Statistical Data Notes – Class 10 AI (417) | Practical Revision

Get comprehensive practical revision notes of statistical data class 10 AI and data science essentials designed to help you ace your board exams and practicals. This Unit 4 Statistical Data is assessed in practical examinations as per the CBSE syllabus.

What is Data Science?

Data Science is a field that combines statistics, data analysis, and machine learning to understand real-world problems, analyse data patterns, and make accurate predictions and decisions.
It uses knowledge from:

• Mathematics
• Statistics
• Computer Science
• Information Science

Applications of Data Science

1️⃣ Internet Search: Search engines like Google use data science algorithms to provide best results in fractions of a second.

2️⃣ Targeted Advertising: It is a major application of data science, where audiences are identified using data science algorithms based on their behaviour, preferences, and data patterns for personalized advertising.

3️⃣ Website Recommendations: Key application of data science, where systems suggest relevant products or content based on a user’s past searches, behaviour, and interests to improve user experience.

Example: Product suggestions on Amazon.

Benefits:

• Better user experience
• Personalized suggestions
• Increased sales

4️⃣ Genetics & Genomics: Data Science enables personalized treatment by combining medical and genomic data for disease research, drug response analysis, and genetic risk prediction.

• Benefits:
  • Personalized treatment
  • Better understanding of DNA
  • Individual healthcare improvement
  • Advance generic risk prediction

Introduction to No-Code, Low-Code, and High-Code

There are three most popular approaches to code:

• High-Code
• Low-Code
• No-Code

📌 No-Code

• Completely code-free platforms for beginners.
• Use graphical interfaces to build AI solutions.
• No prior technical knowledge required.

📌 Low-Code

• Uses templates and drag-and-drop with minimal coding.
• Allows some customization with basic coding.
• Suitable for users with basic technical knowledge.

📌 High-Code

• Requires writing full code from scratch.
• Needs advanced programming skills.
• Offers full control over application design and features.

Aspect	High Code	Low Code	No Code
Definition	Traditional development using full coding	Uses tools with some coding	No coding required
Coding Required	All code is written manually	Some coding required	No coding needed
Users	Professional developers	Users with basic technical knowledge	Anyone (beginners)
Cost	Expensive	Less expensive than high code	Least expensive
Customization	Full control and customization	Limited customization	Very limited customization
Ease of Use	Difficult	Moderate	Very easy (drag-and-drop)

Why do we need No-Code AI?

• Reduces coding errors, making development easier.
• No coding required → no syntax or programming issues.
• Cost-effective compared to fully coded AI systems.
• Companies can build AI solutions without hiring experts.
• Easy to use – even students can create AI models.
• Uses drag-and-drop interface for simple development.
• Allows users to see what they are building in real time.

Who can use No-Code AI?

• Accessible to the general public.
• Suitable for non-technical users like doctors, architects, and musicians.

Benefits of No-Code Tool

• Accessibility: Both technical and non-technical can solve business problems
• Easy to Use: Drag and drop features makes it very easy to create applications
• Fast: Development is significantly faster than traditional development
• Innovation: Business user can build solutions for unique problems

Disadvantages of No-Code tools

• Lack of Flexibility: Drag-and-drop features are convenient, but they limit customization and flexibility in no-code platforms.
• Automation Bias: Users may rely too much on the tool’s decisions and ignore other correct information or human judgment.
• Security Issues: These platforms may not focus strongly on security and offer limited control, making them less suitable for handling sensitive data.

Popular No-Code AI Tools

No-Code tool	Details	Released
Azure Machine Learning	Cloud-based ML service by Microsoft Allows building ML models without coding Supports data cleaning, training, evaluation, and deployment	July 2014
Google Cloud AutoML	Cloud-based service provided by GoogleEnables users with limited ML knowledge to build high-quality models Allows creation of custom ML models quickly Models can be easily used in applications and websites	January 2018
Orange Data Mining	An open-source data visualization, machine learning and data mining toolkit. Developer: University of LjubljanaNo coding required Easy to use with attractive visuals	October 1996
Lobe AI	Create custom machine learning models using a visual interfaceMakes machine learning simple and easy Free and easy-to-use for training models Automatically trains and exports custom models	2015
Teachable Machine	Web-based tool for creating ML models easily Supports training with images, sounds, and poses No coding or technical expertise required	November 2017

Important Concepts in Statistics

Statistical Sampling

• The entire set of raw data available for test is called population
• Sample or portion of the population is taken for test.

Descriptive statistics

• Helps in understanding the basic characteristics of data
• Mean: Average value of the data
• Median: Middle value in sorted data
• Mode: Most frequently occurring value

Distribution

Probability

• Probability is the likelihood (chance) of an event occurring.
• An event is the outcome of an experiment.
• Events can be:
  • Independent (not affected by other events)
  • Dependent (affected by other events)

Variance

• Variance: Shows how far data values are from the mean (spread of data).
• Standard Deviation: Indicates how widely values are distributed.
• Outlier: A data value that is very different from other values

Orange Data Mining

• An open-source data visualization, machine learning and data mining toolkit.
• Developer: University of Ljubljana
• No coding required
• Data analysis is done through Python and visual programming.
• perform operations on data through simple drag and drop steps.
• Easy to use with attractive visuals

Orange Data Mining Widgets

🔹 Data Exploration Widgets

• Used to explore and understand data patterns or trends
• Scatter Plot: Shows relationship between two variables
• Data Table: Displays and inspects data
• Distributions: Shows data distribution (histograms)

🔹 Preprocessing Widgets

• Used to clean up data and ensure data is on same scale
• Impute: Handles missing values
• Normalize: Scales data to a common range
• Select Columns: Choose specific columns from datasets

🔹 Feature Selection Widgets

• Used to select important features of data for analysis
• Select Columns: Chooses relevant features
• Select Best Features: Automatically selects important features

🔹 Modelling Widgets

• Used to build machine learning models like decision trees or clustering algorithms
• Classification Tree: Constructs a decision tree classifier.
• k-Means: Performs k-means clustering on the data.
• Support Vector Machine: Trains a support vector machine classifier.
• Logistic Regression: Constructs a logistic regression model.

🔹 Evaluation Widgets

• Used to check model performance
• Test & Score: Evaluates model accuracy
• Cross Validation: Tests model reliability
• ROC Curve: Shows model performance graph

🔹 Visualization Widgets

• Used to represent data visually like charts or graphs
• Bar Chart: Displays data in bars
• Heat Map: Shows data intensity using colors
• Scatter Plot: Shows relationship between variables

Note: Unit 4 (Statistical Data) is assessed in practical examinations as per the CBSE syllabus.