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Python Data Analysis
Learn how to analyze data with PythonPreview the first lesson free — get full access to all 42 lessons.
Beginner Provider : Briana Brownell 7 chapters · 42 Lessons · 3h 31m in English
Course Description
This course will give you the knowledge and skills needed to effectively analyze and model data. First you will learn the basics of data analysis and the different types of data that can be analyzed. You will learn about best practices for working with data, including cleaning and preprocessing data, storing data, and protecting data privacy. Additionally, you will learn about the Python programming language and how to use Jupyter notebooks for data analysis, as well as common libraries used in data analysis with Python.
You will then learn different aspects of data analysis, including how business intelligence can use data analysis to support decision-making. The course covers cleaning and preparing data for statistical analysis, as well as techniques for transforming data, identifying correlations and causality, and comparing groups of data using crosstabulations and statistical tests.
Next you will learn about statistical measures, data distributions, and visualization techniques to effectively summarize data. You will learn best practices for aggregating data, handling missing values, summarizing and visualizing categorical data, time-series data, and multivariate data. Additionally, you will learn how to create effective visualizations to communicate data insights and understand common challenges that can arise when summarizing data. The course then focuses on using machine learning methods to model data. You will learn about linear and logistic regression, decision trees, ensemble methods, dimension reduction, clustering, neural networks and deep learning, and association rules and anomaly detection. You will learn how to implement these models in Python.
Finally, you will learn how to evaluate the performance of machine learning models and how to identify and prevent overfitting and underfitting. Additionally, the course covers the deployment and maintenance of machine learning models, detecting and addressing drift, interpreting and communicating results, and ethical considerations in the use of machine learning models.
What You'll Learn
- Understand fundamental data analysis concepts and the different types of data that can be analyzed
- Apply best practices for cleaning, preprocessing, storing, and protecting the privacy of data
- Use Python and Jupyter notebooks with common libraries such as Pandas DataFrames for data analysis
- Transform data and identify correlations and causality, and compare groups using crosstabulations and statistical tests
- Summarize and visualize categorical, time-series, and multivariate data using statistical measures and visualization techniques
- Model data with machine learning methods including regression, decision trees, ensemble methods, clustering, and neural networks, and evaluate their performance
Key Takeaways
- The course covers the basics of data analysis, including the different types of data and best practices for cleaning, preprocessing, storing, and protecting data.
- It teaches the Python programming language and the use of Jupyter notebooks and common libraries for data analysis.
- It addresses transforming data, identifying correlations and causality, comparing groups with crosstabulations and statistical tests, and summarizing data with statistical measures, distributions, and visualizations.
- It explains machine learning methods such as linear and logistic regression, decision trees, ensemble methods, dimension reduction, clustering, neural networks and deep learning, and association rules and anomaly detection, implemented in Python.
- It covers evaluating model performance, preventing overfitting and underfitting, deploying and maintaining models, addressing drift, and ethical considerations in machine learning.
Frequently Asked Questions
What will I learn in this course?
You will learn the knowledge and skills needed to effectively analyze and model data, including the basics of data analysis, working with data in Python and Jupyter notebooks, summarizing and visualizing data, and using machine learning methods to model data and evaluate their performance.
What tools and programming language does this course use?
The course teaches the Python programming language, the use of Jupyter notebooks for data analysis, and common Python libraries used in data analysis, including Pandas DataFrames.
What machine learning methods are covered?
The course covers linear and logistic regression, decision trees, ensemble methods, dimension reduction, clustering, neural networks and deep learning, and association rules and anomaly detection, along with how to implement these models in Python.
Does the course cover data privacy and ethics?
Yes. It covers best practices for storing data and protecting data privacy, as well as ethical considerations in the use of machine learning models.
What skills will I gain from this course?
You will gain skills in data analysis, data mining, data preprocessing, data science, data visualization, and quantitative data analysis.
Professional Certifications and Continuing Education Units (CEUs)
International Institute of Business Analysis (IIBA®)
Continuing Development Units (CDUs): 3.75
Society for Human Resource Management (SHRM®)
Professional Development Credits (PDCs): 3.75
KnowledgeCity is approved by SHRM as a Recertification General Provider to offer SHRM-CP or SHRM-SCP professional development credits (PDCs). By taking the courses approved by SHRM, KnowledgeCity can award SHRM Professional Development Credits (PDCs) for HR knowledge and competency programs related to the SHRM Body of Applied Skills and Knowledge™ (the SHRM BASK™).
