Regression & Forecasting for Data Scientists using Python
- Offered byCoursera
Regression & Forecasting for Data Scientists using Python at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Regression & Forecasting for Data Scientists using Python at Coursera Highlights
- Earn a certificate of completion
- Add to your LinkedIn profile
- 21 quizzes
Regression & Forecasting for Data Scientists using Python at Coursera Course details
- What you'll learn
- Develop expertise in time series analysis, forecasting, and linear regression
- Analyze techniques for exploratory data analysis, trend identification
- Understand various time-series models and implement them using Python
- Prepare and preprocess data for accurate linear regression modeling
- Build and interpret linear regression models for informed decision-making
- This course provides comprehensive training in regression analysis and forecasting techniques for data science, emphasizing Python programming. You will master time-series analysis, forecasting, linear regression, and data preprocessing, enabling you to make data-driven decisions across industries
Regression & Forecasting for Data Scientists using Python at Coursera Curriculum
Time-Series Analysis and Forecasting
Introduction to Regression & Forecasting for Data Scientists using Python
Introduction to Time-Series Basics
Time-Series Forecasting Use Cases and steps
Forecasting Model Creation
Time-Series Basic Notations
Installing Anaconda and Jupyter Notebook
Data Loading in Python Part 1
Data Loading in Python Part 2
Data Loading in Python Part 3
Feature Engineering in Python Part 1
Feature Engineering in Python Part 2
Visualization in Python Part 1
Visualization in Python Part 2
Visualization in Python Part 3
Time-Series Power Transformation
Moving Average
Exponential Smoothing
Conclusion to Time - Series Analysis and Forecasting
Course Introduction
Course Syllabus
Python for Data Analysis
Feature Engineering Techniques for Time-Series Data
Time Series Transformation Techniques
Practice Quiz: Time - Series Basics
Practice Quiz: Time-Series Data Loading and Feature Engineering
Practice Quiz: Time-Series Visualization
Practice Quiz: Time - Series Transformation
Graded Quiz: Time-Series Analysis and Forecasting
Time-Series Analysis and Forecasting
Ungraded Lab: Time Series Analysis and Forecasting
Time-Series Models
Introduction to Time-Series Models
Test Train Split in Python Part 1
Test Train Split in Python Part 2
Walk Forward Validation
Naïve (Persistence) Model in Python Part 1
Naïve (Persistence) Model in Python Part 2
Auto-regression basics
Auto-regression model creation Part 1
Auto-regression model creation Part 2
With Validation in Python
Moving average model basics
Moving average model in python Part 1
Moving average model in python Part 2
ACF and PACF
ARIMA Model Basics
ARIMA Model in Python Part 1
ARIMA Model in Python Part 2
ARIMA Model validation in python
SARIMA Model
SARIMA Model in Python Part 1
SARIMA Model in Python Part 2
Conclusion to Time-Series Models
Evaluating Time Series Forecasting Models
Choosing the Right Forecasting Method
Understanding ACF and PACF Plots
Practice Quiz: Naïve (Persistence) Model
Practice Quiz: Auto Regression Model
Practice Quiz: Moving Average Model
Practice Quiz: ARIMA Model
Practice Quiz: Time-Series Models
Graded Quiz: Time-Series Models
Time-Series Models
Ungraded Labs: Time Series Models
Linear Regression - Data Preprocessing
Introduction to Linear Regression - Data Preprocessing
The dataset and data dictionary Part 1
The dataset and data dictionary Part 2
Importing data in Python
Univariant analysis and EDD in Python Part 1
Univariant analysis and EDD in Python Part 2
Outlier treatment in Python
Missing value imputation in python
Seasonality in data
Bi-Variant Analysis and Variable Transformation Part 1
Bi-Variant Analysis and Variable Transformation Part 2
Handling quantitative data
Dummy variable creation in python
Correlation analysis
Correlation analysis in python
Conclusion to Linear Regression - Data Preprocessing
Handling Outliers in Time Series Data
Bivariate Analysis
Lagged Correlation: Analyzing Time-Series Dependencies
Practice Quiz: EDD and Outlier
Practice Quiz: Missing Values
Practice Quiz: Bi-variant Analysis
Practice Quiz: Correlation Analysis
Graded Assessment: Linear Regression - Data Preprocessing
Linear Regression - Data Preprocessing
Ungraded Labs: Linear Progression Data Preprocessing
Linear Regression - Model Creation
Introduction to Linear Regression - Model Creation
OLS method
Accessing Accuracy of Predicted Coefficients Part 1
Accessing Accuracy of Predicted Coefficients Part 2
RSE and R - Square
Simple Linear Regression in Python Part 1
Simple Linear Regression in Python Part 2
Multiple-Linear Regression
Multiple-linear regression Part 1
Multiple-linear regression Part 2
F-Statistics
Results of Categorical Variables
Test-train Split in python
Conclusion to Linear Regression - Model Creation
Conclusion to Regression & Forecasting for Data Scientists using Python
Understanding OLS Method
Applied Linear Statistical Models
Understanding Test-Train
Practice Quiz: Basics Equation
Practice Quiz: Simple Linear Regression
Practice Quiz: Multiple-linear regression
Practice Quiz: Test-Train
Graded Quiz: Linear Regression - Model Creation
Linear Regression - Model Creation
Ungraded Labs: Linear Regression - Model Creation
Regression & Forecasting for Data Scientists using Python at Coursera Admission Process
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