Data Science for Business Professionals

Preface

Data Science Overview

• Evolution of data analytics
• Define data science
• Domain Knowledge
• Mathematical and Scientific Techniques
• Tools and Technology
• Data science analysis types
• Data science job roles
• ML model development process
• Data Visualizations
• Result Communication
• Responsible and Ethical AI
• Career in Data Science
• Conclusion

Mathematics Essentials

• Introduction to linear algebra
• Scalar, vectors, matrices, and tensors
• The determinant
• Eigenvalues and Eigenvectors
• Eigenvalue decomposition and Singular Value Decomposition (SVD)
• Principal Component Analysis
• Multivariate Calculus
• Differential Calculus
• Multiple variables
• Definite vs. Indefinite Integrals
• Conclusion

Statistics Essentials

• Introduction to probability and statistics
• Descriptive statistics
• Conditional probability
• Random variables
• Inferential statistics
• Conclusion

Exploratory Data Analysis

• What is EDA?
• Understanding data
• Methods of EDA
• Key concepts of EDA
• Conclusion

Data Preprocessing

• Introduction to data preprocessing
• Methods in data preprocessing
• Conclusion

Feature Engineering

• Introduction to feature engineering
• Feature engineering techniques
• Applying feature engineering
• Conclusion

Machine Learning Algorithms

• Introduction to machine learning
• Top 10 Algorithms of Machine Learning Explained
• Building a machine learning model
• Conclusion

Productionizing Machine Learning Models

• Types of ML production system
• Introduction to REST APIs
• Flask framework
• Ml Model User Interface
• Conclusion

Data Flows in Enterprises

• Introducing data pipeline
• Designing data pipeline
• ETL vs. ELT
• Scheduling jobs
• Messaging Queue
• Passing Arguments to Data Pipeline
• Conclusion

Introduction to Databases

• Modern databases and terminology
• Relational database or SQL database
• Connect Python to Postgres
• Document-oriented database or No-SQL
• Graph databases
• Filesystem as storage
• Conclusion

Introduction to Big Data

• Introducing Big Data
• Introducing Hadoop
• Setting-up a Hadoop Cluster
• Word-count MapReduce Program
• Conclusion

DevOps for Data Science

• Introduction to DevOps
• Agile methodology, CI/CD, and DevOps
• DevOps for data science
• Conclusion

Introduction to Cloud Computing

• Introducing cloud computing
• Types of Cloud Services
• Types of cloud infrastructure
• Data science and cloud computing
• Market growth of cloud
• Conclusion

Deploy Model to Cloud

• Register for GCP free account
• GCP console
• Create VM and its properties
• Connecting and Uploading Code to VM
• Executing Python Model On Cloud
• Access the Model Via Browser
• Scaling the resources in Cloud
• Conclusion

Introduction to Business Intelligence

• What is business intelligence?
• Business intelligence analysis
• Business intelligence process
• Business Intelligence Trends
• Gartner 2019 Magic Quadrant
• Conclusion

Data Visulazation Tools

• Introduction to data visualization
• Data visualization tools
• Introduction to Microsoft Power BI
• Conclusion

Industry Use Case 1 - Form Assist

• Abstract
• Introduction
• Related Work
• Proposed work
• Data augmentation
• Optimization
• Feature extraction
• Image thresholding
• Classifier
• Results
• Conclusion
• Acknowledgment

Industry Use Case 2 - People Reporter

• Abstract
• Introduction
• Event detection
• Work architecture
• Results
• Nipah Virus Outbreak in Kerala
• Conclusion
• Acknowledgment

Do It Your Self Challenges

• DIY challenge 1 - Analyzing the pathological slide for blood analysis
• DIY challenge 2 - IoT based weather monitoring system
• DIY challenge 3 - Facial image-based BMI calcula... disease; this challenge comes from this domain.
• DIY challenge 4 - Chatbot assistant for Tourism in North East
• DIY challenge 5 - Assaying and grading of fruits for e-procurement
• Conclusion