A Laptop For Every Data Scientist: PCs Compared Across 5 Price Ranges

Even as cloud compute continues to get cheaper, data scientists need to stay up to date with the latest in technology so they can run state-of-the-art algorithms locally. With the PC market heating up, especially in the laptop space, data scientists are now spoilt for choice as to what to pick for their next workspace.

As most data scientists use general purpose languages such as Python, R and Scala, it is important to pick a computer that can keep up. Due to the low-level optimisations that these languages bring to the table, it is possible for them to run on low-level processors.

However, armed with the largest of datasets and the most complex of problems, data scientists’ workloads will stress lower-powered computers. Keeping that in mind, it is important to pick a good laptop for on-the-go compute tasks.

The factors that will be considered for the ranking will be:

Processor Clock Speed: It is important to look at the clock speed and not core count as coding on Python and other languages is a single-threaded workload. Parallelism is possible with threading libraries, but the base distributions only runs on one core. A high clock speed is important for faster execution.

RAM Size: In case of a laptop having a higher size of RAM, it is possible to store the dataset in the RAM and work on it from there. This increases the read and write speed of data vastly, and results in tangible performance increases.

Storage Availability: It is important for a modern laptop to have an SSD to run the OS. It is also important to have a higher-capacity HDD to store datasets, libraries and more.

Laptop Under ₹30,000

In case you are looking for a basic laptop that can run Python code efficiently, one can start from the lower end of the spectrum. The Acer Aspire 3 A315 is a good choice for a data scientist starting out.

It comes with 4GB of RAM, so smaller datasets can be run on it, along with a processor from Intel’s 8th Gen i3 lineup. It is to be noted that the laptop’s processor, while only dual-core, can boost up to 3.2GHz, making it fit for our workload.

This is a budget option, and does not include an SSD, opting for a 1TB HDD. It is basic, and will enable any data scientist to begin his/her journey.

Laptop Under ₹40,000

For this price range, it is possible to pick up a fairly high-powered laptop with a good amount of RAM. The Lenovo Ideapad 330 with the Core i5 8250U is a good pick for any data scientist.

The CPU boosts up to 3.4GHz, and 4 cores with 8 threads allows for multi-threaded workloads to be run with ease. It also has 8GB of RAM, a good fit for larger datasets. It still has a HDD as opposed to an SSD, with a large capacity of 1TB.

Laptop Under ₹50,000

For this price range, there are a couple of recommendations, depending on the workload. For simple coding tasks, the HP 15 series with the Core i5 8265U is a good fit. With 8GB of fast RAM and a processor with a boost clock of 3.9GHz, the laptop is a beast for CPU-heavy tasks.

For more GPU-oriented tasks, the Ideapad 330 with the i5 8300H processor and a 2GB 1050Ti GPU is a great choice. The added GPU compute makes for a good balance for parallelism, and the 8300H CPU with a 4.0GHz is a high-powered model (indicated by the H) and will provide dependable backup to the GPU compute.

Both of the laptops come with 1TB HDDs.

Laptop Under ₹60,000

The Lenovo Ideapad 330 with the i7 8550U emerges as the clear winner in this price range. The i7 offers clock speeds of upto 4.0 GHz, making it a good fit for coding and related single-threaded tasks. This quad-core processor is also bundled with a 4GB GTX 1050 for GPU compute workloads.

The Wallet-Breakers

There are also options that are above the 60,000 price range, with the first option that comes to mind being the HP Pavilion 15 with the 9th Gen i5 9300H and GTX 1650. Priced slightly above 60,000, this laptop is quite future-proof, with a 512GB+512GB SSD and HDD combination. The 9300H is one of the more higher-powered processors, and also has the advantage of being the latest generation of CPUs manufactured by Intel.

The CPU boosts up to 4.1GHz, making it an absolute workhorse. The 8GB of RAM can easily be upgraded to a 16GB variant with an additional stick.

Apart from this, if you are playing for team Red, there is the ASUS TUF laptop with AMD’s Ryzen 7 3750H with the GTX 1650. This processor is slightly better than the 9300H, and boosts up to 4.0GHz. It is also equipped with a 512GB+512GB SSD and HDD combo, and 8GB of RAM.

More Great AIM Stories

Anirudh VK
I am an AI enthusiast and love keeping up with the latest events in the space. I love video games and pizza.

More Stories


8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

Yugesh Verma
All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges

Yugesh Verma
A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Yugesh Verma
A guide to explainable named entity recognition

Named entity recognition (NER) is difficult to understand how the process of NER worked in the background or how the process is behaving with the data, it needs more explainability. we can make it more explainable.

Yugesh Verma
10 real-life applications of Genetic Optimization

Genetic algorithms have a variety of applications, and one of the basic applications of genetic algorithms can be the optimization of problems and solutions. We use optimization for finding the best solution to any problem. Optimization using genetic algorithms can be considered genetic optimization

Yugesh Verma
How to Visualize Backpropagation in Neural Networks?

The backpropagation algorithm computes the gradient of the loss function with respect to the weights. these algorithms are complex and visualizing backpropagation algorithms can help us in understanding its procedure in neural network.

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM