• views: 78
• date: 28 June 2025
• posted by: 0nelove

Gain advanced data analytics skills using Python. Apply analytical and machine learning techniques to extract useful information from datasets
What you'll learn
Build foundational analytic and machine learning techniques through data mining concepts, representing real-world data, and extraction patterns.
Explore unstructured data using clustering, dimensionality reduction, and topic modeling to uncover hidden patterns and improve predictive analysis.
Analyze network structures using NetworkX, apply network generation models, simulate diffusion processes, and detect community structures.
Extract meaningful information from text data by applying machine learning techniques for named entity recognition across diverse domains.
Skills you'll gain
Unstructured Data
Graph Theory
ChatGPT
Data Science
Unsupervised Learning
Algorithms
Dimensionality Reduction
Big Data
Applied Machine Learning
Network Model
Text Mining
Deep Learning
In our increasingly interconnected world, we're collecting more raw data than ever. In "More Applied Data Science with Python," you'll learn how to extract and analyze complex data sets using Python. Practice using real-world data sets, like health data and comment sections, to develop visual representations and identify key patterns amongst populations. You'll also learn to manage missing and messy data using advanced manipulation methods. Throughout this course series, you'll build a foundation for advanced analytics and machine learning with the help of Scikit-Learn and NLP libraries by applying methods for data mining, clustering, topic modeling, network modeling, and information extraction. Upon completing the series, you'll have gained advanced data analysis skills that will help you gain insights into the datasets you're exploring.
Learners should have intermediate Python programming skills before enrolling in the Specialization. It is encouraged that you complete Applied Data Science with Python prior to beginning this Specialization.
Applied Learning Project
Perform data analysis, manipulation, and extraction on real-world data collected from restaurants, genetics, recipes, emojis, contact lists, addresses, music, epidemics, community structures, and more. Apply machine learning and advanced analytics techniques to derive practical findings and make representations that explain data patterns and connections.




   
   
   

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