Data Science & Deep Learning for Business™ 20 Case Studies

Data Science & Deep Learning for Business™ 20 Case Studies
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 2.64 GB
Genre: eLearning Video | Duration: 187 Lessons (20h 32m) | Language: English

Learn Data Science with Python & solve problems in Marketing, Retail, Recommendations, Customer Clustering & Forecasting.

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!

Requirements

Familiar with basic programming concepts
Highschool level math knowledge
Broadband Internet connection

Description

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.

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 Problems

The 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 Regularization

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

Solving problems using Predictive Modeling, Classification, and Deep Learning

Data Science in Marketing - Modeling Engagement Rates and perform A/B Testing

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

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

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

Natural Language Processing - Bag of Words, Lemmatizing/Stemming, TF-IDF Vectorizer, and Word2Vec

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)

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:

Figuring Out Which Employees May Quit (Retention Analysis

Figuring Out Which Customers May Leave (Churn Analysis)

Who do we target for Donations?

Predicting Insurance Premiums

Predicting Airbnb Prices

Detecting Credit Card Fraud

Four (4) Data Science in Marketing Case Studies:

Analyzing Conversion Rates of Marketing Campaigns

Predicting 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 Techniques

Clustering Customer Data from Travel Agency

Product Recommendation Systems - Ecommerce Store Items

Movie Recommendation System using LiteFM

Two (2) Time-Series Forecasting Case Studies:

Sales Forecasting for a Store

Stock Trading using Re-Enforcement Learning

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

Summarizing Reviews

Detecting Sentiment in text

Spam Filters

One (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.

Who this course is for:

Begineers to Data Science
Business Analysts who wish to do more with their data
College graduates who lack real world experience
Business oriented persons (Management or MBAs) who'd like to use data to enhance their business
Software Developers or Engineers who'd like to start learning Data Science
Anyone looking to become more employable as a Data Scientist