Data Science, Analytics & AI for Business & the Real World™ course will teach students Data Science and Statistics to solve business Problems. The course includes 35+ Practical Case Studies covering so many typical business problems faced by Data Scientists in the real world. It concentrates on mastering all the fundamental theory and programming abilities required to be a Data Scientist.
The course syllabus covers all of the key areas of data science concepts. Sampling, distributions, the normal distribution, descriptive statistics, correlation and covariance, probability significance testing, and hypothesis testing are all covered in detail in the chapter on statistics for data science. The course is usually available at INR 2,299 on Udemy but you can click now to get 78% off and get Data Science, Analytics & AI for Business & the Real World™ for INR 449.
Who all can opt for this course?
- Novice data scientists
- Business analysts who want to use their data more effectively
- Graduates from colleges with little practical experience
- Management or MBA students who are interested in using data to improve their firm
- Engineers or software developers who want to start understanding data science
- Candidates who wants to increase their employability as a data scientist
- Someone interested in using data to solve problems in the real world
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 399 ( |
| Duration | 30 Hours |
| Rating | 4.3/5 |
| Student Enrollment | 3,728 students |
| Instructor | Rajeev D. Ratan https://www.linkedin.com/in/rajeevd.ratan |
| Topics Covered | Data Analytics, Data Wrangling, Machine Learning Algorithms, Statistics, and Probability |
| Course Level | Beginner |
| Total Student Reviews | 371 |
Learning Outcomes
- Ensure data quality, Pandas will develop into a Data Analytics and Data Wrangling whiz
- Effective machine learning algorithms
- Probability and statistics
- A/B testing and hypothesis testing
- Design stunning graphs, charts, and other visuals that use data to convey a story
- Recognize typical business issues and how to use data science to solve them
- Dashboards for data using Google Data Studio
- 36 Case Studies and real world Business Issues
- Collaborative filtering, LiteFM, and Deep Learning techniques for recommendation engines
- NLTK and deep learning are used for natural language processing (NLP)
- Time Series Forecasting Using Prophet on Facebook
- Marketing with Data Science (Ad engagemnt & Performance)
- Clustering and analytics for consumers
- Analysis of social media sentiment
- How to apply deep learning (Keras, Tensorflow) in a number of real-world case studies
- Machine learning model deployment using Heroku and Flask (CI/CD)
- Examine sports, healthcare, restaurants, and the economy Analytics
- Using PySpark for Big Data Analysis and Machine Learning
- How Data Science can be used in Retail (Market Basket Analysis, Sales Analytics and Demand forecasting)
- At Google Colab, students can utilise already-configured Jupyter Notebooks (no hassle or setup, extremely simple to get started)
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Introduction (01 hour 01 minutes) | The Data Science Hype |
| About Our Case Studies | ||
| Why Data is the new Oil | ||
| Defining Business Problems for Analytic Thinking & Data Driven Decision making | ||
| 10 Data Science Projects every Business should do! | ||
| How Deep Learning is Changing Everything | ||
| The Career paths of a Data Scientist | ||
| The Data Science Approach to Problems | ||
| 2. | Setup (Google Colab) & Download Code (06 minutes) | Downloading and Running Your Code |
| Colab Setup | ||
| 3. | Introduction to Python (51 minutes) | Why use Python for Data Science? |
| Python Introduction – Part 1 – Variables | ||
| Python – Variables (Lists and Dictionaries) | ||
| More information on elif | ||
| Python – Conditional Statements | ||
| Python – Loops | ||
| Python – Functions | ||
| Python – Classes | ||
| 4. | Pandas (02 hours 23 minutes) | Pandas Introduction |
| Pandas 1 – Data Series | ||
| Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells | ||
| 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 8 – ADVANCED Operations – Iterows, Vectorization and Numpy | ||
| Pandas 9 – ADVANCED Operations – Map, Filter, Apply | ||
| Pandas 10 – 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 & Visualizations (01 hour 54 minutes) | Introduction 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 (22 minutes) | Introduction to Probability |
| Estimating Probability | ||
| Addition Rule | ||
| Bayes Theorem | ||
| 7. | Hypothesis Testing (24 minutes) | Introduction to Hypothesis Testing |
| Statistical Significance | ||
| Hypothesis Testing – P Value | ||
| Hypothesis Testing – Pearson Correlation | ||
| 8. | A/B Testing – A Worked Example (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 | ||
| 9. | Data Dashboards – Google Data Studio (01 hour 46 minutes) | Intro to Google Data Studio |
| Opening Google Data Studio and Uploading Data | ||
| Your First Dashboard Part 1 | ||
| Your First Dashboard Part 2 | ||
| Creating New Fields | ||
| Adding Filters to Tables | ||
| Scorecard KPI Visalizations | ||
| Scorecards with Time Comparison | ||
| Bar Charts (Horizontal, Vertical & Stacked) | ||
| Line Charts | ||
| Pie Charts, Donut Charts and Tree Maps | ||
| Time Series and Comparitive Time Series Plots | ||
| Scatter Plots | ||
| Geographic Plots | ||
| Bullet and Line Area Plots | ||
| Sharing and Final Conclusions | ||
| Our Executive Sales Dashboard | ||
| 10. | Machine Learning (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 | ||
| 11. | Deep Learning (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 | ||
| 12. | Unsupervised Learning – Clustering (52 minutes) | Introduction to Unsupervised Learning |
| K-Means Clustering | ||
| Choosing K | ||
| Kmeans – Elbow and Silhoutte Method | ||
| 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) | ||
| 13. | Dimensionality Reduction (17 minutes) | Principal Component Analysis |
| t-Distributed Stochastic Neighbor Embedding (t-SNE) | ||
| PCA & t-SNE in Python with Visualization Comparisons | ||
| 14. | Recommendation Systems (29 minutes) | Introduction to Recommendation Engines |
| Before recommending, how do we rate or review Items? | ||
| User Collaborative Filtering and Item/Content-based Filtering | ||
| The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me | ||
| 15. | Natural Language Processing (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 | ||
| 16. | Big Data (28 minutes) | Introduction to Big Data |
| Challenges in Big Data | ||
| Hadoop, MapReduce and Spark | ||
| Introduction to PySpark | ||
| RDDs, Transformations, Actions, Lineage Graphs & Jobs | ||
| 17. | Predicting the US 2020 Election (55 minutes) | Understanding Polling Data |
| Cleaning & Exploring our Dataset | ||
| Data Wrangling our Dataset | ||
| Understanding the US Electoral System | ||
| Visualizing our Polling Data | ||
| Statistical Analysis of Polling Data | ||
| Polling Simulations | ||
| Polling Simulation Result Analysis | ||
| Visualizing our results on a US Map | ||
| 18. | Predicting Diabetes Cases (18 minutes) | Understanding and Preparing Our Healthcare Data |
| First Attempt – Trying a Naive Model | ||
| Trying Different Models and Comparing the Results | ||
| 19. | Market Basket Analysis (22 minutes) | Understanding our Dataset |
| Data Preparation | ||
| Visualizing Our Frequent Sets | ||
| 20. | Predicting the World Cup Winner (Soccer/Football) (31 minutes) | Understanding and Preparing Our Soccer Datasets |
| Understanding and Preparing Our Soccer Datasets | ||
| Predicting Game Outcomes with our Model | ||
| Simulating the World Cup Outcome with Our Model | ||
| 21. | Covid-19 Data Analysis and Flourish Bar Chart Race Visualization (01 hour 03 minutes) | Understanding Our Covid-19 Data |
| Analysis of the most Recent Data | ||
| World Visualizations | ||
| Analyzing Confirmed Cases in each Country | ||
| Mapping Covid-19 Cases | ||
| Animating our Maps | ||
| Comparing Countries and Continents | ||
| Flourish Bar Chart Race – 1 | ||
| Flourish Bar Chart Race – 2 | ||
| 22. | Analyzing Olmypic Winners (24 minutes) | Understanding our Olympic Datasets |
| Getting The Medals Per Country | ||
| Analyzing the Winter Olympic Data and Viewing Medals Won Over Time | ||
| 23. | Is Home Advantage Real in Soccer and Basketball? (19 minutes) | Understanding Our Dataset and EDA |
| Goal Difference Ratios Home versus Away | ||
| How Home Advantage Has Evolved Over. Time | ||
| 24. | IPL Cricket Data Analysis (20 minutes) | Loading and Understanding our Cricket Datasets |
| Man of Match and Stadium Analysis | ||
| Do Toss Winners Win More? And Team vs Team Comparisons | ||
| 25. | Streaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) – Movie Analysi (28 minutes) | Understanding our Dataset |
| EDA and Visualizations | ||
| Best Movies Per Genre Platform Comparisons | ||
| 26. | Micro Brewery and Pub Data Analysis (13 minutes) | EDA, Visualizations and Map |
| 27. | Pizza Resturant Data Analysis (18 minutes) | EDA and Visualizations |
| Analysis Per State | ||
| Pizza Maps | ||
| 28. | Supply Chain Data Analysis (17 minutes) | Understanding our Dataset |
| Visualizations and EDA | ||
| More Visualizations | ||
| 29. | Indian Election Result Analysis (28 minutes) | Intro |
| Visualizations of Election Results | ||
| Visualizing Gender Turnout | ||
| 30. | Africa Economic Crisis Data Analysis (12 minutes) | Economic Dataset Understanding |
| Visualizations and Correlations | ||
| 31. | Predicting Which Employees May Quit (40 minutes) | Figuring Out Which Employees May Quit –Understanding the Problem & EDA |
| Data Cleaning and Preparation | ||
| Machine Learning Modeling + Deep Learning | ||
| 32. | Figuring Out Which Customers May Leave (31 minutes) | Understanding the Problem |
| Exploratory Data Analysis & Visualizations | ||
| Data Preprocessing | ||
| Machine Learning Modeling + Deep Learning | ||
| 33. | Who to Target For Donations? (28 minutes) | Understanding the Problem |
| Exploratory Data Analysis & Visualizations | ||
| Preparing our Dataset for Machine Learning | ||
| Modeling using Grid Search for finding the best parameters | ||
| 34. | Predicting Insurance Premiums (22 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| Data Preparation and Machine Learning Modeling | ||
| 35. | 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 | ||
| 36. | Detecting Credit Card Fraud (27 minutes) | Understanding our Dataset |
| Exploratory Analysis | ||
| Feature Extraction | ||
| Creating and Validating Our Model | ||
| 37. | Analyzing Conversion Rates in Marketing Campaigns (17 minutes) | Exploratory Analysis of Understanding Marketing Conversion Rates |
| 38. | Predicting Advertising Engagement (17 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| Data Preparation and Machine Learning Modeling | ||
| 39. | Product Sales Analysis (31 minutes) | Problem and Plan of Attack |
| Sales and Revenue Analysis | ||
| Analysis per Country, Repeat Customers and Items | ||
| 40. | Determing Your Most Valuable Customers (13 minutes) | Understanding the Problem + Exploratory Data Analysis and Visualizations |
| Customer Lifetime Value Modeling | ||
| 41. | Customer Clustering (K-means, Hierarchial) – Train Passenger (42 minutes) | Data Exploration & Description |
| Simple Exploratory Data Analysis and Visualizations | ||
| Feature Engineering | ||
| K-Means Clustering of Customer Data | ||
| Cluster Analysis | ||
| 42. | Build a Product Recommendation System (30 minutes) | Dataset Description and Data Cleaning |
| Making a Customer-Item Matrix | ||
| User-User Matrix – Getting Recommended Items | ||
| Item-Item Collaborative Filtering – Finding the Most Similar Items | ||
| 43. | Movie Recommendation System – LiteFM (09 seconds) | Intro |
| 44. | Deep Learning Recommendation System (20 minutes) | Understanding Our Wikipedia Movie Dataset |
| Creating Our Dataset | ||
| Deep Learning Embeddings and Training | ||
| Getting Recommendations based on Movie Similarity | ||
| 45. | Predicting Brent Oil Prices (23 minutes) | Understanding our Dataset and it’s Time Series Nature |
| Creating our Prediction Model | ||
| Making Future Predictions | ||
| 46. | Stock Trading using Reinforcement Learning (01 minutes) | Introduction to Reinforcement Learning |
| Using Q-Learning and Reinforcement Learning to Build a Trading Bot | ||
| 47. | Sales/Demand Forecasting (11 seconds) | Problem and Plan of Attack |
| 48. | Detecting Sentiment in Tweets (23 minutes) | Understanding our Dataset and Word Clouds |
| Visualizations and Feature Extraction | ||
| Training our Model | ||
| 49. | Spam or Ham Detection (14 minutes) | Loading and Understanding our Spam/Ham Dataset |
| Training our Spam Detector | ||
| 50. | Explore Data with PySpark and Titanic Surival Prediction (25 minutes) | Exploratory Analysis of our Titantic Dataset |
| Transformation Operations | ||
| Machine Learning with PySpark | ||
| 51. | Newspaper Headline Classification using PySpark (08 minutes) | Loading and Understanding our Dataset |
| Building our Model with PySpark | ||
| 52. | Deployment into Production (34 minutes) | Introduction to Production Deployment Systems |
| Creating the Model | ||
| Introduction to Flask | ||
| About our WebApp | ||
| Deploying our WebApp on Heroku |
Resources Required
- No need to be an expert in programming or maths, high school algebra would be sufficient
- It is beginner-friendly because this course covers all aspects of programming
Featured Review
Armando G. (5/5) : I love this class an simple to understand the process of learning this tremendous tool. I’m really happy because I bought this course even long time ago however is time to start learning now!!!
Pros
- Remon Augustos de Campos (5/5) : I had to give a presnetaiton on on Covid-19 data analysis and everyone was so extremely impressed by what I presented which was variations to what I learned on this course! Amazing Value, you won’t regret buying this
- Avery Willis (5/5) : Loving this course, not done yet but looking forward to finishing it!
- John Earlington (5/5) : I just completed more of the course and the content and teaching is really good! Loving how thorough and well taught things are.
Cons
- Carlos Alberto Sanchez R. (1/5) : I bought this course only for the projects, I see a litte bit from the first part so I can’t make a full coment from the theory part.
- Alireza F. (1/5) : The teacher, unfortunately, burps a lot such that it makes it very unpleasant to listen and watch the course.
- Carlos A. M. (1/5) : Instructor explains topics very fast, covers lots of content without substance.
- Thng Kok W. (1/5) : Terrible. Scroll up and down. Can’t even follow what you are are teaching.
About the Author
The instructor of this course is Rajeev D. Ratan who is a Data Scientist, Computer Vision Expert & Electrical Engineer. With 4.4 Instructor Rating and 9,166 Reviews on Udemy, he/she offers 7 Courses and has taught 61,650 Students so far.
- Instructor Rajeev is a computer vision engineer and data scientist
- Instructor 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
- Instructor was a member of a team that won a robotics competition at the University of Edinburgh
- Instructor studied on employing data-driven methodologies for probabilistic stochastic modelling for public transportation has been published
- In order to use deep learning in education, Instructor founded his own computer vision startup
- Since then, Instructor have worked with two other companies in the computer vision space as well as a large international company in data science
- Instructor 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, Analytics & AI for Business & the Real World™ | Python & Machine Learning for Financial Analysis | Data Science & Deep Learning for Business™ 20 Case Studies |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 30.5 hours | 23 hours | 21 hours |
| Rating | 4.3 /5 | 4.5 /5 | 4.1 /5 |
| Student Enrollments | 3,728 | 97,081 | 10,540 |
| Instructors | Rajeev D. Ratan | Dr. Ryan Ahmed, Ph.D., MBA | Rajeev D. Ratan |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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