Data Science & Analytics
Get to know who we are and also meet with some of our team.
Online, flexible schedule
Our programs are online and designed to accommodate busy schedules, and even the occasional missed week. The programs are flexible, yet with our minimum 15 hours/week requirement, they are intensive enough to prepare students well for their new career and get them job-ready in a relatively short amount of time. We offer extra help and private sessions with academic instructors for students who need a bit more support.
This course provides a comprehensive overview of data science and analytics, from foundational concepts to advanced analytical techniques. You will learn how to collect, clean, and analyze data, as well as how to visualize and present findings effectively. By the end of the course, you will be prepared to tackle real-world data challenges.
What you’ll study
The course covers a wide array of topics, including data collection methods, statistical analysis, machine learning, and data visualization. You'll gain practical experience with popular data science tools and libraries.
Entry requirements
- A working laptop or desktop computer with a minimum of 4GB RAM
- A stable internet connection with a minimum speed of 5 Mbps
- An up-to-date web browser (Chrome, Firefox, or Safari)
- Access to video conferencing software (e.g., Slack or Zoom)
How you'll be taught
The course will feature lectures, hands-on coding sessions, and projects to develop practical data science skills.
Fees and funding
To enroll in the course, a one-time payment of £2500 is required. Alternatively, you can opt for an installment payment plan, consisting of three payments: 50% of the total cost upfront, followed by two equal payments of 25% each. Please contact us to discuss the installment plan details.
Careers and employability
Graduates can pursue careers as Data Scientists, Data Analysts, Business Intelligence Analysts, or Data Engineers in various sectors.
Syllabus
- Introduction to Data Science and Analytics
- Data Collection and Data Sources
- Data Cleaning and Preprocessing Techniques
- Descriptive Statistics and Data Summarization
- Exploratory Data Analysis (EDA)
- Data Visualization Principles and Techniques
- Introduction to Machine Learning Concepts
- Supervised vs. Unsupervised Learning
- Statistical Inference and Hypothesis Testing
- Introduction to Python for Data Science (NumPy, Pandas)
- Machine Learning Algorithms (Regression, Classification, Clustering)
- Model Evaluation and Selection Techniques
- Time Series Analysis and Forecasting
- Big Data Technologies (Hadoop, Spark)
- Data Ethics and Responsible Analytics
- Capstone Project: Analyze a Real-World Dataset
How to apply
- You must provide accurate and complete information during the registration process.
- You must complete the payment process to successfully enroll in the course.
- Follow the instructions received in the email to access the course materials or live session.
Where our graduates work
What our graduates say
Don’t just take our word for it, hear from our students themselves
Lucas Turner
@lucasturner
Completing the program was life-changing! The hands-on projects prepared me for real-world scenarios, and I landed a job within weeks.
Hannah Scott
@hannahscott
A fantastic experience with knowledgeable instructors and comprehensive modules. A bit fast-paced, but highly rewarding.
Benjamin Green
@benjamingreen
This course exceeded my expectations! The support and career guidance helped me secure my dream role in tech.
Lily Carter
@lilycarter
From coding basics to advanced topics, this course covered it all. I feel confident and prepared for my career in development.
Ethan Perez
@ethanperez
Great curriculum with real-world projects. Some areas could use more depth, but overall an amazing foundation.
Zoe Anderson
@zoeanderson
The mentorship was incredible! My mentor was with me every step of the way, and I felt well-prepared for job applications.
Mason Walker
@masonwalker
I can’t recommend this enough! The blend of theory and practice made learning easy and enjoyable. Best investment I made.
Ella Rivera
@ellarivera
Solid course with excellent resources. The projects helped me build my portfolio, which played a big role in landing a job.
Jack Thompson
@jackthompson
This course made me industry-ready. I secured a DevOps role shortly after graduation thanks to the in-depth labs and exercises.
Frequently Asked Questions
Here are some of the most common questions we get asked. If you have a question that isn't answered here, feel free to reach out to us.
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