Data, Python, statistics, linear regression and case studies — written by Aivasu Tech Institute's mentors. Free to read, structured to actually help you learn.
Data Science notes by Aivasu Tech Institute cover 109 topics — from ds home to deploy on streamlit cloud & render — each explained with short definitions, syntax and runnable code examples. They are 100% free, need no signup, and work as quick revision for college exams, Data Science interviews and Aivasu Tech Institute's mentor-led Data Science course in Pitampura, Delhi.
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Data Science is a combination of multiple disciplines that uses statistics, data analysis, and machine learning to analyze data and to extract knowledge and ins
One purpose of Data Science is to structure data, making it interpretable and easy to work with.
A database table is a table with structured data.
Python is a programming language widely used by Data Scientists.
A data frame is a structured representation of data.
This chapter shows three commonly used functions when working with Data Science: max(), min(), and mean().
Before analyzing data, a Data Scientist must extract the data, and make it clean and valuable.
Mathematical functions are important to know as a data scientist, because we want to make predictions and interpret them.
| Duration | Average_Pulse | Max_Pulse | Calorie_Burnage | Hours_Work | Hours_Sleep |
Now we will explain how we found the slope and intercept of our function:
Statistics is the science of analyzing data.
Percentiles are used in statistics to give you a number that describes the value that a given percent of the values are lower than.
Standard deviation is a number that describes how spread out the observations are.
Variance is another number that indicates how spread out the values are.
Correlation measures the relationship between two variables.
A matrix is an array of numbers arranged in rows and columns.
Correlation measures the numerical relationship between two variables.
We are missing one important variable that affects Calorie_Burnage, which is the Duration of the training session.
The output from linear regression can be summarized in a regression table.
The "Information Part" in Regression Table
The "Coefficients Part" in Regression Table
The "Statistics of the Coefficients Part" in Regression Table
R-Squared and Adjusted R-Squared describes how well the linear regression model fits the data points:
Case: Use Duration + Average\_Pulse to Predict Calorie\_Burnage
A complete Master's Program in Data Science roadmap — 270+ hours, 25+ projects and the full toolkit: Python, SQL, ML, Deep Learning, GenAI, Power BI/Tableau and Cloud.
A plain-English definition of data science and the problems it solves.
How the field evolved and where demand is heading through 2026.
Where each discipline starts and ends, and how they overlap.
The six phases from business understanding to deployment.
Descriptive, diagnostic, predictive and prescriptive analytics explained.
How AI and GenAI fit into the modern data scientist's toolkit.
Real use cases in healthcare, finance, retail and marketing.
Fairness, transparency and the responsibilities of building models.
Analyst, scientist, AI engineer and MLOps roles compared.
The five statement categories and when you use each.
COUNT, SUM, AVG, MIN and MAX with grouped results.
Formatting, extracting and transforming text and dates.
Combining and comparing result sets across queries.
INNER, LEFT, RIGHT, FULL, SELF and CROSS joins with examples.
Nested queries and readable Common Table Expressions.
Virtual tables and how indexes speed up reads.
ROW_NUMBER, RANK, LEAD and LAG for analytics queries.
Reading execution and avoiding common slow-query traps.
Variables, data types and operators for data work.
Lists, tuples, dictionaries and sets in practice.
if / elif / else for branching logic.
for, while and Pythonic comprehensions.
Reusable code and anonymous functions.
Reading and writing CSV, JSON and text files.
Arrays, vectorised maths and broadcasting.
Series, DataFrames, cleaning, merging and grouping.
Line, bar, histogram, scatter and heatmap plots.
Mean, median, mode, variance and standard deviation.
Shape of data and the bell curve.
Conditional probability and updating beliefs.
Binomial, Poisson and normal distributions.
Why sample means become normal at scale.
p-value, t-test, chi-square and ANOVA.
Measuring how two variables move together.
Linear and logistic regression fundamentals.
The core tradeoff behind model error.
MAE, MSE, RMSE and R-squared explained.
Types, workflow and the train-test split.
Simple, multiple and polynomial regression.
Classification built on the sigmoid function.
Tree splits, ensembles and feature importance.
Instance-based classification and regression.
Fast probabilistic classification.
Margins, support vectors and the kernel trick.
Gradient boosting for tabular performance.
Unsupervised grouping with centroids.
Building clusters as a tree of merges.
Dimensionality reduction with variance.
Cross-validation, confusion matrix and ROC-AUC.
Neurons, activations and network architecture.
How networks learn via gradients.
Building and training your first model.
Image data, filters and pooling.
Memory over sequences.
Gated units for long-range dependencies.
Reusing pre-trained models for new tasks.
Generator vs discriminator training.
Transformer-based models and how they work.
LLMs that plan, use tools and act.
LLMs, GPT and Gemini in a data context.
Prompts that generate SQL, Python and explanations.
Calling OpenAI and Gemini programmatically.
Content generation with LLMs.
Grounding LLMs with vector databases.
Chat with documents using embeddings.
Natural-language querying of your data.
Guardrails for production GenAI.
Connecting and exploring data in Tableau.
Bar, line, scatter, heatmap and geographic views.
Derived measures, parameters and interactivity.
Composing and publishing dashboards.
Navigation and the report canvas.
Shaping and cleaning data in Power BI.
Relationships and star schema basics.
Measures and calculated columns.
Interactive reports and filters.
IaaS, PaaS and SaaS for data workloads.
S3, EC2 and IAM for data scientists.
Shipping a data app to the cloud.
Wrapping models as REST APIs.
Versioning, pipelines and monitoring.
Collaboration and reproducibility.
Free-tier deployment options.
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