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Regression & Forecasting for Data Scientists using Python
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EDUCBA

Regression & Forecasting for Data Scientists using Python

EDUCBA

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4 Module
Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.
4.6

(41 Bewertungen)

Stufe Mittel

Empfohlene Erfahrung

Empfohlene Erfahrung

Stufe „Mittel“

Basic knowledge of Python programming.

Familiarity with fundamental data analysis concepts.

Understanding statistical concepts but not mandatory.

1 Woche zu vervollständigen
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In Ihrem eigenen Lerntempo lernen

4 Module
Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.
4.6

(41 Bewertungen)

Stufe Mittel

Empfohlene Erfahrung

Empfohlene Erfahrung

Stufe „Mittel“

Basic knowledge of Python programming.

Familiarity with fundamental data analysis concepts.

Understanding statistical concepts but not mandatory.

1 Woche zu vervollständigen
unter 10 Stunden pro Woche
Flexibler Zeitplan
In Ihrem eigenen Lerntempo lernen
  • Info
  • Module
  • Empfehlungen
  • Referenzen
  • Bewertungen

Was Sie lernen werden

  • 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

Kompetenzen, die Sie erwerben

  • Kategorie: Exploratory Data Analysis
    Exploratory Data Analysis
  • Kategorie: Supervised Learning
    Supervised Learning
  • Kategorie: Statistical Analysis
    Statistical Analysis
  • Kategorie: Scikit Learn (Machine Learning Library)
    Scikit Learn (Machine Learning Library)
  • Kategorie: Python Programming
    Python Programming
  • Kategorie: Machine Learning Algorithms
    Machine Learning Algorithms
  • Kategorie: Data Cleansing
    Data Cleansing
  • Kategorie: Predictive Modeling
    Predictive Modeling
  • Kategorie: Forecasting
    Forecasting
  • Kategorie: Pandas (Python Package)
    Pandas (Python Package)
  • Kategorie: Feature Engineering
    Feature Engineering
  • Kategorie: Time Series Analysis and Forecasting
    Time Series Analysis and Forecasting
  • Kategorie: Regression Analysis
    Regression Analysis
  • Kategorie: Data Transformation
    Data Transformation
  • Kategorie: Data Analysis
    Data Analysis

Wichtige Details

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21 Aufgaben

Unterrichtet in Englisch

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In diesem Kurs gibt es 4 Module

Course Description: 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.

Learning Objectives: • Develop expertise in time series analysis, forecasting, and linear regression. • Gain proficiency in Python programming for data analysis and modeling. • Analyze the techniques for exploratory data analysis, trend identification, and seasonality handling. • Figure out various time-series models and implement them using Python. • Prepare and preprocess data for accurate linear regression modeling. • Predict and interpret linear regression models for informed decision-making. There are Four Modules in this Course: Module 1: Time-Series Analysis and Forecasting Module description: 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, they will learn to build, validate, and interpret forecasting models. By delving into real-world case studies and ethical considerations, participants 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. Module 2: Time-Series Models Module description: 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. You will acquire the ability to build forecasting models for future predictions based on historical data. Discover various forecasting methods, such as ARIMA models and seasonal forecasting techniques, and implement them using Python programming. Develop the ability to formulate customized time-series forecasting strategies based on data characteristics. Module 3: Linear Regression - Data Preprocessing Module description: The Linear Regression - Data Preprocessing module is a fundamental course that equips participants with essential skills for preparing and optimizing data before applying linear regression techniques. Through hands-on learning, participants will understand 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. You will gain insights into various regression techniques such as linear regression, polynomial regression, and logistic regression, and their implementation using Python programming. Identify missing data and outliers within datasets and implement appropriate strategies to handle them effectively. Recognize the significance of feature scaling and selection and learn how to apply techniques such as standardization and normalization to improve model convergence and interpretability. Module 4: Linear Regression - Model Creation Module description: The Linear Regression - Model Creation module offers a comprehensive understanding of building predictive models through linear regression techniques. You will learn to choose 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 acquire 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. You will understand how to identify and select relevant features from datasets for inclusion in linear regression models. Acquire the skills to interpret model coefficients, recognize their significance, and deliver the implications of these coefficients to non-technical stakeholders. Discover how to fine-tune model parameters, and regularization techniques, and perform cross-validation to enhance model generalization. Target Learner: This course is designed for aspiring data scientists, analysts, and professionals seeking to enhance their skills in regression analysis, forecasting, and Python programming. It is suitable for those looking to harness the power of temporal data and predictive modeling in their careers. Learner Prerequisites: • Basic knowledge of Python programming. • Familiarity with fundamental data analysis concepts. • Understanding statistical concepts is beneficial but not mandatory. Reference Files: You will have access to code files in the Resources section and lab files in the Lab Manager section. Course Duration: 5 hours 44 minutes Total Duration: Approximately 4 weeks • Module 1: Time-Series Analysis and Forecasting (1 week) • Module 2: Time-Series Models (1 week) • Module 3: Linear Regression - Data Preprocessing (1 week) • Module 4: Linear Regression - Model Creation (1 week)

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.

Das ist alles enthalten

18 Videos5 Lektüren5 Aufgaben1 Diskussionsthema1 Unbewertetes Labor

18 Videos•Insgesamt 91 Minuten
  • Introduction to Regression & Forecasting for Data Scientists using Python•1 Minute•Modulvorschau
  • Introduction to Time-Series Basics•1 Minute
  • Time-Series Forecasting Use Cases and steps•6 Minuten
  • Forecasting Model Creation•5 Minuten
  • Time-Series Basic Notations•7 Minuten
  • Installing Anaconda and Jupyter Notebook•4 Minuten
  • Data Loading in Python Part 1•6 Minuten
  • Data Loading in Python Part 2•5 Minuten
  • Data Loading in Python Part 3•4 Minuten
  • Feature Engineering in Python Part 1•8 Minuten
  • Feature Engineering in Python Part 2•5 Minuten
  • Visualization in Python Part 1•7 Minuten
  • Visualization in Python Part 2•7 Minuten
  • Visualization in Python Part 3•8 Minuten
  • Time-Series Power Transformation•2 Minuten
  • Moving Average•5 Minuten
  • Exponential Smoothing•2 Minuten
  • Conclusion to Time - Series Analysis and Forecasting•0 Minuten
5 Lektüren•Insgesamt 28 Minuten
  • Course Introduction•5 Minuten
  • Course Syllabus•5 Minuten
  • Python for Data Analysis•6 Minuten
  • Feature Engineering Techniques for Time-Series Data•6 Minuten
  • Time Series Transformation Techniques•6 Minuten
5 Aufgaben•Insgesamt 32 Minuten
  • Graded Quiz: Time-Series Analysis and Forecasting•20 Minuten
  • Practice Quiz: Time - Series Basics•3 Minuten
  • Practice Quiz: Time-Series Data Loading and Feature Engineering•3 Minuten
  • Practice Quiz: Time-Series Visualization•3 Minuten
  • Practice Quiz: Time - Series Transformation•3 Minuten
1 Diskussionsthema•Insgesamt 10 Minuten
  • Time-Series Analysis and Forecasting•10 Minuten
1 Unbewertetes Labor•Insgesamt 30 Minuten
  • Ungraded Lab: Time Series Analysis and Forecasting•30 Minuten

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.

Das ist alles enthalten

22 Videos3 Lektüren6 Aufgaben1 Diskussionsthema1 Unbewertetes Labor

22 Videos•Insgesamt 112 Minuten
  • Introduction to Time-Series Models•2 Minuten•Modulvorschau
  • Test Train Split in Python Part 1•5 Minuten
  • Test Train Split in Python Part 2•5 Minuten
  • Walk Forward Validation•3 Minuten
  • Naïve (Persistence) Model in Python Part 1•7 Minuten
  • Naïve (Persistence) Model in Python Part 2•5 Minuten
  • Auto-regression basics•3 Minuten
  • Auto-regression model creation Part 1•4 Minuten
  • Auto-regression model creation Part 2•4 Minuten
  • With Validation in Python•7 Minuten
  • Moving average model basics•4 Minuten
  • Moving average model in python Part 1•4 Minuten
  • Moving average model in python Part 2•5 Minuten
  • ACF and PACF•6 Minuten
  • ARIMA Model Basics•4 Minuten
  • ARIMA Model in Python Part 1•6 Minuten
  • ARIMA Model in Python Part 2•6 Minuten
  • ARIMA Model validation in python•5 Minuten
  • SARIMA Model•6 Minuten
  • SARIMA Model in Python Part 1•5 Minuten
  • SARIMA Model in Python Part 2•5 Minuten
  • Conclusion to Time-Series Models•1 Minute
3 Lektüren•Insgesamt 18 Minuten
  • Evaluating Time Series Forecasting Models•6 Minuten
  • Choosing the Right Forecasting Method•6 Minuten
  • Understanding ACF and PACF Plots•6 Minuten
6 Aufgaben•Insgesamt 35 Minuten
  • Graded Quiz: Time-Series Models•20 Minuten
  • Practice Quiz: Naïve (Persistence) Model•3 Minuten
  • Practice Quiz: Auto Regression Model•3 Minuten
  • Practice Quiz: Moving Average Model•3 Minuten
  • Practice Quiz: ARIMA Model•3 Minuten
  • Practice Quiz: Time-Series Models•3 Minuten
1 Diskussionsthema•Insgesamt 10 Minuten
  • Time-Series Models•10 Minuten
1 Unbewertetes Labor•Insgesamt 30 Minuten
  • Ungraded Labs: Time Series Models•30 Minuten

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.

Das ist alles enthalten

16 Videos3 Lektüren5 Aufgaben1 Diskussionsthema1 Unbewertetes Labor

16 Videos•Insgesamt 76 Minuten
  • Introduction to Linear Regression - Data Preprocessing•2 Minuten•Modulvorschau
  • The dataset and data dictionary Part 1•4 Minuten
  • The dataset and data dictionary Part 2•4 Minuten
  • Importing data in Python•3 Minuten
  • Univariant analysis and EDD in Python Part 1•4 Minuten
  • Univariant analysis and EDD in Python Part 2•4 Minuten
  • Outlier treatment in Python•6 Minuten
  • Missing value imputation in python•3 Minuten
  • Seasonality in data•4 Minuten
  • Bi-Variant Analysis and Variable Transformation Part 1•7 Minuten
  • Bi-Variant Analysis and Variable Transformation Part 2•7 Minuten
  • Handling quantitative data•5 Minuten
  • Dummy variable creation in python•4 Minuten
  • Correlation analysis•6 Minuten
  • Correlation analysis in python•5 Minuten
  • Conclusion to Linear Regression - Data Preprocessing•1 Minute
3 Lektüren•Insgesamt 25 Minuten
  • Handling Outliers in Time Series Data•8 Minuten
  • Bivariate Analysis•7 Minuten
  • Lagged Correlation: Analyzing Time-Series Dependencies•10 Minuten
5 Aufgaben•Insgesamt 32 Minuten
  • Practice Quiz: EDD and Outlier •3 Minuten
  • Practice Quiz: Missing Values•3 Minuten
  • Practice Quiz: Bi-variant Analysis•3 Minuten
  • Practice Quiz: Correlation Analysis•3 Minuten
  • Graded Assessment: Linear Regression - Data Preprocessing•20 Minuten
1 Diskussionsthema•Insgesamt 10 Minuten
  • Linear Regression - Data Preprocessing•10 Minuten
1 Unbewertetes Labor•Insgesamt 30 Minuten
  • Ungraded Labs: Linear Progression Data Preprocessing•30 Minuten

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.

Das ist alles enthalten

15 Videos3 Lektüren5 Aufgaben1 Diskussionsthema1 Unbewertetes Labor

15 Videos•Insgesamt 68 Minuten
  • Introduction to Linear Regression - Model Creation•1 Minute•Modulvorschau
  • OLS method•7 Minuten
  • Accessing Accuracy of Predicted Coefficients Part 1•7 Minuten
  • Accessing Accuracy of Predicted Coefficients Part 2•5 Minuten
  • RSE and R - Square•5 Minuten
  • Simple Linear Regression in Python Part 1•4 Minuten
  • Simple Linear Regression in Python Part 2•4 Minuten
  • Multiple-Linear Regression•5 Minuten
  • Multiple-linear regression Part 1•6 Minuten
  • Multiple-linear regression Part 2•3 Minuten
  • F-Statistics•4 Minuten
  • Results of Categorical Variables•4 Minuten
  • Test-train Split in python•5 Minuten
  • Conclusion to Linear Regression - Model Creation•0 Minuten
  • Conclusion to Regression & Forecasting for Data Scientists using Python•0 Minuten
3 Lektüren•Insgesamt 25 Minuten
  • Understanding OLS Method•7 Minuten
  • Applied Linear Statistical Models•8 Minuten
  • Understanding Test-Train•10 Minuten
5 Aufgaben•Insgesamt 32 Minuten
  • Graded Quiz: Linear Regression - Model Creation•20 Minuten
  • Practice Quiz: Basics Equation•3 Minuten
  • Practice Quiz: Simple Linear Regression•3 Minuten
  • Practice Quiz: Multiple-linear regression•3 Minuten
  • Practice Quiz: Test-Train•3 Minuten
1 Diskussionsthema•Insgesamt 10 Minuten
  • Linear Regression - Model Creation•10 Minuten
1 Unbewertetes Labor•Insgesamt 30 Minuten
  • Ungraded Labs: Linear Regression - Model Creation•30 Minuten

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Dozent

EDUCBA
EDUCBA
EDUCBA
133 Kurse•94.100 Lernende

von

EDUCBA

von

EDUCBA

Welcome to EDUCBA, a place where knowledge is limitless! We provide a wide selection of instructive and engaging programmes designed to empower students of all ages and experiences. From the convenience of your home, start a revolutionary educational experience with our cutting-edge technologies courses and experienced instructors.

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Lernender seit 2018
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Bewertungen von Lernenden

4.6

41 Bewertungen

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Zeigt 3 von 41 an

A
AS
5

Geprüft am 20. März 2024

Essential guide for data scientists: simplifies regression and forecasting in Python with powerful techniques, good course

K
KK
5

Geprüft am 13. Feb. 2024

The course provided a comprehensive overview. Concepts were explained clearly with examples that made it easy to understand.

M
MM
5

Geprüft am 4. März 2024

Excellent course! All concepts are explained well.

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Häufig gestellte Fragen

Linear Regression: Use when you expect a linear relationship between the independent and dependent variables.

Polynomial Regression: Suitable when the relationship appears to be polynomial, like quadratic or cubic.

Lasso or Ridge Regression: Helpful when dealing with multicollinearity or to prevent overfitting in high-dimensional datasets.

Mean Absolute Error (MAE): Measures the average absolute differences between predicted and actual values.

Mean Squared Error (MSE): Calculates the average of squared differences between predicted and actual values.

Root Mean Squared Error (RMSE): The square root of MSE, providing a more interpretable error metric.

Data Preprocessing: Clean and preprocess your time series data, handle missing values, and ensure it's in a suitable format (e.g., pandas DataFrame).

Train-Test Split: Split your data into training and testing sets to evaluate model performance.

Feature Engineering: Create relevant features, such as lag values, rolling statistics, and seasonality indicators.

Model Selection: Experiment with different forecasting models, such as ARIMA, Exponential Smoothing, or machine learning models, based on your data characteristics.

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