University of Pennsylvania
How to Use Data Specialization
University of Pennsylvania

How to Use Data Specialization

Analyze Data, Build Models, and Present Insights. Transform data into actionable insights through data analysis, predictive modeling, data visualization, and communication of results.

Brandon Krakowsky

Instructor: Brandon Krakowsky

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Get in-depth knowledge of a subject
Beginner level

Recommended experience

8 weeks to complete
at 8 hours a week
Flexible schedule
Learn at your own pace
Earn a Certificate
With paid plans
Get in-depth knowledge of a subject
Beginner level

Recommended experience

8 weeks to complete
at 8 hours a week
Flexible schedule
Learn at your own pace
Earn a Certificate
With paid plans

What you'll learn

  • Master the process of data wrangling, including data storage, access, and manipulation using SQL.

  • Perform exploratory data analysis (EDA) using Python, focusing on data inspection, cleaning, visualization, and summarization.

  • Apply predictive analytics techniques to make data-driven predictions using Python.

  • Create compelling data visualizations with Tableau for decision-making and storytelling.

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Taught in English
22 practice exercises

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  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from University of Pennsylvania

Specialization - 3 course series

What you'll learn

  • Classifying and analyzing data.

  • Executing SQL queries to work with data.

  • Using Python and pandas library to manipulate and explore data.

Skills you'll gain

Exploratory Data Analysis, SQL, Data Manipulation, Pandas (Python Package), Data Analysis, Data Visualization Software, Matplotlib, Data Science, Seaborn, Analytics, Data Cleansing, Big Data, Data Visualization, Data Management, Data Storage, and NumPy

What you'll learn

  • Implement data preprocessing and model training procedures for regression.

  • Interpret feature importance in decision trees and random forests.

  • Explain the difference between supervised and unsupervised learning.

Skills you'll gain

Predictive Analytics, Unsupervised Learning, Decision Tree Learning, Regression Analysis, Random Forest Algorithm, Predictive Modeling, Supervised Learning, Forecasting, Machine Learning, Data Analysis, Classification And Regression Tree (CART), Feature Engineering, Python Programming, and Scikit Learn (Machine Learning Library)

What you'll learn

  • Explore and interpret data through various visualization techniques, identifying trends, outliers, and patterns.

  • Create interactive and informative visualizations that enable data-driven decision-making and storytelling.

  • Analyze engaging and persuasive data presentations that captivate and inform stakeholders.

Skills you'll gain

Tableau Software, Data Storytelling, Data Visualization Software, Data Presentation, Trend Analysis, Stakeholder Communications, Presentations, Spatial Analysis, Verbal Communication Skills, Data-Driven Decision-Making, Data Visualization, Data Analysis, and Scatter Plots

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Instructor

Brandon Krakowsky
University of Pennsylvania
10 Courses165,497 learners

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