DS-BUS-PROF.AW1

Data Science for Business Professionals

Our Data Science course is the GPS you need to navigate the data highway. No wrong turns, just progress.

20 Interactive Lessons and 124 topics mapped to the official exam objectives

Intermediate Self-paced · 1 year access

20Interactive Lessons

124Topics

34Flashcards

34Glossary of terms

01 / Skills you'll get

What you will be able to do

Prepare to dive into the multidisciplinary world of data science with our data science for business course.

Through clear, bite-sized lessons and hands-on labs, you’ll explore key concepts like statistics, machine learning (ML), data pipelines, and cloud computing. Accomplish your learning objectives while building and deploying data-driven solutions.

This data science for professionals course covers everything from exploratory data analysis and feature engineering to DevOps, business intelligence, and ethical AI.

So, gear up because data-driven job opportunities await you!

Master key concepts in mathematics and statistics essential for data analysis.
Explore and apply ML algorithms to solve real-world problems.
Build and implement data pipelines for efficient data processing.
Gain proficiency in data preprocessing and feature engineering techniques.
Create impactful data visualizations using tools like Tableau and Power BI.
Understand cloud computing fundamentals and deploy models on platforms like GCP and AWS.
Develop skills in DevOps practices, including CI/CD, Docker, Jenkins, and Git.
Design and manage modern databases, including SQL, NoSQL, and graph databases.
Implement big data solutions using Hadoop and MapReduce.
Applying business intelligence strategies to drive data-informed decision-making.
Practice responsible and ethical AI principles in data science projects.
Gain hands-on experience through industry use cases and DIY challenges.
Enhance problem-solving aptitude by tackling real-world data science problems.
Communicate data insights to stakeholders.

Course Highlights

20 Structured Lessons Comprehensive coverage of core course objectives
1 Year Full Access Self-paced learning accessible anytime on all devices

02 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

20 Interactive Lessons · 124 topics

01 Preface

02 Data Science Overview 13 topics

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

03 Mathematics Essentials 11 topics

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

04 Statistics Essentials 6 topics

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

05 Exploratory Data Analysis 5 topics

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

06 Data Preprocessing 3 topics

Introduction to data preprocessing
Methods in data preprocessing
Conclusion

07 Feature Engineering 4 topics

Introduction to feature engineering
Feature engineering techniques
Applying feature engineering
Conclusion

08 Machine Learning Algorithms 4 topics

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

09 Productionizing Machine Learning Models 5 topics

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

10 Data Flows in Enterprises 7 topics

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

11 Introduction to Databases 7 topics

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

12 Introduction to Big Data 5 topics

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

13 DevOps for Data Science 4 topics

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

14 Introduction to Cloud Computing 6 topics

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

15 Deploy Model to Cloud 8 topics

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

16 Introduction to Business Intelligence 6 topics

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

17 Data Visulazation Tools 4 topics

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

18 Industry Use Case 1 - Form Assist 12 topics

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

19 Industry Use Case 2 - People Reporter 8 topics

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

20 Do It Your Self Challenges 6 topics

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

03 / FAQs

Questions before you start

Who is this course for?

This business analytics with data science course is ideal for business professionals, managers, and aspiring data scientists who want to upskill or reskill in data science to make data-driven decisions and solve real-world problems.

Will I learn about machine learning and AI?

Yes, this data science course for business professionals covers key ML algorithms, model development, and ethical AI practices to help you build and deploy ML solutions.

What are the technical requirements for taking this course?

You’ll need a computer with internet access. Specific tools and software used in the course will be introduced and explained in our virtual hands-on labs as you progress.

How does this course help with career advancement?

By mastering in-demand data science skills, you’ll be equipped for roles like data analyst, business intelligence professional, or data scientist, opening doors to new career opportunities.

From Data to Decision, Learn It All

Data science + business smarts = unstoppable you. Join the course and transform your future!

1 year of full access
Certificate of completion