Time Series Analysis in Python. Master Applied Data Analysis
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The Ultimate Course on Time Series Analysis in Python brings you expertise in Forecasting Models, Regression, ARIMA, SARIMA and Time Series Data Analysis with Python
Do you want to know how meteorologists forecast weather?
Do you want to know how retailers reduce excess inventory and increase profit margin?
Predict the future using Time Series Forecasting!
Time series forecasting is all about looking into the future.
Time Series is an important field in statistical programming. It allows you to analyze:-
1. Trends
2. Seasonality
3. Irregularity
Time Series Analysis has tons of applications such as stock market analysis, pattern recognition, earthquake prediction, census analysis and many more.
Due to the advanced modern technologies, the data is growing exponentially and this data can be used to modelled for the future which can really make a big difference.
You are at the right place!
Welcome to this online resource to learn Time Series Analysis using Python.
This course will really help you to boost your career.
This course begins with the basic level and goes up to the most advanced techniques step by step. Even if you do not know anything about time series, this course will make complete sense to you.
In this course you will learn about the following:-
1. What is time series data, its applications and components.
2. Fetching time series data using different methods.
3. Handling missing values and outliers in time series data.
4. Decomposing and splitting time series data.
5. Different smoothing techniques such as simple moving averages, simple exponential, holt, and holt-winter exponential.
6. Checking stationarity of the time series data and converting non-stationary to stationary.
7. Auto-regressive models such as simple AR model and moving average model.
8. Advanced auto-regressive models such as ARMA, ARIMA, SARIMA.
9. ARIMAX and SARIMAX model.
10. Evaluation metrics used for time series data.
11. Rules for choosing the right model for time series data.
All the mentioned topics will be covered theoretically as well as implemented in code.
You will compare all the models and will see how to read the results.
We will work with real data and you will have access to all the resources used in this course.
This course is for everyone who wants to master time series and become proficient in working with real-life time-based data.
For taking up this course you need to have prior knowledge of Python programming.
But wait!
Here is the surprise!!
If you are not aware of the python programming language then also don’t worry.
We have a crash course in python for you. You can take up python’s crash course and then proceed with the time series analysis.
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Introduction to Auto Regressive Models -
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Checking for Stationarity Part 1
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Checking for Stationarity using Statistical Methods Part 2
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Checking for Stationary Implementation
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Converting Non-Stationary Series into Stationary
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Converting Non-Stationary Series into Stationary Implementation
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Auto Correlation and Partial Correlation
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Auto Correlation and Partial Correlation Implementation
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The Simple Auto Regressive Model
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The Simple Auto Regressive Model Implementation
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Moving Average Model
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Moving Average Model Implementation
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Quiz on AR Models -
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Quiz Solution on AR Models
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Why should you learn Python?
Hello and Welcome to the Complete Guide of Time Series Analysis Course.
In this section, we are going to learn about All Python Fundamentals Step by Step and also learn how we can implement Python in Real-World Scenario.
With an exponentially growing community around data science, machine learning, AI. Python is a language that opens programming access to the world. Python is considered easy to read, write and learn. Plus, it's extremely scalable. It is widely used in small, large, online, or offline projects.
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Installing Python and Jupyter Notebook
This Lecture covers the installation of python and Jupyter Notebook in your own environment.
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Naming Convention for variables
In this video lecture, we will understand what are variables in Python and how we will use them throughout this Python Course.
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Built in Data Types and Type Casting
Hello and Welcome to another interesting lecture on Time Series.
In this lecture, we will learn python built-in Data types and Python Type Casting.
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Scope of Variables
Welcome, Student to another interesting lecture of Time Series Analysis Using Python.
In this lecture, we will learn what is Python's scope of variable with some examples?
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Quiz on Variables and Data Types
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Quiz Solution
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Arithmetic and Assignment Operators
In this video, you will learn about Python Arithmetic operators and Python Assignment Operators.
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Comparison, Logical, and Bitwise Operators
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Identity and Membership Operators
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Quiz on Operators
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Quiz Solution
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String Formatting
In this lecture, we are going to learn about Python Strings and String formatting, and also we will implement examples.
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String Methods
In the previous lecture, we learned about the different string formatting. In this lecture, we are going to learn about the different String methods.
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User Input
In this lecture, we are going to learn about how to take input from the users.
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Quiz on Strings
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Quiz Solution
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If, elif, and else
In the previous videos, we learned about various strings methods as well as how we can take input from users.
In this section, we are going to learn about a very important topic i.e Python's Loops and Conditionals Statement.
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For and While
In the previous lecture, we learned about if-elif-else statements.
In this lecture, we are going to learn about the For and While loops, and for better understanding, we implement few examples.
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Break and Continue
Previously, we learned about some conditional statements and as well as loops.
In this lecture, we are going to learn about the break and continue statements to alter the flow of a loop.
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Quiz on Loops and Conditionals
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Quiz Solution
