The Ultimate Guide to Top 10 Data Science Tools in 2023

Data science has seen tremendous growth in recent years, with companies leveraging data to drive decision making. This rising demand has led to an explosion of data science tools.

In this comprehensive guide, we‘ll cover the top 10 data science tools that every data scientist should know, along with key trends and alternatives.

The Rise of Data Science Tools

Interest in data science has steadily increased over the past decade, as shown in Google Trends:
Google trends show rising interest in data science
Two main classes of tools have emerged:

  • Self-service tools for those with technical expertise like Python and R
  • Low/no code tools that enable business users to perform analysis

Understanding these landscapes is key for selecting the right data science stack.

Python Remains King

According to Kaggle‘s 2022 survey of over 23,000 data scientists, Python remains the dominant language with 87.6% of respondents using it. SQL, C++, and R rounded out the top languages [1].

Python has overtaken SQL in popularity since 2016:
Python popularity vs SQL over time
An impressive 81% of respondents believed Python should be the first language learned for data science.
81% say Python first for data science
While Python leads, there are many other important tools like databases, workflow managers, and visualization libraries that comprise a modern data science stack:
Data science tools ecosystem
Understanding the right mix is critical. Next we‘ll break down the landscape.

The Data Science Tools Landscape

There are two key ways to categorize data science tools:

Open Source vs Proprietary: Most platforms are open source, but some like DataRobot use proprietary code. Even these often open source parts of their stacks to attract developers.

Low/No Code vs Coding: Tools like Tensorflow are code-focused, while no-code tools like DataRobot enable business users to develop models.

Below we‘ll cover alternatives to building custom models, as well as provide our ranked list of top tools.

Alternatives to Custom Models

While you can build custom data science models in-house, alternatives exist:

  • Competitions: Get cost-effective models from datasets on platforms like Kaggle.
  • Consulting: Hire data science consultants to create solutions tailored to your needs.

We‘ve written comprehensive guides on AI consulting and data science consulting processes.

Now let‘s dive into the top tools.

Top 10 Data Science Tools

Based on popularity and company size, here are the top 10 data science tools to know:

1. TensorFlow

Created by Google for deep learning, TensorFlow has 164k GitHub stars [2]. It enables building ML models on premise, cloud, browser, or mobile.

2. PyTorch

PyTorch is an open source Python framework with 55k GitHub stars [3] used widely for its flexibility and speed.

3. Alteryx

Founded by MIT data scientists, Alteryx provides a proprietary analytics platform used by major enterprises. Its open source Featuretools for feature engineering has 6k stars [4].

4. DataRobot

DataRobot offers a proprietary automated machine learning platform, making AI more accessible to business users.

5. Dataiku

Dataiku is an end-to-end proprietary platform for data integration, machine learning, and collaboration used by companies for use cases like churn prediction.

6. H2O.ai

H2O is an open source suite for automatic machine learning, with capabilities like automatic model selection.

7. Trifacta

Trifacta focuses on interactive cloud platforms for data wrangling, profiling, and pipeline management. It was acquired by Alteryx in 2022.

8. RapidMiner

RapidMiner provides an open source platform for building machine learning models and managing data science processes.

9. Lumen Data

Lumen offers data strategy consulting and services like data integration, analytics, and MDM on their proprietary platform.

10. Qubole

Qubole provides a cloud data lake platform, enabling ad-hoc analysis and data pipelines.

Speeding Up Data Science Projects

Two key tools can accelerate data science projects:

Web Crawlers

Web crawlers automatically collect online data for analytics and machine learning, powering innovations like AI assistants.

Data Extraction Tools

These tools convert unstructured data like text and images into structured data for model training. This can save significant time over manual approaches.

Key Takeaways

  • Python remains the dominant tool for data scientists in 2023.
  • Open source and proprietary tools both play major roles.
  • Low/no code tools are expanding access to analytics and machine learning.
  • The top 10 tools provide capabilities across the data science pipeline.
  • Web crawlers and data extractors speed up data collection and prep.

For a deeper dive, check out our guides on data mining, consultant selection, and data pipelines.

I hope this overview has been helpful for understanding the data science tools landscape in 2023. Let me know if you have any other questions!

Sources

  1. "2022 Data Science and Machine Learning Survey." Kaggle. https://www.kaggle.com/competitions/kaggle-survey-2022
  2. "Tensorflow." Github. https://github.com/tensorflow/tensorflow
  3. "PyTorch." Github. https://github.com/pytorch/pytorch
  4. "Featuretools." Github. https://github.com/alteryx/featuretools

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