University of Colorado Boulder
Data Wrangling with Python Specialization
University of Colorado Boulder

Data Wrangling with Python Specialization

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

Di Wu

Instructor: Di Wu

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4.7

(31 reviews)

Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.7

(31 reviews)

Beginner level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Define techniques and methods for collecting data from various sources including files, web, databases, etc.

  • Identify statistical analysis and visualization techniques that can be used to gain insights into the data.

  • Calculate and apply techniques for data preprocessing such as dealing with missing values, outliers, sampling, normalization, and discretization.

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Taught in English
32 practice exercises

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Specialization - 5 course series

What you'll learn

  • You will be able to describe the fundamentals of programming in Python.

  • You will be able to identify data structures for efficient organization and manipulation of data.

  • You will practice using NumPy and Pandas for numerical computing, data manipulation, and analysis.

Skills you'll gain

Python Programming, Data Structures, Data Wrangling, Pandas (Python Package), Data Manipulation, NumPy, Data Transformation, Scripting, Data Integration, Exploratory Data Analysis, Statistical Analysis, Data Analysis, Programming Principles, Data Cleansing, and Data Quality

What you'll learn

  • How to utilize Python and Python packages to collect data from various sources

  • How to integrate data collected from various sources to a unified dataset for further processing and analysis

Skills you'll gain

Data Collection, Pandas (Python Package), Data Integration, Application Programming Interface (API), Data Import/Export, SQL, Web Scraping, Databases, Data Quality, Data Manipulation, Query Languages, Data Processing, Data Cleansing, and Extensible Markup Language (XML)

What you'll learn

  • Understand and communicate the various statistical aspects of datasets, including measures of central tendency, variation, location, and correlation.

  • Utilize Pandas for data manipulation and preparation to set the foundation for data visualization.

  • Utilize Matplotlib and Seaborn to create accurate and meaningful data visualizations.

Skills you'll gain

Scatter Plots, Box Plots, Pandas (Python Package), Seaborn, Descriptive Statistics, Matplotlib, Correlation Analysis, Data Visualization Software, Data Presentation, Data Manipulation, Statistical Analysis, Data Visualization, Statistical Methods, Python Programming, Exploratory Data Analysis, Statistical Visualization, Statistics, Data Analysis, Data Storytelling, and Plot (Graphics)

What you'll learn

  • Understand the importance of data processing and manipulation in the data analysis pipeline.

  • Learn techniques to handle missing values and outliers, data reduction, and data scaling and discretization.

  • Understand the concept of data cube and perform multidimensional aggregation for exploratory analysis.

Skills you'll gain

Pivot Tables And Charts, Data Warehousing, Anomaly Detection, Sampling (Statistics), Dimensionality Reduction, Data Cleansing, Data Transformation, Data Quality, Exploratory Data Analysis, Feature Engineering, Data Manipulation, and Data Processing

What you'll learn

  • Initiate and conduct a data wrangling project from raw data to a refined dataset for analysis.

  • Apply data wrangling techniques learned in the specialization to handle real-life data scenarios.

  • Utilize Python libraries and tools effectively for data wrangling tasks. Communicate and present data wrangling results effectively to stakeholders.

Skills you'll gain

Data Wrangling, Dimensionality Reduction, Data Validation, Data Integration, Pivot Tables And Charts, Data Transformation, Data Cleansing, Data Collection, Exploratory Data Analysis, Data Analysis, Data Visualization, Data Processing, Data Quality, Data Manipulation, Statistical Visualization, Data Mining, and Statistical Analysis

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Instructor

Di Wu
University of Colorado Boulder
21 Courses54,035 learners

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