Time Series Analysis in Python. Master Applied Data Analysis
Python Time Series Analysis with 10+ Forecasting Models including ARIMA, SARIMA, Regression & Time Series Data Analysis
What you’ll learn
-
What is Time Series Data, it applications and components.
-
Fetching time series data using different methods.
-
Handling missing values and outliers in a time series data.
-
Decomposing and Splitting time series data.
-
Different smoothing techniques such as Simple Moving Averages, Simple Exponential, Holt and Holt-winter Exponential.
-
Checking Stationarity of the time 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 time series data.
-
Rules for Choosing the Right Model for time series data.
Requirements
-
Must have a Desire of Learning Advanced Technologies
-
Must have a Laptop to Practice the Course Curriculum
-
Must have an Internet Connectivity
-
Basic Knowledge of English Language
Who this course is for:
- Programming Beginners
- Data Science Enthusiast
- Python Developers
- Programmers who wants to specialize in finance
- Machine Learning Enthusiast
- Beginner Python Developers curious to Learn about Time Series
- Statisticians who want to Upgrade themselves
- Data Analysts who want to Upgrade themselves
