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 Lecciones interactivas y 124 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
20Lecciones interactivas
124Topics
34Tarjetas didácticas
34Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
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 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
-
1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
20 Lecciones interactivas · 124 topics01 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 / Preguntas frecuentes
Preguntas antes de empezar
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.
Keep going
You might also like
From Data to Decision, Learn It All
Data science + business smarts = unstoppable you. Join the course and transform your future!
- 1 año de acceso completo
- Certificado de finalización
No se requiere tarjeta de crédito