Data Science Masterclass
This 20-session course covers key data science concepts, tools, and best practices, with each session lasting 2 hours. The course will walk you through the process of data collection, cleaning, analysis, modeling, and visualization, using real-world industry examples and a hands-on demo project.
About Course
The Data Science Masterclass: Real-World Industry Projects is an in-depth 20-session course designed to provide you with the skills and knowledge required to excel as a data scientist in various industries. Each session lasts 2 hours, and the course covers key data science concepts, tools, and best practices. The curriculum includes a hands-on demo project that simulates real-world industry scenarios, enabling you to apply the skills learned throughout the course.
In the final two sessions, you will work on a hands-on demo project that encompasses the data science concepts, tools, and techniques covered throughout the course. The project will provide you with practical experience in working with real-world data, from collecting and preprocessing the data to building, evaluating, and deploying machine learning models. This hands-on experience will help you apply the skills and knowledge gained to real-world industry projects, ensuring that you have a comprehensive understanding of the data science process.
No, prior experience in data science is not required. The course starts with the fundamentals of data science, making it accessible to beginners. However, some basic knowledge of programming, statistics, or data analysis would be helpful.
This course is suitable for aspiring data scientists, professionals seeking a career change, business analysts, software developers, and IT professionals who want to enhance their skills in data science.
The hands-on demo project is designed to give you practical experience in working with real-world data, from collecting and preprocessing the data to building, evaluating, and deploying machine learning models. The project helps you apply the concepts and techniques you have learned throughout the course, ensuring you gain the skills needed to work on real-world industry projects.
- While there are no strict prerequisites, some basic knowledge of programming, statistics, or data analysis is recommended to get the most out of the course.
This course covers a wide range of data science topics, which may be helpful in preparing for various data science certifications. However, the course is not specifically designed to prepare you for a particular certification exam. If you’re interested in pursuing a certification, it’s a good idea to identify the specific exam you’d like to take and review its requirements, then use this course as a foundation and supplement it with additional study materials and practice exams as needed.
Course Content
Session 1: Introduction to Data Science
Understanding the role of a data scientist
Key concepts in data science
Overview of the data science process
Session 2: Python for Data Science
Session 3: Data Collection and Preprocessing
Session 4: Exploratory Data Analysis (EDA)
Session 5: Statistical Analysis
Session 6: Machine Learning Fundamentals
Session 7: Supervised Learning: Regression
Session 8: Supervised Learning: Classification
Session 9: Unsupervised Learning: Clustering
Session 10: Unsupervised Learning: Dimensionality Reduction
Session 11: Time Series Analysis and Forecasting
Session 12: Natural Language Processing (NLP)
Session 13: Deep Learning Basics
Session 14: Recommendation Systems
Session 15: Big Data and Data Science
Session 16: Data Visualization Tools
Session 17: Model Deployment and Production
Session 18: Ethical Considerations in Data Science
Session 19: Demo Project – Part 1
Session 20: Demo Project – Part 2
