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Generiss Academy

Data Management, Data Plus

Data Science with Machine Learning

Through a combination of live online lectures, practical labs, case studies, and capstone projects, students will gain expertise in data ... Show more
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Generiss Academy
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Unlock the power of data and build predictive intelligence with our premier, Virtual Instructor-Led (VIL) course: Data Science with Machine Learning. This intensive program bridges the gap between raw data analysis and predictive AI engineering.

Led in real-time by industry-vetted data scientists, you will transform from a data observer into a strategic builder. Through live, interactive lectures, collaborative breakout rooms, and hands-on production code environments, you will master data wrangling, statistical analysis, and the deployment of advanced machine learning models. By the end of this course, you will have constructed an enterprise-grade portfolio of predictive models, giving you the practical experience needed to solve complex business challenges.

Would you like to book an exclusive One 2 One Q & A session so discuss this course, have your questions answered and access offers and discounts? Please use the calendar below to book your free, no obligation session with our panel of experts.

Beginner Level
Intermediate Level
Advanced Level
How is this course delivered?
All classes are delivered live online by expert instructors. Sessions are interactive and include real-time coding, demonstrations, and Q&A. Students also participate in group labs, projects, and discussions
Do I need a technical background to join?
No. This course is designed to take complete beginners through to advanced level. A willingness to learn and basic computer literacy is sufficient to get started.
What software and tools will I need?
You will use Python, Jupyter Notebook, and libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and Keras. Instructions and support for installing and using these tools are provided at the start of the course.
How much time should I commit per week?
Students should commit approximately 8 hours per week for live classes plus an additional 4–6 hours for practice, assignments, and project work.
Will I receive a certificate?
Yes. Students who complete all modules and capstone projects will receive a Generiss Academy Certificate in Data Science with Machine Learning. This can be added to your CV and LinkedIn profile.
Will this course help me get a job?
Yes. The course is designed to be career-focused and job-ready, giving you both the skills and the portfolio you need to stand out to employers. Career guidance and interview preparation support are also available through Generiss Academy.
Is this course fully live or pre-recorded?
This is a 100% Virtual Instructor-Led course. You will attend live, scheduled classes via our HD streaming portal, allowing you to ask questions, share your code screen, and receive instant feedback from your instructor. All sessions are recorded and made available to you for review within 24 hours.
What machine learning libraries will we use?
You will work directly with the industry-standard modern data stack. This includes Pandas and NumPy for data manipulation, Matplotlib and Seaborn for data visualization, Scikit-Learn for traditional machine learning algorithms, and an introduction to TensorFlow/PyTorch for deep learning networks.
What happens if I miss a live session?
Life happens! If you miss a class, you can access the full recording of the lecture alongside the instructor's code repository and notes via your student dashboard. You can also utilize our dedicated community Slack channel to get help from TAs and peers before the next class.

Course Duration

  • Total Length: 12 Weeks

  • Format: Virtual Instructor-Led Training (VILT)

  • Weekly Commitment: 6 hours of live instruction (split into two 3-hour evening/weekend sessions) + 4 hours of self-paced lab work.

 

Roles and Opportunities

Organizations across finance, healthcare, e-commerce, and automation are aggressively hiring specialists who can build production-ready AI pipelines. Upon graduating, you will be highly competitive for the following roles:

  • Data Scientist

  • Machine Learning Engineer

  • AI Research Analyst

  • Predictive Modeller

  • Data Product Manager

  • Business Intelligence (BI) Developer

 

Career Paths

Graduating from this program opens up two primary specialized career trajectories:

  • The Analytical Track (Data Scientist): Progress from Associate Data Scientist $rightarrow$ Senior Data Scientist $rightarrow$ Principal Data Scientist $rightarrow$ Director of Data & Analytics. (Focuses on business insight, predictive experimentation, and executive strategy).

  • The Engineering Track (ML Engineer): Progress from ML Engineer $rightarrow$ Senior ML Engineer $rightarrow$ MLOps Specialist $rightarrow$ Head of AI Infrastructure. (Focuses on deploying models into production, optimizing neural networks, and scaling software pipelines).

 

Current Market Salaries (UK & US)

Data science and machine learning professionals continue to command premium compensation packages due to the specialized nature of production-level AI skills.

United States (USD)

  • Entry-Level / Junior: $85,000 – $115,000

  • Mid-Career Data Scientist: $130,000 – $165,000

  • Machine Learning / AI Specialist: $140,000 – $185,000 (Base salary; FAANG/top-tier firms regularly push total compensation beyond $250,000 with equity).

  • Senior / Principal Roles: $190,000 – $260,000+

United Kingdom (GBP)

  • Entry-Level / Junior: £35,000 – £48,000

  • Mid-Career Data Scientist: £55,000 – £75,000

  • Machine Learning / AI Specialist: £65,000 – £95,000 (London-based roles offer a premium, frequently reaching up to £130,000).

  • Senior / Lead Roles: £95,000 – £145,000+

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Course details
Duration 18 Weeks
Lectures 3
Level Advanced
Course requirements

To ensure your success in this live, fast-paced environment, students should meet the following baseline prerequisites:

  • Programming Basics: Fundamental familiarity with Python (loops, functions, and basic data structures like lists and dictionaries).

  • Mathematics: Basic understanding of high school-level algebra and elementary statistics (mean, median, standard deviation).

  • Hardware: A laptop or desktop computer (Windows, Mac, or Linux) with at least 8GB RAM and a reliable high-speed internet connection for live video streaming and cloud-based coding environments.

Intended audience

This course is specifically structured for ambitious professionals looking to transition into or dominate the data space, including:

  • Data & Business Analysts looking to upgrade their skills from static reporting (Excel/SQL) to predictive modeling.

  • Software Engineers & IT Professionals wanting to specialize in AI, MLOps, and algorithmic development.

  • Academic Researchers & Statisticians transitioning from theoretical math to commercial data roles.

  • Tech Enthusiasts & Career Changers with basic programming knowledge who want an instructor-guided path into data science.