Watch Pandas Data Cleaning and Modeling with Python LiveLessons

Pandas Data Cleaning and Modeling with Python LiveLessons is an online video course offered by Pearson, designed for aspiring data analysts and data scientists who want to master the art of handling large datasets using Python programming language. This course introduces viewers to pandas, a powerful open source data analysis and manipulation library built on top of Python, and showcases its many features and capabilities, such as data cleaning, data modeling, data visualization, and statistical analysis, through a series of practical examples and real-world projects.

The course is divided into eight lessons, each of which explores a key topic or technique related to pandas data cleaning and modeling. These lessons are presented by expert instructors who bring years of experience in data analysis, machine learning, and software development to the table, making the course engaging, informative, and easy-to-follow.

The first lesson, Introduction to Pandas, introduces viewers to the basics of pandas, including how to install and import it, create and manipulate data objects, and perform basic statistical operations. The lesson also provides an overview of the different data types that pandas supports, such as series, dataframes, and panel objects, and explains how to use them effectively for data analysis.

The second lesson, Data Cleaning with Pandas, covers various techniques and tools for cleaning and transforming messy data into a usable format using pandas. This includes handling missing data, dealing with outliers, merging and reshaping datasets, and filtering and sorting data based on specific criteria. Viewers also learn how to use regular expressions and other advanced techniques to clean and transform text data.

The third lesson, Data Modeling with Pandas, focuses on using pandas to build predictive models that can help solve real-world problems. This includes techniques such as linear regression, logistic regression, decision trees, and clustering. Viewers learn how to use pandas to load and preprocess data, split it into training and testing sets, train and validate models, and evaluate model performance using metrics such as accuracy, precision, and recall.

The fourth lesson, Time Series Analysis with Pandas, takes a deep dive into the world of time series data, and shows viewers how to use pandas to analyze and model time series data. This includes techniques such as time series visualization, time series decomposition, autocorrelation, and forecasting. Viewers also learn how to use pandas to perform temporal aggregations and resampling, and how to leverage Python's datetime module to work with date and time data.

The fifth lesson, Advanced Data Analysis with Pandas, covers a range of advanced topics and techniques for data analysis using pandas. This includes working with large datasets, using pandas with SQL databases, handling categorical data, and performing group operations on data. The lesson also showcases the power of pandas for visualizing data using third-party libraries such as matplotlib and seaborn.

The sixth lesson, Machine Learning with Scikit-Learn, introduces viewers to Scikit-Learn, a popular open source library for machine learning in Python, and shows how to use it in conjunction with pandas for building predictive models. This includes techniques such as feature scaling, cross-validation, hyperparameter tuning, and model selection. Viewers learn how to use pandas to preprocess and transform data, and how to use Scikit-Learn to train and validate models using different algorithms such as k-nearest neighbors, support vector machines, and random forests.

The seventh lesson, Deep Learning with TensorFlow, takes a step further and shows viewers how to use TensorFlow, a powerful open source library for deep learning and neural networks in Python, to solve complex data problems. Viewers learn how to use pandas to prepare data for use in deep learning models, and how to use TensorFlow to build and train different types of neural networks such as feedforward, convolutional, and recurrent networks. The lesson also covers techniques such as transfer learning and image recognition.

The eighth and final lesson, Putting It All Together, brings everything together by showing how to apply pandas and other Python libraries to a real-world data problem from start to finish. The lesson covers the entire data analysis process, from data collection and cleaning to feature engineering, model training, and deployment. Viewers also learn how to leverage cloud computing resources such as Amazon Web Services (AWS) for scaling up data analysis and model training.

Overall, Pandas Data Cleaning and Modeling with Python LiveLessons is a comprehensive and hands-on course that equips viewers with the skills and knowledge needed to become proficient in handling large datasets using pandas and other Python libraries. Whether you are looking to sharpen your data analysis skills, transition into a data-centric role, or simply want to explore the world of data science, this course provides you with a solid foundation for success.