Data Wrangling with Python Project
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Data Wrangling with Python Project
This course is part of Data Wrangling with Python Specialization
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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 Manipulation
- Data Mining
- Data Processing
- Data Quality
- Data Collection
- Pivot Tables And Charts
- Data Analysis
- Data Visualization
- Data Pipelines
- Data Cleansing
- Statistical Analysis
- Statistical Visualization
- Data Validation
- Quality Assurance
- Data Transformation
- Data Integration
- Data Wrangling
- Descriptive Statistics
- Exploratory Data Analysis
- Data Preprocessing
Details to know
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There are 5 modules in this course
The "Data Wrangling Project" course provides students with an opportunity to apply the knowledge gained throughout the specialization in a real-life data wrangling project of their interest. Participants will follow the data wrangling pipeline step by step, from identifying data sources to processing and integrating data, to achieve a fine dataset ready for analysis. This course enables students to gain hands-on experience in the data wrangling process and prepares them to handle complex data challenges in real-world scenarios.
Throughout the course, students will work on their data wrangling project, applying the knowledge and skills gained in each module to achieve a refined and well-prepared dataset. By the end of the course, participants will be proficient in the data wrangling process and ready to tackle real-world data challenges in diverse domains.
In this introductory week, you will gain an understanding of the data wrangling pipeline, which serves as a structured approach to transform raw data into a cleaned and organized dataset for analysis. You will learn the key stages involved in the pipeline, setting the foundation for the rest of the course.
What's included
4 readings1 assignment
4 readings•Total 151 minutes
- Course Updates and Accessibility Support•1 minute
- Assessment Strategy•30 minutes
- Data Wrangling Pipeline•60 minutes
- Data Wrangling Project Outline•60 minutes
1 assignment•Total 60 minutes
- Self Reflection on Data Wrangling Pipiline•60 minutes
In this week, you will learn how to identify and define the scope and objectives of your data wrangling project. You will explore various data sources, understand their structure, and assess the suitability of each source for the project.
What's included
3 readings1 assignment
3 readings•Total 180 minutes
- Scope and Objectives•60 minutes
- Identify the Sources•60 minutes
- Understand the Sources•60 minutes
1 assignment•Total 60 minutes
- Self Reflection On Project Proposal•60 minutes
This week covers the data collection and integration stage of the data wrangling process. You will learn techniques for data collection, validate the collected data, and integrate data from multiple sources.
What's included
3 readings1 assignment
3 readings•Total 180 minutes
- Data Collection•60 minutes
- Data Validation•60 minutes
- Data Integration•60 minutes
1 assignment•Total 60 minutes
- Self Reflection on Data Collection and Integration•60 minutes
This week focuses on gaining a comprehensive understanding of the dataset through statistical analysis and data visualization. You will learn how to perform descriptive statistics, create informative visualizations, and conduct exploratory data analysis (EDA).
What's included
3 readings1 assignment
3 readings•Total 180 minutes
- Data Statistical Understanding•60 minutes
- Data Visualization•60 minutes
- Exploratory Data Analysis•60 minutes
1 assignment•Total 60 minutes
- Self Reflection on Data Understanding and Visualization•60 minutes
In this week, you will delve into essential data processing and manipulation techniques. You will learn how to handle missing values, detect and handle outliers, perform data sampling and dimensionality reduction, apply data scaling and discretization, and explore data cubes and pivot tables.
What's included
9 readings1 assignment1 discussion prompt
9 readings•Total 490 minutes
- Missing Values•60 minutes
- Outliers•60 minutes
- Sampling•60 minutes
- Dimension Elimination•60 minutes
- Scaling•60 minutes
- Discretization•60 minutes
- Data Cube•60 minutes
- Pivot Table•60 minutes
- Congratulations!•10 minutes
1 assignment•Total 60 minutes
- Final Self Reflection on Data Processing and Manipulation•60 minutes
1 discussion prompt•Total 60 minutes
- Data Wrangling Project Show Off!•60 minutes
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