Data Science for Business | 6 Real-world Case Studies
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- Curriculum
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Are you looking to land a top-paying job in Data Science?
Or are you a seasoned AI practitioner who want to take your career to the next level?
Or are you an aspiring entrepreneur who wants to maximize business revenue with Data Science and Artificial Intelligence?
If the answer is yes to any of these questions, then this course is for you!
Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, transportation and technology.
In business, Data Science is applied to optimize business processes, maximize revenue and reduce cost. The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.
In this course, we will assume that you are an experienced data scientist who have been recently as a data science consultant to several clients. You have been tasked to apply data science techniques to the following 6 departments: (1) Human Resources, (2) Marketing, (3) Sales, (4) Operations, (5) Public Relations, (6) Production/Maintenance. Your will be provided with datasets from all these departments and you will be asked to achieve the following tasks:
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Task #1 @Human Resources Department: Develop an AI model to Reduce hiring and training costs of employees by predicting which employees might leave the company.
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Task #2 @Marketing Department: Optimize marketing strategy by performing customer segmentation
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Task #3 @Sales Department: Develop time series forecasting models to predict future product prices.
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Task #4 @Operations Department: Develop Deep Learning model to automate and optimize the disease detection processes at a hospital.
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Task #5 @Public Relations Department: Develop Natural Language Processing Models to analyze customer reviews on social media and identify customers sentiment.
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Task #6 @Production/Maintenance Departments: Develop defect detection, classification and localization models.
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Introduction to Case Study and Key Learning Outcomes -
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Task #1: Problem Statement and Business Case
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Task #2: Import Libraries and Datasets
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Task #3: Explore Dataset - Part 1
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Task #3: Explore Dataset - Part 2
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Task #3: Explore Dataset - Part 3
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Task #3: Explore Dataset - Part 4
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Task #4: Perform Data Cleaning
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Task #5: Understand intuition of Random Forest, Logistic Regression, and ANNs
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Task #6: Understand Classification KPIs
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Task #7: Build and Train Logistic Regression Classifier
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Task #8: Build and Train Random Forest Classifier Model
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Task #9: Build and Train Artificial Neural Network Classifier Model
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Introduction to Case Study and Key Learning Outcomes
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Task #1: Understand Problem Statement and Business Case
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Task #2: Import Libraries and Datasets
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Task #3: Perform Data Visualization
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Task #4: Understand the Theory and Intuition behind K-Mean Algorithm
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Task #5: Obtain Optimal Number of Clusters "K"
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Task #6: Apply K-Means Clustering to Perform Market Segmentation
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Task #7: Understand the Intuition Behind Principal Component Analysis (PCA)
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Task #8: Understand the Intuition Behind Autoencoders
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Task #9: Build and Train Autoencoder - Part #1
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Build and Train Autoencoder - Part #2
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Introduction to Case Study and Key Learning Outcomes
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Task #1: Understand the Problem Statement and Business Case
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Task #2: Import Datasets - Part #1
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Task #2: Import Datasets - Part #2
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Task #3: Explore Data - Part #1
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Task #3: Explore Data - Part #2
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Task #3: Explore Data - Part #3
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Task #3: Explore Data - Part #4
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Task #4: Understand Facebook Prophet intuition
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Task #5: Train The Model - Part #1
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Task #6: Train The Model - Part #2
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Introduction to Case Study and Key Learning Outcomes
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Task #1: Understand the Business Case and Problem Statement
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Task #2: Load and Explore Dataset
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Task #3: Visualize Datasets
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Task #4: Understand Intuition Behind Convolutional Neural Networks (CNNs)
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Task #5: Understand Intuition Behind Transfer Learning
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Task #6: Load Model with Pretrained Weights
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Task #7: Build and Train ResNet
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Task #8: Evaluate Trained Model Performance
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Introduction to Case Study and Key Learning Outcomes
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Task #1: Understand Problem Statement and Business Case
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Task #2: Import Libraries and Datasets
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Task #3: Explore Dataset - Part #1
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Task #3: Explore Dataset - Part #2
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Task #4: Perform Data Cleaning
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Task #5: Remove Punctuation
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Task #6: Remove Stopwords
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Task #7: Perform Tokenization/Count Vectorization
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Task #8: Perform Text Cleaning pipeline
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Task #9: Naive Bayes Intuition
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Task #10: Train a Naive Bayes Classifier
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Task #11: Evaluate Trained Naive Bayes Classifier
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Task #12: Train and Evaluate a Logistic Regression Classifier
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Introduction and Welcome Message
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Task #1 - Understand the Problem Statement & Business Case
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Task #2 - Import Libraries and Datasets
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Task #3 - Visualize and Explore Dataset
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Task #4 - Understand the Intuition behind ResNet, CNNs, and Transfer Learning
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Task #5 - Build & Train ResNet Classifiers
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Task #6 - Assess Trained ResNet Model Performance
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Task #7 - Understand the Intuition behind ResUnet Segmentation Models
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Task #8 - Build & Train a ResUnet Segmentation Model
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Task #9 - Assess Trained ResUnet Model
