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 HighlightsDetails
Registration LinkApply Now!
PriceINR 399 (INR 2,299) 78 % off
Duration30 Hours
Student Enrollment3,728 students
InstructorRajeev D. Ratan
Topics CoveredData Analytics, Data Wrangling, Machine Learning Algorithms, Statistics, and Probability
Course LevelBeginner
Total Student Reviews371

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
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!!!


  • 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.


  • 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

ParametersData Science, Analytics & AI for Business & the Real World™Python & Machine Learning for Financial AnalysisData Science & Deep Learning for Business™ 20 Case Studies
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration30.5 hours23 hours21 hours
Rating4.3 /54.5 /54.1 /5
Student Enrollments3,72897,08110,540
InstructorsRajeev D. RatanDr. Ryan Ahmed, Ph.D., MBARajeev D. Ratan
Register HereApply Now!Apply Now!Apply Now!

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