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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-deep-learning-for-business-20-case-studies/
课程评论：没有评论
课程名称：商业数据科学与深度学习：20个案例研究 课程概述：欢迎参加《商业数据科学与深度学习：20个案例研究》课程！本课程教您如何利用数据科学和深度学习解决现实商业问题，并将这些技术应用于20个真实案例研究中。传统企业正在大量招聘数据科学家，掌握这些技术应用于解决实际问题的能力，将在未来十年中成为极为宝贵的技能。 学生反馈：许多学员给予本课程高度评价，纷纷表示其内容比他们在伦敦大学学院（UCL）学习的商业分析硕士课程更为易懂和系统。课程采用端到端的实践项目执行方式，适合具有一定基础的学习者，能够让他们在真实项目中感受到思考过程。 课程关键点： - 数据科学在解决常见商业问题中的应用。 - 现代数据科学工具使用：Python、Pandas、Scikit-learn、Seaborn、Matplotlib及Plotly进行数据处理与可视化。 - 详细的统计学内容：抽样、分布、描述统计、相关性与协方差、概率显著性测试及假设检验。 - 机器学习理论：线性回归、逻辑回归、决策树、随机森林、KNN、SVM、模型评估、异常值检测、ROC与AUC及正则化。 - 深度学习理论与工具：TensorFlow 2.0和Keras（神经网络、CNN、RNN与LSTM）。 - 使用预测建模、分类和深度学习解决问题。 - 市场营销中的数据科学：客户参与率建模与A/B测试。 - 零售中的数据科学：客户细分、客户生命周期价值与产品分析。 - 无监督学习与推荐系统。 - 自然语言处理技术与大数据应用。 20个案例研究包括： - 预测建模：员工离职预测、客户流失预测、捐赠目标识别等。 - 市场营销案例：营销活动转化率分析、广告表现驱动因素、客户终身价值分析等。 - 零售数据科学案例：产品分析、客户数据聚类、电子商务推荐系统等。 - 时间序列预测：销售预测与股票交易。 - 自然语言处理案例：评论摘要、情绪检测与垃圾邮件过滤。 - 大数据案例：新闻标题分类。 总结：随着大数据趋势的加速推进，企业对数据科学家的需求日益增加。课程旨在帮助学员从零基础成长为能够解决实际商业挑战的数据科学家，抓住21世纪这一热门职业的机遇。
Welcome to the course on Data Science & Deep Learning for Business™ 20 Case Studies!This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade!What student reviews of this course are saying, "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! 6 stars out of 5!""It is pretty different in format, from others. The appraoch taken here is an end-to-end hands-on project execution, while introducing the concepts. A learner with some prior knowledge will definitely feel at home and get to witness the thought process that happens, while executing a real-time project. The case studies cover most of the domains, that are frequently asked by companies. So it's pretty good and unique, from what i have seen so far. Overall Great learning and great content."-"Data Scientist has become the top job in the US for the last 4 years running!" according to Harvard Business Review & Glassdoor.However, Data Science has a difficult learning curve - How does one even get started in this industry awash with mystique, confusion, impossible-looking mathematics, and code? Even if you get your feet wet, applying your newfound Data Science knowledge to a real-world problem is even more confusing.This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of Data Science and Deep Learning to real-world business problems.This course has a comprehensive syllabus that tackles all the major components of Data Science knowledge. Our Learning path includes:How Data Science and Solve Many Common Business ProblemsThe Modern Tools of a Data Scientist - Python, Pandas, Scikit-learn, Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).Statistics for Data Science in Detail - Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing and Hypothesis Testing.Machine Learning Theory - Linear Regressions, Logistic Regressions, Decision Trees, Random Forests, KNN, SVMs, Model Assessment, Outlier Detection, ROC & AUC and RegularizationDeep Learning Theory and Tools - TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs & LSTMs)Solving problems using Predictive Modeling, Classification, and Deep LearningData Science in Marketing - Modeling Engagement Rates and perform A/B TestingData Science in Retail - Customer Segmentation, Lifetime Value, and Customer/Product AnalyticsUnsupervised Learning - K-Means Clustering, PCA, t-SNE, Agglomerative Hierarchical, Mean Shift, DBSCAN and E-M GMM ClusteringRecommendation Systems - Collaborative Filtering and Content-based filtering + Learn to use LiteFM Natural Language Processing - Bag of Words, Lemmatizing/Stemming, TF-IDF Vectorizer, and Word2VecBig Data with PySpark - Challenges in Big Data, Hadoop, MapReduce, Spark, PySpark, RDD, Transformations, Actions, Lineage Graphs & Jobs, Data Cleaning and Manipulation, Machine Learning in PySpark (MLLib)Deployment to the Cloud using AWS to build a Machine Learning APIOur fun and engaging 20 Case Studies include:Six (6) Predictive Modeling & Classifiers Case Studies:Figuring Out Which Employees May Quit (Retention Analysis)Figuring Out Which Customers May Leave (Churn Analysis)Who do we target for Donations?Predicting Insurance PremiumsPredicting Airbnb PricesDetecting Credit Card FraudFour (4) Data Science in Marketing Case Studies:Analyzing Conversion Rates of Marketing CampaignsPredicting Engagement - What drives ad performance?A/B Testing (Optimizing Ads)Who are Your Best Customers? & Customer Lifetime Values (CLV)Four (4) Retail Data Science Case Studies:Product Analytics (Exploratory Data Analysis TechniquesClustering Customer Data from Travel AgencyProduct Recommendation Systems - Ecommerce Store ItemsMovie Recommendation System using LiteFMTwo (2) Time-Series Forecasting Case Studies:Sales Forecasting for a StoreStock Trading using Re-Enforcement LearningThree (3) Natural Langauge Processing (NLP) Case Studies:Summarizing ReviewsDetecting Sentiment in textSpam FiltersOne (1) PySpark Big Data Case Studies:News Headline Classification"Big data is at the foundation of all the megatrends that are happening."Businesses NEED Data Scientists more than ever. Those who ignore this trend will be left behind by their competition. In fact, the majority of new Data Science jobs won't be created by traditional tech companies (Google, Facebook, Microsoft, Amazon, etc.) they're being created by your traditional non-tech businesses. The big retailers, banks, marketing companies, government institutions, insurances, real estate and more. "Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win."With Data Scientist salaries creeping up higher and higher, this course seeks to take you from a beginner and turn you into a Data Scientist capable of solving challenging real-world problems.-Data Scientist is the buzz of the 21st century for good reason! The tech revolution is just starting and Data Science is at the forefront. Get a head start applying these techniques to all types of Businesses by taking this course!
