Machine LearningPython ProgrammingPredictive AnalyticsData ProcessingData Visualization (DataViz)Python LibrariesData Pre-ProcessingWeb ScrapingBig Data ProductsRecommender Systems
Python Data Products for Predictive Analytics 특화 과정
Build Predictive Systems with Accuracy. Collect, model, and deploy data-driven systems using Python and machine learning.
제공자:
Python Data Products for Predictive Analytics 특화 과정캘리포니아 샌디에고 대학교배울 내용
Discover how to transform data and make it suitable for data-driven predictive tasks
Understand how to compute basic statistics using real-world datasets of consumer activities, like product reviews and more
Use Python to create interactive data visualizations to make meaningful predictions and build simple demo systems
Perform simple regressions and classifications on datasets using machine learning libraries
귀하가 습득할 기술
이 전문 분야 정보
최근 조회 5,382회
Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego.
This Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills.
Python Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets.
응용 학습 프로젝트
You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인 강좌
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 일정
유연한 마감을 설정하고 유지 관리합니다.
중급 단계
관련 경험이 어느 정도 필요합니다.
완료하는 데 약 5개월 필요
매주 2시간 권장
영어
자막: 영어, 프랑스어, 포르투갈어 (브라질), 러시아어, 스페인어
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인 강좌
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유동적 일정
유연한 마감을 설정하고 유지 관리합니다.
중급 단계
관련 경험이 어느 정도 필요합니다.
완료하는 데 약 5개월 필요
매주 2시간 권장
영어
자막: 영어, 프랑스어, 포르투갈어 (브라질), 러시아어, 스페인어
이 전문 분야에는 4개의 강좌가 있습니다.
Basic Data Processing and Visualization
134개의 평가
This is the first course in the four-course specialization Python Data Products for Predictive Analytics, introducing the basics of reading and manipulating datasets in Python. In this course, you will learn what a data product is and go through several Python libraries to perform data retrieval, processing, and visualization.
Design Thinking and Predictive Analytics for Data Products
42개의 평가
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models. At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization.
Meaningful Predictive Modeling
33개의 평가
This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
Deploying Machine Learning Models
41개의 평가
In this course we will learn about Recommender Systems (which we will study for the Capstone project), and also look at deployment issues for data products. By the end of this course, you should be able to implement a working recommender system (e.g. to predict ratings, or generate lists of related products), and you should understand the tools and techniques required to deploy such a working system on real-world, large-scale datasets.
제공자:
캘리포니아 샌디에고 대학교
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
자주 묻는 질문
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
Can I just enroll in a single course?
하나의 강좌에만 등록할 수 있나요?
Can I take the course for free?
해당 강좌를 무료로 수강할 수 있나요?
이 강좌는 100% 온라인으로 진행되나요? 직접 참석해야 하는 수업이 있나요?
전문 분야를 완료하는 데 얼마나 걸리나요?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
전문 분야를 완료하면 대학 학점을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
