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Scikit-learn VS Qlik

Compare Scikit-learn VS Qlik and see what are their differences

Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
Qlik

Qlik offers an Active Intelligence platform, delivering end-to-end, real-time data integration and analytics cloud solutions to close the gaps between data, insights, and action.

Rating
5.0 · 1 review
Alternatives Is it good?
Sales Blueprint Architect
Close More Business sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Scikit-learn seems to be a lot more popular than Qlik. While we know about 40 links to Scikit-learn, we've tracked only 1 mention of Qlik.

social mentions
40 vs 1
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
205 vs 240+

Base details

Website, pricing, platforms and company facts side by side.

Scikit-learn
Qlik
Website scikit-learn.org qlik.com
Pricing
Open source
Official pricing
Listed in
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1
Data Analytics Data Integration Data Dashboard Business Intelligence AI And Machine Learning Data Visualization Embedded Analytics Data Streaming (CDC) Data Warehouse Automation +6

Features and specs

What each product offers, as listed by its team.

Scikit-learn 5 features
Qlik 5 features
  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.
  • Data Integration
    Qlik offers powerful data integration capabilities, allowing users to pull in data from multiple sources, clean it, and prepare it for analysis. This is particularly useful for organizations dealing with diverse datasets.
  • Associative Data Engine
    Qlik's unique associative data engine enables users to explore data freely, without the limitations of traditional hierarchical or query-based models. This feature ensures that all data relationships are maintained and accessible.
  • Interactive Visualizations
    Qlik provides highly interactive and customizable visualizations, making it easier for users to derive insights and share findings. The visualizations are intuitive and can be tailored to meet specific business needs.
  • AI Capabilities
    The platform includes AI-driven features like Insight Advisor, which helps users uncover insights and generate analytics automatically. This reduces the learning curve and makes advanced analytics more accessible.
  • Scalability
    Qlik is designed to scale from small teams to large enterprises. It supports both on-premises and cloud deployments, making it flexible to meet various business sizes and infrastructure preferences.

Possible disadvantages

  • Complexity in Initial Setup
    The initial setup and configuration of Qlik can be complex and time-consuming, often requiring specialized knowledge or professional services to get started effectively.
  • Cost
    Qlik can be expensive, especially for smaller businesses. The cost includes not just licensing fees but also potential expenditures on training, deployment, and maintenance.
  • Learning Curve
    Although Qlik offers a powerful feature set, there is a steep learning curve for new users. Mastering the platform's full capabilities can take significant time and effort.
  • Performance Issues
    In some instances, users have reported performance issues, particularly when dealing with extremely large datasets or complex queries, which can hinder real-time analysis.
  • Limited Third-Party Integration
    While Qlik does support integration with various third-party tools, it may not be as extensive as some other analytics platforms. This can limit its usefulness in a highly diversified technology stack.

Analysis

An editorial look at what each product does well and who it suits.

Scikit-learn
Qlik

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Overall verdict

  • Qlik is generally considered a good choice for data visualization and business intelligence needs.

Why this product is good

  • Flexibility
    Qlik's platform allows for self-service data discovery, guided analytics, and embedded analytics.
  • Integration
    Qlik integrates well with various data sources, making it versatile for diverse data environments.
  • User friendly
    Qlik offers an intuitive interface that caters both to advanced users and beginners.
  • Active community
    There is a strong community of Qlik users and developers who contribute to forums and share solutions.
  • Powerful analytics
    It provides robust analytics capabilities with associative data indexing, which lets users easily explore data.

Recommended for

  • Businesses seeking a comprehensive business intelligence tool.
  • Users who require a highly flexible, self-service analytics environment.
  • Organizations that need to integrate a wide array of data sources.
  • Companies looking for strong visual analytics capabilities.

Videos

Walkthroughs and reviews on video.

Scikit-learn 2 videos + Add
Qlik 2 videos + Add

Learning Scikit-Learn (AI Adventures)

More videos

  • - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

A Day in the life of a Qlik Cloud User

More videos

  • - Qlik Sense Product Tour

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Scikit-learn
Qlik
100% 100%
Data Science And Machine Learning
0% 0%
0% 0%
Data Dashboard
100% 100%
100% 100%
Data Science Tools
0% 0%
0% 0%
Business Intelligence
100% 100%

User comments

Share your experience with using Scikit-learn and Qlik. For example, how are they different and which one is better?

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Scikit-learn no reviews yet
Qlik 5.0 · 1 review
  • Wow So Impresssed
    SaaSHub review
    · Apr 2026

    Qlik's associative data model makes data analytics seamless. Highly Recomend using this to anyone.

  • 10 Best Alternatives to Looker in 2024
    www.sigmacomputing.com · Dec 2024

    Qlik: Qlik sets itself apart with its associative analytics engine, enabling users to uncover trends and patterns through intuitive exploration without predefined queries. This offers a more flexible and dynamic...

  • Top 11 Fivetran Alternatives for 2024
    estuary.dev · Aug 2024

    Qlik provides three data integration products - Stitch (covered under Stitch) Talend Data Fabric (covered under Talend) and Qlik Replicate, which was originally Attunity. Qlik Replicate has both on-premises and cloud...

View more

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Scikit-learn 40 mentions
Qlik 1 mention
  • Detecting Ingress Tool Transfer (T1105) with Python
    Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process,... - Source: dev.to / 4 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Pick
    Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab.... - Source: dev.to / 5 months ago
  • Where to Get Hands-On AI Training for Cybersecurity Professionals
    Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable... - Source: dev.to / 5 months ago

View more

  • GME FTD - Moving Daily Avg.
    All files was pulled into a program called : QLIK, qlik.com is the company and my company uses it for our reporting and our customer's reporting needs. Source: over 5 years ago

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