This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
Data Wrangling with Python Specialization
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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.
Instructor: Di Wu
1,729 already enrolled
Included with 
Get in-depth knowledge of a subject
4.7
(29 reviews)
Beginner level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
4.7
(29 reviews)
Beginner level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
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.
Overview
Skills you'll gain
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
32 practice exercises
Advance your subject-matter expertise
- 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 Colorado Boulder
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
Category: Python Programming
Category: Data Structures
Category: Data Wrangling
Category: Data Manipulation
Category: NumPy
Category: Pandas (Python Package)
Category: Data Quality
Category: Programming Principles
Category: Data Integration
Category: Data Analysis
Category: Data Transformation
Category: Scripting
Category: Exploratory Data Analysis
Category: Data Cleansing
Category: Data Visualization
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
Category: Data Collection
Category: Data Integration
Category: Pandas (Python Package)
Category: Web Scraping
Category: SQL
Category: Data Import/Export
Category: Application Programming Interface (API)
Category: Databases
Category: Data Quality
Category: Extensible Markup Language (XML)
Category: Data Cleansing
Category: Data Processing
Category: Query Languages
Category: Data Manipulation
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
Category: Scatter Plots
Category: Seaborn
Category: Data Analysis
Category: Matplotlib
Category: Correlation Analysis
Category: Box Plots
Category: Descriptive Statistics
Category: Pandas (Python Package)
Category: Data Visualization Software
Category: Statistics
Category: Statistical Analysis
Category: Data Presentation
Category: Data Visualization
Category: Statistical Methods
Category: Exploratory Data Analysis
Category: Data-Driven Decision-Making
Category: Python Programming
Category: Data Manipulation
Category: Data Storytelling
Category: 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
Category: Data Warehousing
Category: Pivot Tables And Charts
Category: Dimensionality Reduction
Category: Sampling (Statistics)
Category: Anomaly Detection
Category: Data Cleansing
Category: Feature Engineering
Category: Data Manipulation
Category: Statistical Methods
Category: Exploratory Data Analysis
Category: Data Processing
Category: Data Transformation
Category: Data Quality
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
Category: Data Wrangling
Category: Data Integration
Category: Dimensionality Reduction
Category: Data Cleansing
Category: Data Validation
Category: Data Manipulation
Category: Data Collection
Category: Exploratory Data Analysis
Category: Data Transformation
Category: Pivot Tables And Charts
Category: Data Visualization
Category: Data Analysis
Category: Data Mining
Category: Statistical Visualization
Category: Data Quality
Category: Statistical Analysis
Category: Data Processing
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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
It will take about 2 months to complete this Specialization.
Learners are expected to have a mastery of the fundamentals of Python programming and statistics.
It is recommended to complete the courses in this Specialization sequentially.
More questions
Financial aid available,