Data Science & Deep Learning for Business™ 20 Case Studies

Use Python for Data Analysis, Data Science in Marketing & Retail, Recommendations, Forecasts, Customer Clustering & NLP

Created by Rajeev Ratan | 16 hours on-demand video course

Welcome to the course on Data Science & Deep Learning for Business™ 20 Case Studies! This course takes on Machine Learning and Statistical theory and teaches you to use it in solving 20 real-world Business 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. As a result, “Data Scientist has become the top job in the US for the last 4 years running!” according to Harvard Business Review & Glassdoor.

What you’ll learn

• Understand the value of data for businesses
• Learn to use Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and more!
• Apply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time Value
• Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests
• Unsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNE
• Build a Product Recommendation Tool using collaborative & item/content based
• Hypothesis Testing and A/B Testing – Understand t-tests and p values
• Natural Langauge Processing – Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam Detection
• To use Google Colab’s iPython notebooks for fast, relaible cloud based data science work
• Deploy your Machine Learning Models on the cloud using AWS
• Advanced Pandas techniques from Vectorizing to Parallel Processsng
• Statistical Theory, Probability Theory, Distributions, Exploratory Data Analysis
• Predicting Employee Churn, Insurance Premiums, Airbnb prices, credit card fraud and who to target for donations
• Big Data skills using PySpark for Data Manipulation and Machine Learning
• Cluster customers based on Exploratory Data Analysis, then using K-Means to detect customer segments
• Build a Stock Trading Bot using re-inforement learning
• Apply Data Science & Analytics to Retail, performing segementation, analyzing trends, determining valuable customers and more!

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