Data Science for Business Professionals

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

(DS-BUS-PROF.AW1) / ISBN : 978-1-64459-663-0
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About This Course

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! 

Skills You’ll Get

  • 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. 

1

Preface

2

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
3

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
4

Statistics Essentials

  • Introduction to probability and statistics
  • Descriptive statistics
  • Conditional probability
  • Random variables
  • Inferential statistics
  • Conclusion
5

Exploratory Data Analysis

  • What is EDA?
  • Understanding data
  • Methods of EDA
  • Key concepts of EDA
  • Conclusion
6

Data Preprocessing

  • Introduction to data preprocessing
  • Methods in data preprocessing
  • Conclusion
7

Feature Engineering

  • Introduction to feature engineering
  • Feature engineering techniques
  • Applying feature engineering
  • Conclusion
8

Machine Learning Algorithms

  • Introduction to machine learning
  • Top 10 Algorithms of Machine Learning Explained
  • Building a machine learning model
  • Conclusion
9

Productionizing Machine Learning Models

  • Types of ML production system
  • Introduction to REST APIs
  • Flask framework
  • Ml Model User Interface
  • Conclusion
10

Data Flows in Enterprises

  • Introducing data pipeline
  • Designing data pipeline
  • ETL vs. ELT
  • Scheduling jobs
  • Messaging Queue
  • Passing Arguments to Data Pipeline
  • Conclusion
11

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
12

Introduction to Big Data

  • Introducing Big Data
  • Introducing Hadoop
  • Setting-up a Hadoop Cluster
  • Word-count MapReduce Program
  • Conclusion
13

DevOps for Data Science

  • Introduction to DevOps
  • Agile methodology, CI/CD, and DevOps
  • DevOps for data science
  • Conclusion
14

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
15

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
16

Introduction to Business Intelligence

  • What is business intelligence?
  • Business intelligence analysis
  • Business intelligence process
  • Business Intelligence Trends
  • Gartner 2019 Magic Quadrant
  • Conclusion
17

Data Visulazation Tools

  • Introduction to data visualization
  • Data visualization tools
  • Introduction to Microsoft Power BI
  • Conclusion
18

Industry Use Case 1 - Form Assist

  • Abstract
  • Introduction
  • Related Work
  • Proposed work
  • Data augmentation
  • Optimization
  • Feature extraction
  • Image thresholding
  • Classifier
  • Results
  • Conclusion
  • Acknowledgment
19

Industry Use Case 2 - People Reporter

  • Abstract
  • Introduction
  • Event detection
  • Work architecture
  • Results
  • Nipah Virus Outbreak in Kerala
  • Conclusion
  • Acknowledgment
20

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

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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. 

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.

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. 

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!

$239.99

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