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via Udemy |
Go to Course: https://www.udemy.com/course/data-science-analytics-ai-for-business-the-real-world/
Certainly! Here's a comprehensive review and recommendation for the course "Data Science, Analytics & AI for Business & the Real World™ 2020" on Coursera: --- **Course Review and Recommendation: Data Science, Analytics & AI for Business & the Real World™ 2020** If you're venturing into the world of Data Science and seeking a course that bridges the gap between theoretical knowledge and practical application, then "Data Science, Analytics & AI for Business & the Real World™ 2020" on Coursera is an excellent choice. This course offers a hands-on, immersive experience that is especially valuable for beginners who are overwhelmed by the mystique surrounding data science, as well as for intermediate learners eager to solidify their understanding through real-world projects. **What Makes This Course Stand Out?** 1. **Practical Focus with Over 35 Case Studies:** One of the most compelling aspects of this course is its emphasis on real-world applications. Covering a broad spectrum of industries such as marketing, retail, healthcare, finance, and sports, the case studies give learners the chance to solve authentic problems—from predicting election outcomes to analyzing customer churn and building recommendation systems. 2. **Comprehensive Content Coverage:** The course thoroughly covers all essential elements of Data Science, including data manipulation, visualization, statistical analysis, machine learning, deep learning, natural language processing, big data with PySpark, and deployment. This ensures a well-rounded learning experience, equipping you with the skills needed to handle end-to-end data science projects. 3. **Modern Tools and Technologies:** Learners gain practical experience with popular tools and libraries like Python, Pandas, Scikit-learn, Keras, TensorFlow, PySpark, and visualization platforms such as Seaborn, Matplotlib, and Plotly. The inclusion of cloud deployment using Heroku adds valuable insight into how to operationalize models. 4. **Structured and Engaging Learning Path:** The curriculum is logically organized, starting from foundational concepts like statistics and visualization, progressing through machine learning and deep learning, and culminating with big data and deployment. This structured approach is ideal for building confidence step by step. 5. **In-Demand Skills:** The course aims to prepare learners for the growing demand for data scientists in diverse industries. Harvard Business Review and Glassdoor consistently rank Data Scientist as one of the top jobs in the US, making this course a strategic investment for career advancement. **Who Should Enroll?** - Beginners who want a comprehensive, practical introduction to Data Science. - Professionals from non-technical backgrounds aiming to gain data-driven insights. - Aspiring data scientists seeking hands-on experience with real-world case studies. - Business analysts and managers interested in leveraging data science in decision-making. **Final Verdict:** I highly recommend "Data Science, Analytics & AI for Business & the Real World™ 2020" to anyone serious about learning Data Science with a focus on practical application. Its extensive case studies and comprehensive curriculum make it one of the most valuable courses for building confidence and competence in this dynamic field. Whether you're just starting or looking to enhance your skills, this course will equip you with the tools and experience to tackle real-world business challenges effectively. **Enroll today and start transforming data into actionable insights!** --- If you'd like, I can help you craft a shorter summary or personalized review based on your specific background or goals.
Data Science, Analytics & AI for Business & the Real World™ 2020This is a practical course, the course I wish I had when I first started learning Data Science.It focuses on understanding all the basic theory and programming skills required as a Data Scientist, but the best part is that it features 35+ Practical Case Studies covering so many common business problems faced by Data Scientists in the real world. Right now, even in spite of the Covid-19 economic contraction, traditional businesses are hiring Data Scientists in droves! And they expect new hires to have the ability to apply Data Science solutions to solve their problems. Data Scientists who can do this will prove to be one of the most valuable assets in business over the next few decades!"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 Complete 2020 Data Science Learning path includes:Using Data Science to Solve Common Business Problems The Modern Tools of a Data Scientist - Python, Pandas, Scikit-learn, NumPy, Keras, prophet, statsmod, scipy and more!Statistics for Data Science in Detail - Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing, and Hypothesis Testing.Visualization Theory for Data Science and Analytics using Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).Dashboard Design using Google Data StudioMachine 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 Analysis and Statistical Case Studies - Solve and analyze real-world problems and datasets. Data 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 + Deep Learning Recommendation SystemsNatural 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 Heroku to build a Machine Learning APIOur fun and engaging Case Studies include:Sixteen (16) Statistical and Data Analysis Case Studies:Predicting the US 2020 Election using multiple Polling DatasetsPredicting Diabetes Cases from Health DataMarket Basket Analysis using the Apriori AlgorithmPredicting the Football/Soccer World CupCovid Analysis and Creating Amazing Flourish Visualisations (Barchart Race)Analyzing Olympic DataIs Home Advantage Real in Soccer or Basketball?IPL Cricket Data AnalysisStreaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) - Movie AnalysisPizza Restaurant Analysis - Most Popular Pizzas across the USMicro Brewery and Pub AnalysisSupply Chain AnalysisIndian Election AnalysisAfrica Economic Crisis AnalysisSix (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 LearningBrent Oil Price ForecastingThree (3) Natural Langauge Processing (NLP) Case Studies:Summarizing ReviewsDetecting Sentiment in textSpam DetectionOne (1) PySpark Big Data Case Studies:News Headline ClassificationOne (1) Deployment Project:Deploying your Machine Learning Model to the Cloud using Flask & Heroku