Time Series Analysis in Python. Master Applied Data Analysis

What you'll learn

Fetching series data using different methods.

Handling missing values and outliers in a series data.

Decomposing and Splitting series data.

Different smoothing techniques such as Simple Moving Averages, Simple Exponential, Holt and Holt-winter Exponential.

Checking Stationarity of the series data and Converting Non-stationary to Stationary.

Auto-regressive models such as Simple AR model and Moving Average Model.

Advanced Auto-Regressive Models such as ARMA, ARIMA, SARIMA.

Evaluation Metrics used for series data.

Rules for Choosing the Right Model for series data.

Requirements

Basic and Intermediate concepts of python.

Knowledge of Pandas, matplotlib or seaborn library.

Description

The Ultimate course on Series Analysis in Python which brings you expertise in Forecasting Models, Regression, ARIMA, SARIMA and 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 ma

Predict the future using Series Forecasting!

series forecasting is all about looking into the future.

series is an important field in statistical programming. It allows you to analyze:-

1. Trends

2. Seasonality

3. Irregularity

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 Series Analysis using Python.

This course will really help you to boost your career.

This course bs with the basic level and goes up to the most advanced techniques step by step. Even if you do not know anything about series, this course will make complete sense to you.

In this course you will learn about the following:-

1. What is series data, it applications and components.

2. Fetching series data using different methods.

3. Handling missing values and outliers in a series data.

4. Decomposing and splitting series data.

5. Different smoothing techniques such as simple moving averages, simple exponential, holt and holt-winter exponential.

6. Checking stationarity of the 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 series data.

11. Rules for choosing the right model for 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 series and become proficient in working with real life 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 python programming language then also don't worry.

We have a crash course of python for you. You can take up python's crash course and then proceed with the series analysis.

Who this course is for:

Programming Bners

Data Science Enthusiast

Python Developers

Programmers who wants to specialize in finance



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