To Become An Expert Data Scientist It is particularly useful for Anyone Who Wants Master Time Series Data In Python or Anyone Who Wants To Become Proficient In Time Series Data Analysis Working With Real Life Data or People Interested in Applying Machine Learning Techniques to Time Series Data or Anyone Who Wants To Become An Expert Data Scientist.

Enroll now: Complete Time Series Analysis With Python

Summary

Title: Complete Time Series Analysis With Python

Price: $64.99

Average Rating: 4.47

Number of Lectures: 54

Number of Published Lectures: 53

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 53

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Master Time Series Data In Python & Become Proficient In Time Series Data Analysis
  • LEARN How To Use Python-based Packages For Time Series Analysis
  • APPLY Python Data Science Techniques To REAL LIFE Data
  • IMPLEMENT Common Data Processing And Visualisation Techniques For Time Series Data in Python
  • BE ABLE To Read In, Pre-process & Visualize Time Series Data
  • The Basic Conditions Time Series Data Must Fulfill & How To Check For These
  • MODEL Time Series Data To Forecast Future Values
  • USE Machine Learning Regression For Forecasting Future Values

Who Should Attend

  • Anyone Who Wants Master Time Series Data In Python
  • Anyone Who Wants To Become Proficient In Time Series Data Analysis Working With Real Life Data
  • People Interested in Applying Machine Learning Techniques to Time Series Data
  • Anyone Who Wants To Become An Expert Data Scientist

Target Audiences

  • Anyone Who Wants Master Time Series Data In Python
  • Anyone Who Wants To Become Proficient In Time Series Data Analysis Working With Real Life Data
  • People Interested in Applying Machine Learning Techniques to Time Series Data
  • Anyone Who Wants To Become An Expert Data Scientist

THIS IS YOUR COMPLETE GUIDE TO TIME SERIES DATA ANALYSIS IN PYTHON!

This course is your complete guide to time series analysis using Python. So,all the  main aspects of analyzing temporal data will be covered n depth..

If you take this course, you can do away with taking other courses or buying books on Python based data analysis.

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in in analysing time series data in Python, you can give your company a competitive edge and boost your career to the next level.

                                                     

LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

Hey, my name isMinerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources  using data science related techniques and i have produced many publications for international peer reviewed journals.

 Over the course of my research I realised almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic .

So, unlike other instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics!

You will go all the way from carrying out data reading & cleaning  to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data.

Among other things:

  • You will be introduced to powerful Python-based packages for time series analysis.

  • You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data.

  • & you will learn to apply these frameworks to real life data including temporal stocks and financial data. 

NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED!

You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help youimplement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real-life.

After taking this course, you’ll easily use the common time series packages in Python…

You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.

We will work with real data and you will have access to all the code and data used in the course.

JOIN MY COURSE NOW!

Course Curriculum

Chapter 1: Introduction to the Course

Lecture 1: Welcome to Time Series Analysis with Python

Lecture 2: Data and Scripts Used in the Course

Lecture 3: Introduction to the Python Data Science Environment

Lecture 4: Installation Instructions For Mac

Chapter 2: Read in Data From External Data Sources

Lecture 1: Introduction to Pandas

Lecture 2: Read in CSV Data

Lecture 3: Read in Excel Data

Lecture 4: Read in HTML Data

Lecture 5: Read in JSON Data

Chapter 3: Preprocessing & Visualising Time Series Data in Python

Lecture 1: Some Date Specific Python Functions

Lecture 2: An Example of Time Series Data in Python

Lecture 3: More Details on Datetime

Lecture 4: Basic Operations on Time Series Data

Lecture 5: Theory Behind Exploratory Data Analysis (EDA)

Lecture 6: Principles of Data Visualization

Lecture 7: Prep Up the Time Series Data

Lecture 8: Line Charts For Examining Temporal Data

Lecture 9: Multiple Lines in the Same Chart

Lecture 10: Aggregating & Visualising Data Summary

Lecture 11: Using Multiple Line Plots For Discerning Specific Information

Lecture 12: Histograms

Lecture 13: Plot the Temporal Variations of Two Entities

Chapter 4: Characteristics & Conditions of Time Series Data

Lecture 1: Moving Average (MA) Forecast Example

Lecture 2: Classical Time Series Data

Lecture 3: Different Components of Time Series Data

Lecture 4: Seasonal Part of Time Series

Lecture 5: Of Multiplicative and Additive Seasonality

Lecture 6: Testing for Stationarity: ADF Test

Lecture 7: Make Time Series Stationary: Take Log

Lecture 8: First Order Differencing to Make Time Series Stationary

Lecture 9: Log Based Differencing

Lecture 10: Linear Regression For Detrending

Chapter 5: Basic Time Series Forecasting

Lecture 1: Rolling Mean For Detecting Temporal Variation

Lecture 2: Simple Exponential Smoothing (SES)

Lecture 3: Holt extended simple exponential smoothing

Lecture 4: Holt Winters

Lecture 5: Auto Regression Model (AR): Consider Previous Time Steps

Lecture 6: Implement a Basic ARIMA Model

Lecture 7: Automated ARIMA & Account for Seasonality (SARIMA)

Chapter 6: Machine Learning For Time Series

Lecture 1: Random Forest For Identifying Important Time Periods

Lecture 2: "Prophetic" Time Series Forecasting

Lecture 3: Using Prophet For Predicting Values for a Future Time Frame

Chapter 7: Use Deep Learning For Time Series Data

Lecture 1: What is Keras?

Lecture 2: Install Keras on Windows

Lecture 3: Install Keras on Mac

Lecture 4: Theory Behind ANN and DNN

Lecture 5: MLP For Time Series

Lecture 6: LSTM For Time Series Data

Lecture 7: LSTM For Predicting Stock Prices

Lecture 8: Univariate LSTM For Stock Prediction

Lecture 9: Unseen Values

Chapter 8: Miscellaneous Lectures

Lecture 1: Bitcoin Price Forecasting

Lecture 2: POSIT

Instructors

  • Minerva Singh
    Minerva Singh
    Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

  • 1 stars: 78 votes
  • 2 stars: 83 votes
  • 3 stars: 169 votes
  • 4 stars: 176 votes
  • 5 stars: 450 votes

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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