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Kijarney Help Center

How can we help?

Find answers, learn how to use Kijarney, troubleshoot common problems and get the most out of your data analysis workflow.

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Help Topics

What do you need help with?

Start with one of the topics below to find information about using Kijarney.

Getting Started

Learn how to create an account, upload your first dataset and begin your first analysis.

View Guide

Uploading Data

Learn how to import datasets and prepare them for analysis.

Learn More

Data Cleaning

Understand missing values, duplicates, data types and outliers.

Learn More

Visualization

Learn how to create charts, explore patterns and visualize your datasets.

Learn More

Statistical Analysis

Understand descriptive statistics, correlations and statistical testing.

Learn More

Machine Learning

Learn about models, predictions, classification and machine learning workflows.

Learn More

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Getting Started

Your first analysis in four steps.

You don't need a complicated setup. Start with your dataset and work through the analysis workflow.

STEP 01

Create an Account

Register for Kijarney and access your data analysis workspace.

STEP 02

Upload Data

Import your dataset into Kijarney and inspect its structure.

STEP 03

Prepare & Explore

Clean your data, examine statistics and create visualizations.

STEP 04

Analyze & Predict

Perform deeper analysis or train machine learning models where appropriate.

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Uploading Data

Bring your dataset into Kijarney.

Start by importing the dataset you want to analyze. Make sure your file contains clear column names and consistent data.

Before Uploading

Check that your dataset has meaningful column names, consistent values and appropriate data types.

After Uploading

Inspect the dataset before making changes. Look for missing values, duplicates, unusual values and incorrect data types.

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Data Cleaning

Prepare your data before analysis.

Data quality directly affects the reliability of your analysis and machine learning models.

Duplicates

Duplicate records can distort statistics and model results. Identify them before continuing your analysis.

Missing Values

Missing values may require removal, replacement or another appropriate treatment depending on the dataset.

Data Types

Incorrect data types can affect charts, statistics and machine learning models.

Outliers

Unusual observations may represent genuine events or data problems. Investigate them before deciding whether to remove them.

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Visualization

Turn numbers into visual insights.

Visualizations help reveal distributions, relationships, trends and unusual patterns that may be difficult to identify from raw data.

Bar Charts

Useful for comparing categories and discrete values.

Line Charts

Useful for examining changes and trends over an ordered variable such as time.

Scatter Plots

Useful for exploring relationships between numerical variables.

Heatmaps

Useful for displaying relationships, correlations or matrix-style data.

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Statistical Analysis

Understand what your data is telling you.

Statistical analysis helps move beyond visualization by quantifying patterns and relationships in your data.

Descriptive Statistics

Examine measures such as mean, median, standard deviation, minimum and maximum.

Correlation

Explore the strength and direction of relationships between variables.

Hypothesis Testing

Use statistical tests to evaluate evidence for or against a hypothesis.

Trend Analysis

Examine how values change over time or across ordered observations.

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Machine Learning

Build models from your data.

Machine learning can help identify patterns and generate predictions from historical data.

Regression

Predict numerical values based on relationships learned from your dataset.

Classification

Predict categories or labels for new observations.

Clustering

Discover groups of similar observations without predefined labels.

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Frequently Asked Questions

Common questions.

Kijarney is a browser-based data science platform that provides tools for data cleaning, visualization, statistical analysis and machine learning.
Kijarney is designed to simplify many common data science workflows. The amount of programming knowledge required depends on the specific analysis or model you want to perform.
Kijarney is designed for structured datasets containing rows and columns. Supported file formats depend on the current upload options available in your account.
Yes. Kijarney provides machine learning capabilities for tasks such as regression, classification, clustering and prediction.
Start by checking your dataset for missing values, duplicate rows, incorrect data types, unusual values and inconsistent formatting. Cleaning the dataset before analysis can resolve many problems.
You can contact the Kijarney team through the support options provided below.
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Still need help?

If you cannot find the answer you're looking for, contact the Kijarney team and tell us what you're trying to accomplish.

Chat on WhatsApp Email Support
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