Data scientists and machine learning engineers have specific demands that most consumer laptops simply cannot meet: enough RAM to hold large dataframes in memory, fast storage for dataset I/O, a capable CPU for local model training, and ideally a discrete GPU for GPU-accelerated frameworks such as PyTorch and TensorFlow. The 2-in-1 form factor adds extra utility, letting you annotate charts in tablet mode, present findings to stakeholders without dragging a separate device, and work comfortably on a plane in tent mode. Since last year, Intel's Core Ultra (Meteor Lake and Arrow Lake) generation has brought meaningful improvements to integrated GPU compute via Intel Arc, while LPDDR5X memory is now standard across mid-range convertibles. Qualcomm's Snapdragon X platform has also matured, though Python and conda ecosystem compatibility remains a consideration. This shortlist covers the best options available in the UK right now, from a genuinely portable OLED convertible to a desktop-replacement powerhouse for those running large language model fine-tuning jobs locally.
Quick Verdict
Best Overall: Samsung Galaxy Book4 Pro 360 (2024), the only true 2-in-1 on this list that combines a Core Ultra 7 processor, Thunderbolt 4 connectivity, a stunning Dynamic AMOLED 2X display and a polished Windows 11 experience in a chassis light enough to carry all day. Best Value: Acer Aspire Spin 14 ASP14-52MTN, an OLED convertible with Thunderbolt 4, 16 GB RAM and an Intel Core Ultra 5 at a fraction of the Samsung's price, making it the smartest spend for data scientists on a tighter budget.
Spec Comparison
The Samsung Galaxy Book4 Pro 360 is the standout 2-in-1 on this list for data scientists who need a portable, versatile machine that does not compromise on display quality or connectivity. The 16-inch Dynamic AMOLED 2X panel running at 2880×1800 is genuinely exceptional for data visualisation work: colour accuracy is excellent out of the box, making it ideal for reviewing heatmaps, confusion matrices and Matplotlib or Seaborn charts in their full fidelity. The S Pen stylus is included in the box, which is a meaningful bonus for annotating research papers or sketching model architectures in tablet mode during a commute or conference.
Under the hood, the Intel Core Ultra 7 155H is a Meteor Lake chip with a dedicated Neural Processing Unit (NPU) capable of up to 34 TOPS. While that NPU is not yet exploited by most mainstream ML frameworks, it is a forward-looking feature as Windows AI APIs mature. The 16 P-cores and E-cores configuration means the chip handles multi-threaded data preprocessing pipelines in pandas or Polars comfortably, and the Intel Arc integrated GPU supports OpenCL and, critically, Intel's oneAPI ecosystem, which means you can run GPU-accelerated XGBoost, LightGBM and limited PyTorch workloads without a discrete GPU. For serious deep learning training you will still want a cloud GPU, but for inference, prototyping and feature engineering this machine handles the day-to-day workload well.
Connectivity is a genuine strength. Two Thunderbolt 4 ports allow you to connect an external GPU enclosure (an eGPU via Thunderbolt 4 is supported, though bandwidth is capped versus TB5), a 4K monitor, or high-speed external NVMe drives for dataset storage. The inclusion of a full-size HDMI port and a microSD slot means you are not entirely dongle-dependent, which matters when presenting at a client site or university lab. The 512 GB SSD is adequate for most working datasets, though researchers working with raw image or genomics datasets will want to supplement with external storage.
The 16 GB of LPDDR5X RAM is soldered, which is the main limitation. For ML engineers working with large dataframes, 16 GB can become a bottleneck when running multiple Jupyter kernels simultaneously. The machine is also on the premium end of the price spectrum, but for a true 2-in-1 convertible with this display and connectivity combination, there is no direct rival in the UK market at this specification level. Battery life is strong for a 16-inch machine, and the chassis is impressively slim given the screen size.
Verdict: The best all-round 2-in-1 for data scientists who prioritise display quality, portability and Thunderbolt connectivity, and who do the heavy GPU training in the cloud.
Pros
- 16" Dynamic AMOLED 2X display with 2880×1800 resolution is outstanding for data visualisation
- Two Thunderbolt 4 ports enable eGPU, 4K display and fast external NVMe storage
- Intel Core Ultra 7 155H NPU provides future-proofing for on-device AI inference APIs
Cons
- 16 GB soldered RAM cannot be upgraded, which limits large in-memory dataframe work
- Intel Arc integrated GPU is not yet well-supported by all PyTorch CUDA-equivalent workflows
The ASUS Vivobook 14 (2025) earns its place on this list primarily because of its memory and storage configuration: 24 GB of RAM and a 2 TB SSD at its price point is unusual and directly relevant to data science workflows. Most laptops in the sub-£1,500 bracket ship with 16 GB of soldered RAM, which can become a genuine constraint when working with large pandas DataFrames, running multiple Jupyter kernels, or keeping a local Postgres or DuckDB instance running alongside an IDE and a browser. The extra 8 GB here provides meaningful headroom.
The Intel Core i5-1334U is a 13th-generation Raptor Lake-U chip, which is a dual-cluster design with 2 performance cores and 8 efficiency cores. It is not the fastest chip in this comparison, and it lacks the dedicated NPU found in the Core Ultra (Meteor Lake) generation, but it is perfectly capable for the day-to-day data science workflow: running notebooks, training scikit-learn pipelines, doing feature engineering in Polars, and connecting to remote GPU servers via SSH or Jupyter remote kernels. The Intel UHD Graphics integrated GPU does not support CUDA and has limited oneAPI support compared to Intel Arc, so GPU-accelerated local training is not a realistic use case here.
The 14-inch FHD display at 1920×1080 is the weakest point of this machine for data science use. While the IPS panel is serviceable, the resolution and colour accuracy fall short of the OLED panels on the Samsung and Acer picks. For engineers who spend most of their time in a terminal, an IDE such as VS Code or PyCharm, and a Jupyter notebook, this is an acceptable trade-off. Those who regularly review detailed visualisations or present findings on the laptop screen will notice the difference.
The Vivobook 14 is a conventional clamshell laptop rather than a true 2-in-1 convertible. It does not have a 360-degree hinge or touchscreen, which means it lacks the flexibility of the Samsung and Acer picks. However, for data scientists who do not need the tablet or tent mode functionality and simply want the best RAM and storage configuration for the money, it is a pragmatic choice. The 2 TB SSD is particularly valuable for those working with large raw datasets, model checkpoints and Docker images locally.
Verdict: The best pick for data scientists who prioritise RAM and storage capacity over convertible flexibility, and who do GPU training remotely.
Pros
- 24 GB RAM is the highest in this comparison among non-gaming machines, reducing memory pressure in multi-kernel workflows
- 2 TB SSD provides ample on-device storage for large datasets, model checkpoints and Docker images
Cons
- Not a 2-in-1 convertible: no 360-degree hinge or tablet mode
- Intel Core i5-1334U lacks a dedicated NPU and the Intel Arc iGPU, limiting on-device AI acceleration
- 1920×1080 IPS display is the weakest screen in this comparison for data visualisation work
Buying Guide
RAM: How Much Do You Actually Need?
RAM is the single most important specification for data scientists after CPU performance. When you load a dataset into a pandas DataFrame, the entire dataset must fit in RAM alongside the Python interpreter, your IDE, a browser and any other running processes. A 1 GB CSV file can expand to 3 to 5 GB in memory once parsed into a DataFrame with mixed types. For most practitioners working with datasets under a few hundred megabytes, 16 GB is adequate. For those working with multi-gigabyte datasets, running multiple Jupyter kernels simultaneously, or keeping Docker containers running alongside their notebook environment, 24 GB or 32 GB is a meaningful upgrade. Note that most modern laptops use soldered LPDDR5 or LPDDR5X RAM that cannot be upgraded after purchase, so choose carefully at the point of purchase.
GPU: Integrated vs Discrete, and the CUDA Question
The majority of ML frameworks, including PyTorch and TensorFlow, are optimised for Nvidia CUDA. If you plan to do any serious local model training, a discrete Nvidia GPU is the only option that offers full framework compatibility without workarounds. Intel Arc integrated GPUs support oneAPI and Intel Extension for PyTorch, which is a functional alternative for some workflows but not a drop-in replacement. AMD ROCm support has improved but remains inconsistent across frameworks. For most data scientists who use cloud GPU instances (AWS, GCP, Lambda Labs, Vast.ai) for training and use their laptop only for prototyping, feature engineering and inference, an integrated GPU is perfectly sufficient.
Storage: Speed and Capacity
NVMe PCIe 4 SSDs offer read speeds of 5,000 to 7,000 MB/s, which makes a tangible difference when loading large datasets from disk into memory. Avoid machines with eMMC or UFS storage for data science work, as these are significantly slower. For capacity, 512 GB is the practical minimum, but 1 TB or 2 TB is preferable if you work with image datasets, genomics data, or keep large model checkpoints locally. External NVMe drives connected via Thunderbolt 4 or 5 are a cost-effective way to expand storage without replacing the machine.
Connectivity: Thunderbolt Matters
Thunderbolt 4 provides up to 40 Gbps bandwidth, enabling eGPU enclosures, 4K monitors at 60Hz, and external NVMe drives at full speed. For data scientists who want to add a discrete GPU later without buying a new laptop, Thunderbolt 4 or 5 is essential. USB 3.2 Gen 1 (5 Gbps) and Gen 2 (10 Gbps), found on budget machines, are adequate for peripherals but too slow for high-performance external storage or eGPU use.
Operating System and Python Ecosystem Compatibility
Windows 11 and macOS both support the full Python and conda ecosystem without significant issues. ChromeOS does not natively support conda, pip-installed packages with C extensions, or Docker in the standard configuration, making it unsuitable for professional data science work. Qualcomm Snapdragon-based Windows machines have improved conda compatibility but some packages with x86-specific compiled extensions may require emulation, which adds friction. Intel and AMD x86 Windows machines have the broadest compatibility with the data science software stack.
The 2-in-1 Form Factor: Is It Worth It for Data Scientists?
A 360-degree hinge and touchscreen add cost and weight, but they offer genuine utility for data scientists. Tablet mode is useful for reading papers, annotating PDFs with a stylus, and sketching model architectures. Tent mode is practical for presenting dashboards or results to colleagues without requiring a projector. If you regularly present findings, attend conferences, or work in varied environments, the 2-in-1 form factor is worth the premium. If you work exclusively at a desk with an external monitor, a conventional clamshell may offer better value for the same budget.
For the majority of data scientists and ML engineers, the Samsung Galaxy Book4 Pro 360 (2024) is the overall winner. It is the only machine in this comparison that combines a true 2-in-1 convertible form factor with a Core Ultra 7 processor, dual Thunderbolt 4 ports, a 16-inch Dynamic AMOLED 2X display and a polished Windows 11 experience. The display alone justifies the premium for anyone who spends meaningful time reviewing visualisations, and the Thunderbolt 4 ports provide a clear upgrade path to an eGPU enclosure as workloads grow. The 16 GB RAM ceiling is the one genuine limitation, and those who need more memory headroom should consider pairing it with an external NVMe and cloud GPU instances for training.
For those on a tighter budget, the Acer Aspire Spin 14 is the best value 2-in-1 in this comparison, delivering OLED, Thunderbolt 4 and a current-generation Core Ultra chip at a significantly lower price. ML engineers who need on-device GPU power for local LLM fine-tuning and can accept a non-convertible, desktop-replacement form factor should look at the MSI Raider, which is in a different league for raw compute performance.
