Data Science Master Class
Start Date: February 6th, (3 Months)
Thursdays, Fridays, & Saturdays
Time: 8 pm-10 pm BST
The Data Science Master Class is a comprehensive program designed to equip participants with the theoretical knowledge and practical
skills required to excel in data science. This course covers essential concepts such as data analysis, machine learning, data visualization, and real-world applications across multiple domains, making it ideal for aspiring data scientists and professionals aiming to upskill.
Course Objective
- Understand the fundamentals of data science and its practical applications.
- Master the tools and techniques for data collection, preprocessing, analysis, and visualization.
- Build predictive models using machine learning algorithms.
- Develop hands-on expertise with Python, R, SQL, and data visualization platforms.
- Apply data science solutions to solve real-world problems across various industries.
Learning Outcome
By the end of the course, participants will:
- Demonstrate proficiency in programming for data science using Python and R.
- Perform data manipulation and exploratory data analysis (EDA).
- Build and evaluate machine learning models, including supervised and unsupervised techniques.
- Create compelling data visualizations and dashboards using tools like Tableau and Power BI.
- Apply domain-specific data science techniques to industries such as healthcare, finance, and marketing.
- Complete a capstone project showcasing the ability to solve a real-world data science problem.
Mode of Delivery
Participants completing this course will be prepared for roles such as:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- AI Specialist
- Data Engineer
Target Audience
- Aspiring data scientists and analysts.
- Professionals seeking to transition into data science roles.
- Business professionals looking to leverage data for decision-making.
- Students and researchers interested in data-driven solutions.
Course Module
Introduction to Data Science
Introduction to Data Science
Overview of Data Science and its Applications
Data Science Workflow
Career Paths in Data Science
Tools for Data Science
Data Science Foundation
Descriptive and Inferential Statistics
Probability Distributions
Linear Algebra and Calculus
Data Cleaning and Preprocessing
Exploratory Data Analysis (EDA)
Programming for Data Science
Python and R Basics
Essential Libraries (NumPy, Pandas, ggplot2)
SQL for Data Manipulation
Version Control - Git
Git Basics and Commands
Collaboration on GitHub
Best Practices for Version Control
Data Visualization and Storytelling
Visualization Principles
Python Libraries (Matplotlib, Seaborn, Plotly)
Dashboards with Tableau and Power BI
Machine Learning Fundamentals
Supervised and Unsupervised Learning
Regression Classification and Clustering Models
Model Evaluation Metrics
Advanced Machine Learning
Deep Learning with Neural Networks
Natural Language Processing (NLP)
Reinforcement Learning
Advanced Topics: GANs and Ensemble Methods
Domain Applications & Case Studies
Applications in Healthcare, Finance, Retail, Marketing, Sports, and more.
Capstone Projects
Real-World Problem Identification
Data Analysis, Model Building, and Evaluation
Final Report and Presentation
Duration with Impact:
3 months of intensive learning, giving you time to absorb and apply.
Training + Certification Exam
Course Fee: £1500
Certification Exams
Are you ready to redefine your career?
Enroll now and let the data revolution begin!
Join the Waitlist
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