Data Science & Deep Learning for Business™ 20 Case Studies

• Understand the value of data for business
• Solve common business problems in Marketing, Sales, Customer Clustering, Banking, Real Estate, Insurance, Travel and more!
• Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and more!
• 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!
• How to apply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time Value

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."

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"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 andDeep 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:

1. How Data Science and Solve Many Common Business Problems

2. The Modern Tools of a Data Scientist - Python, Pandas, Scikit-learn, Seaborn, Matplotlib &Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).

3. Statistics for Data Science in Detail - Sampling, Distributions,Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing and Hypothesis Testing.

4. Machine Learning Theory - Linear Regressions, Logistic Regressions, Decision Trees, RandomForests, KNN, SVMs, Model Assessment, Outlier Detection, ROC&AUC and Regularization

5. Deep Learning Theory and Tools - TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs &LSTMs)

6. Solving problems using Predictive Modeling, Classification, and Deep Learning

7. Data Science in Marketing - Modeling Engagement Rates and perform A/BTesting

8. Data Science in Retail - Customer Segmentation, Lifetime Value, and Customer/Product Analytics

9. Unsupervised Learning - K-Means Clustering, PCA,t-SNE, Agglomerative Hierarchical,Mean Shift, DBSCAN and E-M GMM Clustering

10. Recommendation Systems - Collaborative Filtering and Content-based filtering +Learn to use LiteFM

11. Natural Language Processing - Bag ofWords, Lemmatizing/Stemming, TF-IDFVectorizer, and Word2Vec

12. Big 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)

13. Deployment to the Cloud using AWS to build a Machine Learning API

Our fun and engaging 20 Case Studies include:

Six (6) Predictive Modeling & Classifiers Case Studies:

1. Figuring Out Which Employees May Quit (Retention Analysis)

2. Figuring Out Which Customers May Leave (Churn Analysis)

3. Who do we target for Donations?

4. Predicting Insurance Premiums

5. Predicting Airbnb Prices

6. Detecting Credit Card Fraud

Four (4) Data Science in Marketing Case Studies:

1. Analyzing Conversion Rates of Marketing Campaigns

2. Predicting Engagement - What drives ad performance?

3. A/B Testing (Optimizing Ads)

4. Who are Your Best Customers? & Customer Lifetime Values (CLV)

Four (4) Retail Data Science Case Studies:

1. Product Analytics (Exploratory Data Analysis Techniques

2. Clustering Customer Data from Travel Agency

3. Product Recommendation Systems - Ecommerce Store Items

4. Movie Recommendation System using LiteFM

Two (2) Time-Series Forecasting Case Studies:

1. Sales Forecasting for a Store

2. Stock Trading using Re-Enforcement Learning

Three (3) Natural Langauge Processing (NLP) Case Studies:

1. Summarizing Reviews

2. Detecting Sentiment in text

3. Spam Filters

One (1) PySpark Big Data Case Studies:

1. 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.

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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!