‘The Data Science & Deep Learning for BusinessTM 20 Case Studies’ course teaches how to use data science and deep learning to address real-world business problems through 20 case studies. The course is designed for individuals who are interested in data science and deep learning and want to apply these skills to solve business problems.
It is a hands-on course and includes end-to-end projects, introducing concepts as they are applied. The course covers the majority of domains that businesses typically ask about and is considered both comprehensive and unique. The courses are usually available for INR 3,499 on Udemy but you can click on the link to get 87% off and get The Data Science & Deep Learning for BusinessTM 20 Case Studies Course for INR 449.
Who all can opt for this course?
- Aspiring data scientists
- Business analysts who want to use their data more effectively
- College graduates with little practical experience
- Management or MBA students who are interested in using data to enhance their business
- Software engineers or developers that want to begin studying data science
- Anyone who wants to increase their employability as a data scientist
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 20.5 hours |
| Rating | 4.2/5 |
| Student Enrollment | 10,440 students |
| Instructor | Rajeev D. Ratan https://www.linkedin.com/in/rajeevd.ratan |
| Topics Covered | Python, Pandas, Statistics & Probability for Data Science, Hypothesis Testing, Machine Learning, Deep Learning |
| Course Level | Intermediate (familiarity with basic Programming concepts and high school maths) |
| Total Student Reviews | 960 |
Learning Outcomes
- Recognize the importance of data to business
- Solve common problems in marketing, sales, customer clustering, banking, real estate, insurance, travel, and more
- Learn Python, Pandas, Matplotlib & Seaborn, SkLearn, NLTK, Prophet, PySpark, MLLib, and more
- Learn machine learning using Decision Trees, Random Forests, K-NNs, Logistic Regressions, SVMs, and Polynomial and Multivariate Linear Regressions
- Unsupervised machine learning using t-SNE, PCA, K-Means, Mean-Shift, DBSCAN, and EM using GMMs
- Build a collaborative and item/content-based product recommendation tool
- Understand t-tests and p values for hypothesis testing and A/B testing
- Summarize reviews, analyze airline tweets for sentiment, and detect spam using natural language processing
- Use AWS to deploy your machine learning models in the cloud
- Advanced Pandas methods, including vectorization and parallel processing
- Distributions, probability theory, statistical theory, and exploratory data analysis
- Predicting insurance costs, Airbnb costs, credit card theft, and the recipients of gifts
- Big Data abilities for data manipulation and machine learning with PySpark
- Exploratory Data Analysis is used to group customers, while K-Means is used to identify client subgroups
- Reinforcement learning is used to create a stock trading bot
- Apply data science and analytics to the retail industry to perform segmentation, analyze trends, identify valuable clients, and more!
- How to use data science in marketing to raise conversion rates, forecast engagement, and increase the customer lifetime value
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Course Introduction – Why Businesses NEED Data Scientists more than ever! (01 hour 05 minutes) | Introduction – Why do this course? Why Apply Data Science to Business? |
| Why Data is the new Oil and what most Businesses are doing wrong | ||
| Defining Business Problems for Analytic Thinking & Data Driven Decision Making | ||
| Analytic Mindset | ||
| 10 Data Science Projects every Business should do! | ||
| Making Sense of Buzz Words, Data Science, Big Data, Machine & Deep Learning | ||
| How Deep Learning is Changing Everything! | ||
| The Roles in the Data World – Analyst, Engineer, Scientist, Statistician, DevOps | ||
| How Data Scientists Approach Problems | ||
| 2. | Course Setup & Pathways – DOWNLOAD RESOURCES HERE (05 minutes) | Course Approach – Different Options for Different Students |
| Setup Google Colab for your iPython Notebooks (Download Course Code + Slides) | ||
| Download Code, Slides and Datasets | ||
| 3. | Python – A Crash Course (51 minutes) | Why use Python for Data Science? |
| Python – Basic Variables | ||
| Python – Variables (Lists and Dictionaries) | ||
| Python – Conditional Statements | ||
| More information on elif | ||
| Python – Loops | ||
| Python – Functions | ||
| Python – Classes | ||
| 4. | Pandas – Beginner to Advanvced (02 hours 23 minutes) | Introduction to Pandas |
| Pandas 1 – Data Series | ||
| Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering | ||
| Pandas 2B – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering | ||
| Pandas 3A – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations | ||
| Pandas 3B – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations | ||
| Pandas 4 – Data Aggregation – GroupBy, Map, Pivot, Aggreate Functions | ||
| Pandas 5 – Feature Engineer, Lambda and Apply | ||
| Pandas 6 – Concatenating, Merging and Joinining | ||
| Pandas 7 – Time Series Data | ||
| Pandas 7 – ADVANCED Operations – Iterows, Vectorization and Numpy | ||
| Pandas 8 – ADVANCED Operations – More Map, Zip and Apply | ||
| Pandas 9 – ADVANCED Operations – Parallel Processing | ||
| Map Visualizations with Plotly – Cloropeths from Scratch – USA and World | ||
| Map Visualizations with Plotly – Heatmaps, Scatter Plots and Lines | ||
| 5. | Statistics & Probability for Data Scientists (01 hour 54 minutes) | Introdution to Statistics |
| Descriptive Statistics – Why Statistical Knowledge is so Important | ||
| Descriptive Statistics 1 – Exploratory Data Analysis (EDA) & Visualizations | ||
| Descriptive Statistics 2 – Exploratory Data Analysis (EDA) & Visualizations | ||
| Sampling, Averages & Variance And How to lie and Mislead with Statistics | ||
| Sampling – Sample Sizes & Confidence Intervals – What Can You Trust? | ||
| Types of Variables – Quantitive and Qualitative | ||
| Frequency Distributions | ||
| Frequency Distributions Shapes | ||
| Analyzing Frequency Distributions – What is the Best Type of WIne? Red or White? | ||
| Mean, Mode and Median – Not as Simple As You’d Think | ||
| Variance, Standard Deviation and Bessel’s Correction | ||
| Covariance & Correlation – Do Amazon & Google know you better than anyone else? | ||
| Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption | ||
| The Normal Distribution & the Central Limit Theorem | ||
| Z-Scores | ||
| 6. | Probability Theory (29 minutes) | Probability – An Introduction |
| Estimating Probability | ||
| Addition Rule | ||
| Permutations & Combinations | ||
| Bayes Theorem | ||
| 7. | Hypothesis Testing (24 minutes) | Hypothesis Testing Introduction |
| Statistical Significance | ||
| Hypothesis Testing – P Value | ||
| Hypothesis Testing – Pearson Correlation | ||
| 8. | Machine Learning – Regressions, Classifications and Assessing Performance (02 hours 21 minutes) | Introduction to Machine Learning |
| How Machine Learning enables Computers to Learn | ||
| What is a Machine Learning Model? | ||
| Types of Machine Learning | ||
| Linear Regression – Introduction to Cost Functions and Gradient Descent | ||
| Linear Regressions in Python from Scratch and using Sklearn | ||
| Polynomial and Multivariate Linear Regression | ||
| Logistic Regression | ||
| Support Vector Machines (SVMs) | ||
| Decision Trees and Random Forests & the Gini Index | ||
| K-Nearest Neighbors (KNN) | ||
| Assessing Performance – Confusion Matrix, Precision and Recall | ||
| Understanding the ROC and AUC Curve | ||
| What Makes a Good Model? Regularization, Overfitting, Generalization & Outliers | ||
| Introduction to Neural Networks | ||
| Types of Deep Learning Algoritms CNNs, RNNs & LSTMs | ||
| 9. | Deep Learning in Detail (01 hour 34 minutes) | Neural Networks Chapter Overview |
| Machine Learning Overview | ||
| Neural Networks Explained | ||
| Forward Propagation | ||
| Activation Functions | ||
| Training Part 1 – Loss Functions | ||
| Training Part 2 – Backpropagation and Gradient Descent | ||
| Backpropagation & Learning Rates – A Worked Example | ||
| Regularization, Overfitting, Generalization and Test Datasets | ||
| Epochs, Iterations and Batch Sizes | ||
| Measuring Performance and the Confusion Matrix | ||
| Review and Best Practices | ||
| 10. | Case Study 1 – Figuring Out Which Employees May Quit – Retention Analysis (40 minutes) | Figuring Out Which Employees May Quit –Understanding the Problem & EDA |
| Data Cleaning and Preparation | ||
| Machine Learning Modeling + Deep Learning | ||
| 11. | Case Study 2 – Figuring Out Which Customers May Leave – Churn Analysis (31 minutes) | Understanding the Problem |
| Exploratory Data Analysis & Visualizations | ||
| Data Preprocessing | ||
| Machine Learning Modeling + Deep Learning | ||
| 12. | Case Study 3 – Who Do We Target For Donations? Finding the highest incomes (28 minutes) | Understanding the Problem |
| Exploratory Data Analysis and Visualizations | ||
| Preparing our Dataset for Machine Learning | ||
| Modeling using Grid Search for finding the best parameters | ||
| 13. | Case Study 4 – Predicting Insurance Premiums (22 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| Data Preparation and Machine Learning Modeling | ||
| 14. | Case Study 5 – Predicting Airbnb Prices (42 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| Machine Learning Modeling | ||
| Using our Model for Value Estimation for New Clients | ||
| 15. | Case Study 6 – Credit Card Fraud Detection (27 minutes) | Understanding our Dataset |
| Exploratory Analysis | ||
| Feature Extraction | ||
| Creating and Validating Our Model | ||
| 16. | Case Study 7 – Analyzing Conversion Rates of Marketing Campaigns (17 minutes) | Exploratory Analysis of Understanding Marketing Conversion Rates |
| 17. | Case Study 8 – Predicting Engagement – What drives ad performance? (17 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| Data Preparation and Machine Learning Modeling | ||
| 18. | Case Study 9 – A/B Testing (Optimizing Ads) (39 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| A/B Test Result Analysis | ||
| A/B Testing a Worked Real Life Example – Designing an A/B Test | ||
| Statistical Power and Significance | ||
| Analysis of A/B Test Resutls | ||
| 19. | Case Study 10 – Product Analytics (Exploratory Data Analysis) (31 minutes) | Problem and Plan of Attack |
| Sales and Revenue Analysis | ||
| Analysis per Country, Repeat Customers and Items | ||
| 20. | Case Study 11 – Determine Your Best Customers & Customer Lifetime Values (13 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| Customer Lifetime Value Modeling | ||
| 21. | Clustering – Unsupervised Learning (52 minutes) | Introdution to Unsupervised Learning |
| K-Means Clustering | ||
| Choosing K – Elbow Method & Silhouette Analysis | ||
| K-Means in Python – Choosing K using the Elbow Method & Silhoutte Analysis | ||
| Agglomerative Hierarchical Clustering | ||
| Mean-Shift Clustering | ||
| DBSCAN (Density-Based Spatial Clustering of Applications with Noise) | ||
| DBSCAN in Python | ||
| Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) | ||
| 22. | Dimensionality Reduction (20 minutes) | Principal Component Analysis |
| t-Distributed Stochastic Neighbor Embedding (t-SNE) | ||
| PCA & t-SNE in Python with Visualization Comparisons | ||
| 23. | Case Study 12 – Customer Clustering (K-means, Hierarchial) (42 minutes) | Data Exploration & Description |
| Simple Exploratory Data Analysis and Visualizations | ||
| Feature Engineering | ||
| K-Means Clustering of Customer Data | ||
| Cluster Analysis | ||
| 24. | Recommendation Systems Theory (29 minutes) | Introduction to Recommendation Engines |
| Before recommending, how do we rate or review Items? Thought Experiment | ||
| User Collaborative Filtering and Item/Content-based Filtering | ||
| The Netflix Prize, Matrix Factorization & Deep Learning as Latent-Factor Methods | ||
| 25. | Case Study 13 – Build a Product Recommendation System (30 minutes) | Dataset Description and Data Cleaning |
| Making a Customer-Item Matrix | ||
| User-User Matrix – Getting Recommended Items for each Customer | ||
| Item-Item Collaborative Filtering – Finding the Most Similar Items | ||
| 26. | 19.1 Case Study 14 – Use LightFM to Build a Movie Recommendation System (11 seconds) | Plan and Approach |
| 27. | Natural Language Processing an Introduction (20 minutes) | Introduction to Natural Language Processing |
| Modeling Language – The Bag of Words Model | ||
| Normalization, Stop Word Removal, Lemmatizing/Stemming | ||
| TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency) | ||
| Word2Vec – Efficient Estimation of Word Representations in Vector Space | ||
| 28. | Case Study 15 – Summarizing Amazon Reviews (10 seconds) | Problem and Plan of Attack |
| 29. | Case Study 16 – Sentiment Analysis of Airline Tweets (11 seconds) | Problem and Plan of Attack |
| 30. | Case Study 17 – Spam Filter (10 seconds) | Problem and Plan of Attack |
| 31. | Case Study 18 – Demand Forecasting with Facebook’s Prophet (12 seconds) | Problem and Plan of Attack |
| 32. | Case Study 19 – Stock Trading using Reinforcement Learning (01 minutes) | Reinforcement Learning an Introduction |
| Using Q-Learning and Reinforcement Learning to Build a Trading Bot | ||
| 33. | Big Data Introduction (07 minutes) | Introduction to Big Data |
| Challenges in Big Data | ||
| Hadoop, MapReduce and Spark | ||
| Introduction to PySpark | ||
| RDDs, Transformations, Actions, Lineage Graphs & Jobs | ||
| Simple Data Cleaning in PySpark | ||
| Machine Learning in PySpark | ||
| 34. | Case Study 20 – Headline Classification in PySpark (11 seconds) | Using PySpark for Headline Classification |
| 35. | Data Science in Production – Deploying on the Cloud (AWS) (13 minutes) | Install and Run Flask |
| Running Your Computer Vision Web App on Flask Locally | ||
| Running Your Computer Vision API | ||
| Setting Up An AWS Account | ||
| Setting Up Your AWS EC2 Instance & Installing Keras, TensorFlow, OpenCV & Flask | ||
| Changing your EC2 Security Group | ||
| Using FileZilla to transfer files to your EC2 Instance | ||
| Running your CV Web App on EC2 | ||
| Running your CV API on EC2 | ||
| 36. | BONUS – Customer Life Time Values using the BG/NBD and the Gamma-Gamma Model (03 minutes) | Customer Lifetime Value (CLV) Theory |
| Buy-til-you-die (BTYD) models | ||
| Customer Lifetime Value Modeling using lifetimes | ||
| 37. | BONUS – Price Optimization of Airline Tickets (01 minutes) | Price Optimization of Airline Tickets |
| 38. | BONUS – Convolution Neural Networks (54 minutes) | Convolutional Neural Networks Chapter Overview |
| Convolutional Neural Networks Introduction | ||
| Convolutions & Image Features | ||
| Depth, Stride and Padding | ||
| ReLU | ||
| Pooling | ||
| The Fully Connected Layer | ||
| Training CNNs | ||
| Design Your Own CNN | ||
| Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs – Promo | ||
| Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs – Introduction |
Resources Required
- Basic Programming concepts
- High-school level Maths
- Stable internet connection
Featured Review
Amith reddy (5/5): Voice levels and the way the program was designed are top-notch learned a lot though I am a non-IT guy
Pros
- Roger Lloyds (5/5): One of the best data science courses I’ve ever come across!
- Diederik Espen (5/5): The instructor seems to have a knack for knowing what things can trip up a beginner so he explains those concepts brilliantly.
- Arjun Achuthan (5/5): The instructor has great clarity in what he is going to teach.
- Ben Goisima (5/5): Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! Also, I love how he relates it to business applications.
Cons
- Mamtakakkar (2/5): Someone who has experience with case studies would know that it was poorly designed and sufficient explanation wasn’t provided.
- Ashad (1/5): At least you need to write some of the code so that we understand.
- Vikas Bhartiya (1/5): I do a lot of research before buying the course but this time I have trapped.
- Karan Mehta (1/5): The instructor is not clear in his approach and does not explain things fully For example at one instance he says “cross tab is little difficult to understand” and he did not even explain it afterward.
About the Author
The instructor of this course is Rajeev D. Ratan who is a Data Scientist, Computer Vision Expert & Electrical Engineer. With a 4.4 instructor rating and 8,908 reviews on Udemy, he offers 7 courses and has taught 60,145 students so far.
- Rajeev is a computer vision engineer and data scientist.
- He graduated from the University of Edinburgh with a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence, where he developed in-depth expertise in machine learning, computer vision, and intelligent robotics.
- He even was a member of a team that won a robotics competition at the University of Edinburgh.
- His study on employing data-driven methodologies for probabilistic stochastic modeling for public transportation has been published.
- In order to use deep learning in education, he founded my own computer vision startup.
- Since then, he has worked with two other companies in the computer vision space as well as a large international company in data science.
- He previously spent eight years working for two of the biggest telecom companies in the Caribbean, where he developed skills in technical staff management and the deployment of challenging telecom projects.
Comparison Table
| Parameters | Data Science & Deep Learning for Business™ 20 Case Studies | Machine Learning Practical: 6 Real-World Applications | Machine Learning Practical Workout | 8 Real-World Projects |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 20.5 hours | 8.5 hours | 14 hours |
| Rating | 4.2/5 | 4.5 /5 | 4.4 /5 |
| Student Enrollments | 10,440 | 19,610 | 15,038 |
| Instructors | Rajeev D. Ratan | Dr. Ryan Ahmed, Ph.D., MBA | Dr. Ryan Ahmed, Ph.D., MBA |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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