Regression & Forecasting for Data Scientists using Python

Regression & Forecasting for Data Scientists using Python

Taught in English


Gain insight into a topic and learn the fundamentals


Instructor: EDUCBA


(22 reviews)

Intermediate level

Recommended experience

12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

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

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There are 4 modules in this course

The Time-Series Analysis and Forecasting module provides a comprehensive exploration of techniques to extract insights and predict trends from sequential data. You will master fundamental concepts such as trend identification, seasonality, and model selection. With hands-on experience in leading software, you will learn to build, validate, and interpret forecasting models. By delving into real-world case studies and ethical considerations, you will be equipped to make strategic decisions across industries using the power of time-series analysis. This module is a valuable asset for professionals seeking to harness the potential of temporal data. You will develop expertise in time series analysis and forecasting. Discover techniques for exploratory data analysis, time series decomposition, trend analysis, and handling seasonality. Acquire the skill to differentiate between different types of patterns and understand their implications in forecasting.

What's included

18 videos5 readings5 quizzes1 discussion prompt1 ungraded lab

Time-series models are powerful tools designed to uncover patterns and predict future trends within sequential data. By analyzing historical patterns, trends, and seasonal variations, these models provide insights into data behavior over time. Utilizing methods like ARIMA, exponential smoothing, and state-space models, they enable accurate forecasting, empowering decision-makers across various fields to make informed choices based on data-driven predictions.

What's included

22 videos3 readings6 quizzes1 discussion prompt1 ungraded lab

The Linear Regression: Data Preprocessing module is a fundamental course that equips you with essential skills for preparing and optimizing data before applying linear regression techniques. Hands-on learning will teach you the importance of data quality, addressing missing values, outlier detection, and feature scaling. You will learn how to transform raw data into a clean, normalized format by delving into real-world datasets, ensuring accurate and reliable linear regression model outcomes. This module is crucial to building strong foundational knowledge in predictive modeling and data analysis.

What's included

16 videos3 readings5 quizzes1 discussion prompt1 ungraded lab

The Linear Regression - Model Creation module offers a comprehensive understanding of building predictive models through linear regression techniques. You will learn to select and engineer relevant features, apply regression algorithms, and interpret model coefficients. By exploring real-world case studies, you will gain insights into model performance evaluation and learn how to fine-tune parameters for optimal results. This module empowers you to create robust linear regression models for data-driven decision-making in diverse fields.

What's included

15 videos3 readings5 quizzes1 discussion prompt1 ungraded lab


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