machine learning & advanced analytics
Leverage Data Scripts to
Perform advanced data analysis and statistics on all your ClicData datasets using Python.
You're gonna ❤️️ Data Scripts...
Built-in Python script editor
✔ Write custom Python scripts directly in the application using our Data Scripts module.
✔ Use datasets from your ClicData account, add parameters and variables, or output your script results to a new dataset.
✔ Leverage AI to generate code from natural language queries.
Quick set up with
pre-installed libraries and packages
✔ Perform advanced statistical analysis, machine learning, and more using built-in libraries like Pandas, NumPy and SciPy.
✔ Hit the ground running with our pre-installed packages or install your own.
Full database performance
✔ Run your Python scripts efficiently within the platform. Take advantage of the optimized database infrastructure, performance, and scalability.
✔ Data Scripts eliminate disk overload issues so you can run other tasks at the same time
✔ Monitor CPU and memory usage percentages to ensure optimal performance and resource management.
Automated tasks and
data updates
✔ Schedule Python scripts execution at specific intervals.
✔ Reduce the risk of human error and free up resources for high value work.
✔ Feed your models with up-to-date data ensuring accurate and timely insights.
One Python Scripting module, endless possibilities
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Trending
Data trending refers to the analysis of patterns and trends within a set of data over time. It involves examining data points over a specific period to identify the direction and magnitude of changes in the data.
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Cause and Effect
Cause and effect can be explored using statistical techniques such as regression analysis, correlation analysis, and causal inference methods.
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Pattern Detection
Pattern detection is the process of identifying recurring trends or patterns in data. It involves using statistical and machine learning algorithms to analyze data and identify patterns that may not be easily observable to the naked eye.
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Outlier Analysis
Outlier analysis is the process of identifying and analyzing data points that are significantly different from the majority of the data in a dataset. These data points, known as outliers, may be due to measurement errors, data entry errors, or represent unusual events or behaviors.
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Sentiment Analysis
Sentiment analysis and Opinion Mining are used to identify and extract subjective information from text, such as reviews, social media posts, and news articles. It aims to determine whether the sentiment expressed in the text is positive, negative, or neutral.
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Diagnostic Analytics
Identify the root cause of a problem or issue. It is used to troubleshoot problems and inform process improvement for example why a particular product is underperforming, determining the cause of customer complaints, or analyzing the root cause of a process bottleneck.
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Predictive Analytics
This involves using statistical models and machine learning algorithms to make predictions about future events or behaviors based on historical data. It is used to forecast future outcomes and inform strategic planning.
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Programming
Use Python to go beyond data analysis and machine learning, and develop new connections via API or scrap online data for brand coverage, crisis management or any other business use case.
FAQ
Resources to get your Machine Learning projects off the ground
Understanding AI, Machine Learning, Data Science, and Deep Learning
AI, machine learning, data science, deep learning, we tend to use them interchangeably. While there are overlaps between these fields, they do have their own…
Why Statistics for Machine Learning Matters
All modern predictive models involving machine learning rely on statistical algorithms. Statistical techniques capture patterns within data which form the basis of data modeling and…
What’s the role of Machine Learning in BI applications?
Machine Learning is an increasingly trending topic. Tech lovers and developers write and talk about it, but…
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