No, you will not earn university credit for completing this Specialization.

By completing this Specialization, you will have a foundation of data analysis in Python for data science.

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Spezialisierung „Data Analysis with Python“

Spezialisierung „Data Analysis with Python“

Launch your career in Data Science & Data Analysis.

By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis.

Unterrichtet in Deutsch (KI-Synchronisation)

Dozent: Di Wu

3.068 bereits angemeldet

Bei Mehr erfahren enthalten

5-teilige Kursreihe

Befassen Sie sich eingehend mit einem Thema

aus 31 Bewertungen von Kursen in diesem Programm

Stufe Mittel

Empfohlene Erfahrung

2 months to complete

unter 10 Stunden pro Woche

5-teilige Kursreihe

Befassen Sie sich eingehend mit einem Thema

aus 31 Bewertungen von Kursen in diesem Programm

Stufe Mittel

Empfohlene Erfahrung

2 months to complete

unter 10 Stunden pro Woche

Was Sie lernen werden

Describe and define the fundamental concepts and techniques used in Data Analysis. Identify the appropriate techniques to apply.
Compare and contrast different Data Analysis techniques, including Classification, Regression, Clustering, Dimension Reduction, and Association Rules
Design and implement effective Data Analysis workflows, including data preprocessing, feature selection, and model selection

Kompetenzen, die Sie erwerben

Kategorie: Machine Learning Methods
Kategorie: Model Evaluation
Kategorie: Regression Analysis
Kategorie: Dimensionality Reduction
Kategorie: Data Preprocessing
Kategorie: Data Analysis
Kategorie: Logistic Regression
Kategorie: Applied Machine Learning
Kategorie: Predictive Modeling
Kategorie: Analytical Skills
Kategorie: Anomaly Detection
Kategorie: Statistical Analysis
Kategorie: Machine Learning Algorithms
Kategorie: Model Training
Kategorie: Data Mining
Kategorie: Unsupervised Learning
Kategorie: Statistical Machine Learning
Kategorie: Machine Learning
Kategorie: Supervised Learning

Werkzeuge, die Sie lernen werden

Kategorie: Classification Algorithms

Wichtige Details

Zertifikat zur Vorlage

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Unterrichtet in Deutsch (KI-Synchronisation)

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

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Erlernen Sie ein Thema oder ein Tool mit echten Projekten.
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Erwerben Sie ein Karrierezertifikat von University of Colorado Boulder.

Spezialisierung - 5 Kursreihen

The Data Analysis specialization will provide a comprehensive overview of various techniques for analyzing data. The courses will cover a wide range of topics, including Classification, Regression, Clustering, Dimension Reduction, and Association Rules. The courses will be very hands-on and will include real-life examples and case studies, which will help students develop a deeper understanding of Data Analysis concepts and techniques. The courses will culminate in a project that demonstrates the student's mastery of Data Analysis techniques.

Übungsprojekt

The "Data Analysis Project" course empowers students to apply their knowledge and skills gained in this specialization to conduct a real-life data analysis project of their interest. Participants will explore various directions in data analysis, including supervised and unsupervised learning, regression, clustering, dimension reduction, association rules, and outlier detection. Throughout the modules, students will learn essential data analysis techniques and methodologies and embark on a journey from raw data to knowledge and intelligence. By completing the course, students will be proficient in data analysis, capable of applying their expertise in diverse projects and making data-driven decisions.

Classification Analysis

KURS 1, 38 Stunden

Was Sie lernen werden

Understand the concept and significance of classification as a supervised learning method.
Identify and describe different classifiers, apply each classifier to perform binary and multiclass classification tasks on diverse datasets.
Evaluate the performance of classifiers, select and fine-tune classifiers based on dataset characteristics and learning requirements.

Kompetenzen, die Sie erwerben

Kategorie: Classification Algorithms

Kategorie: Model Evaluation

Kategorie: Machine Learning Algorithms

Kategorie: Logistic Regression

Kategorie: Supervised Learning

Kategorie: Decision Tree Learning

Kategorie: Analytical Skills

Kategorie: Fine-tuning

Kategorie: Applied Machine Learning

Kategorie: Feature Engineering

Kategorie: Bayesian Statistics

Kategorie: Data Analysis

Kategorie: Machine Learning Methods

Kategorie: Predictive Modeling

Kategorie: Model Training

Regression Analysis

KURS 2, 40 Stunden

Was Sie lernen werden

Understand the principles and significance of regression analysis in supervised learning.
Implement cross-validation methods to assess model performance and optimize hyperparameters.
Comprehend ensemble methods (bagging, boosting, and stacking) and their role in enhancing regression model accuracy.

Kompetenzen, die Sie erwerben

Kategorie: Regression Analysis

Kategorie: Model Evaluation

Kategorie: Predictive Modeling

Kategorie: Machine Learning Methods

Kategorie: Statistical Modeling

Kategorie: Model Training

Kategorie: Statistical Analysis

Kategorie: Statistical Machine Learning

Kategorie: Supervised Learning

Kategorie: Data Analysis

Kategorie: Model Optimization

Kategorie: Statistical Methods

Kategorie: Applied Machine Learning

Clustering Analysis

KURS 3, 37 Stunden

Was Sie lernen werden

Understand the principles and significance of unsupervised learning, particularly clustering and dimension reduction.
Apply clustering techniques to diverse datasets for pattern discovery and data exploration.
Implement Principal Component Analysis (PCA) for dimension reduction and interpret the reduced feature space.

Kompetenzen, die Sie erwerben

Kategorie: Unsupervised Learning

Kategorie: Dimensionality Reduction

Kategorie: Machine Learning Methods

Kategorie: Machine Learning

Kategorie: Machine Learning Algorithms

Kategorie: Exploratory Data Analysis

Kategorie: Algorithms

Kategorie: Model Evaluation

Kategorie: Applied Machine Learning

Kategorie: Data Analysis

Kategorie: Data Mining

Association Rules Analysis

KURS 4, 23 Stunden

Was Sie lernen werden

Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection
Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.
Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.

Kompetenzen, die Sie erwerben

Kategorie: Anomaly Detection

Kategorie: Unsupervised Learning

Kategorie: Algorithms

Kategorie: Feature Engineering

Kategorie: Data Mining

Kategorie: Data Processing

Kategorie: Machine Learning Methods

Kategorie: Data Preprocessing

Kategorie: Machine Learning Algorithms

Kategorie: Data Analysis

Kategorie: Applied Machine Learning

Data Analysis with Python Project

KURS 5, 18 Stunden

Was Sie lernen werden

Define the scope and direction of a data analysis project, identifying appropriate techniques and methodologies for achieving project objectives.
Apply various classification and regression algorithms and implement cross-validation and ensemble techniques to enhance the performance of models.
Apply various clustering, dimension reduction association rule mining, and outlier detection algorithms for unsupervised learning models.