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Data Science Notes
Data, Python, statistics, linear regression and case studies — written by Coding Now Tech Institute's mentors. Free to read, structured to actually help you learn.
Data Science notes by Coding Now 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 Coding Now Tech Institute's mentor-led Data Science course in Pitampura, Delhi.
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Data Science
8 of 8 topics publishedDS HOME
With our "Try it Yourself" editor, you can edit Python code and view the result.
DS Introduction
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
DS What is Data
One purpose of Data Science is to structure data, making it interpretable and easy to work with.
DS Database Table
A database table is a table with structured data.
DS Python
Python is a programming language widely used by Data Scientists.
DS DataFrame
A data frame is a structured representation of data.
DS Functions
This chapter shows three commonly used functions when working with Data Science: max(), min(), and mean().
DS Data Preparation
Before analyzing data, a Data Scientist must extract the data, and make it clean and valuable.
DS Math
3 of 3 topics publishedDS Linear Functions
Mathematical functions are important to know as a data scientist, because we want to make predictions and interpret them.
DS Plotting Functions
| Duration | Average_Pulse | Max_Pulse | Calorie_Burnage | Hours_Work | Hours_Sleep |
DS Slope and Intercept
Now we will explain how we found the slope and intercept of our function:
DS Statistics
7 of 7 topics publishedStat Introduction
Statistics is the science of analyzing data.
Stat Percentiles
Percentiles are used in statistics to give you a number that describes the value that a given percent of the values are lower than.
Stat Standard Deviation
Standard deviation is a number that describes how spread out the observations are.
Stat Variance
Variance is another number that indicates how spread out the values are.
Stat Correlation
Correlation measures the relationship between two variables.
Stat Correlation Matrix
A matrix is an array of numbers arranged in rows and columns.
Stat Correlation vs Causality
Correlation measures the numerical relationship between two variables.
DS Advanced
7 of 7 topics publishedDS Linear Regression
We are missing one important variable that affects Calorie_Burnage, which is the Duration of the training session.
DS Regression Table
The output from linear regression can be summarized in a regression table.
DS Regression Info
The "Information Part" in Regression Table
DS Regression Coefficients
The "Coefficients Part" in Regression Table
DS Regression P-Value
The "Statistics of the Coefficients Part" in Regression Table
DS Regression R-Squared
R-Squared and Adjusted R-Squared describes how well the linear regression model fits the data points:
DS Linear Regression Case
Case: Use Duration + Average\_Pulse to Predict Calorie\_Burnage
More Data Science Notes
84 topicsMaster's Program in Data Science (2026): Complete Roadmap
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.
What Is Data Science?
A plain-English definition of data science and the problems it solves.
History & Future of Data Science
How the field evolved and where demand is heading through 2026.
Data Science vs Business Intelligence vs AI
Where each discipline starts and ends, and how they overlap.
The Data Science Lifecycle (CRISP-DM)
The six phases from business understanding to deployment.
Types of Analytics
Descriptive, diagnostic, predictive and prescriptive analytics explained.
Introduction to AI & Generative AI
How AI and GenAI fit into the modern data scientist's toolkit.
Data Science Applications by Industry
Real use cases in healthcare, finance, retail and marketing.
Ethics, Bias & Responsible AI
Fairness, transparency and the responsibilities of building models.
Data Science Career Paths
Analyst, scientist, AI engineer and MLOps roles compared.
SQL DDL, DML, DCL, TCL & DQL
The five statement categories and when you use each.
SQL Aggregate Functions
COUNT, SUM, AVG, MIN and MAX with grouped results.
SQL Date & String Functions
Formatting, extracting and transforming text and dates.
UNION, UNION ALL, INTERSECT & EXCEPT
Combining and comparing result sets across queries.
SQL Joins
INNER, LEFT, RIGHT, FULL, SELF and CROSS joins with examples.
SQL Subqueries & CTEs
Nested queries and readable Common Table Expressions.
SQL Views & Indexes
Virtual tables and how indexes speed up reads.
SQL Window Functions
ROW_NUMBER, RANK, LEAD and LAG for analytics queries.
SQL Query Optimization Basics
Reading execution and avoiding common slow-query traps.
Python Basics
Variables, data types and operators for data work.
Python Data Structures
Lists, tuples, dictionaries and sets in practice.
Conditional Statements in Python
if / elif / else for branching logic.
Loops & List Comprehensions
for, while and Pythonic comprehensions.
Functions, Lambda & Scope
Reusable code and anonymous functions.
File Handling in Python
Reading and writing CSV, JSON and text files.
NumPy for Data Science
Arrays, vectorised maths and broadcasting.
Pandas for Data Science
Series, DataFrames, cleaning, merging and grouping.
Matplotlib & Seaborn
Line, bar, histogram, scatter and heatmap plots.
Descriptive Statistics
Mean, median, mode, variance and standard deviation.
Skewness, Kurtosis & Normal Distribution
Shape of data and the bell curve.
Probability & Bayes' Theorem
Conditional probability and updating beliefs.
Probability Distributions
Binomial, Poisson and normal distributions.
Sampling & the Central Limit Theorem
Why sample means become normal at scale.
Hypothesis Testing
p-value, t-test, chi-square and ANOVA.
Correlation & Covariance
Measuring how two variables move together.
Regression Analysis
Linear and logistic regression fundamentals.
Bias-Variance, Overfitting & Underfitting
The core tradeoff behind model error.
Error Metrics
MAE, MSE, RMSE and R-squared explained.
Introduction to Machine Learning
Types, workflow and the train-test split.
Linear Regression
Simple, multiple and polynomial regression.
Logistic Regression
Classification built on the sigmoid function.
Decision Trees & Random Forest
Tree splits, ensembles and feature importance.
K-Nearest Neighbors (KNN)
Instance-based classification and regression.
Naive Bayes Classifier
Fast probabilistic classification.
Support Vector Machines (SVM)
Margins, support vectors and the kernel trick.
XGBoost & AdaBoost
Gradient boosting for tabular performance.
K-Means Clustering
Unsupervised grouping with centroids.
Hierarchical Clustering
Building clusters as a tree of merges.
Principal Component Analysis (PCA)
Dimensionality reduction with variance.
Model Evaluation
Cross-validation, confusion matrix and ROC-AUC.
Neural Networks Introduction
Neurons, activations and network architecture.
Forward & Backward Propagation
How networks learn via gradients.
TensorFlow & Keras Basics
Building and training your first model.
Convolutional Neural Networks (CNNs)
Image data, filters and pooling.
Recurrent Neural Networks (RNNs)
Memory over sequences.
LSTMs & GRUs
Gated units for long-range dependencies.
Transfer Learning
Reusing pre-trained models for new tasks.
Generative Adversarial Networks (GANs)
Generator vs discriminator training.
Large Language Models (LLMs)
Transformer-based models and how they work.
Introduction to Agentic AI
LLMs that plan, use tools and act.
What Is Generative AI?
LLMs, GPT and Gemini in a data context.
Prompt Engineering for Data Analysis
Prompts that generate SQL, Python and explanations.
Using LLM APIs with Python
Calling OpenAI and Gemini programmatically.
Building a Text Summarizer
Content generation with LLMs.
Retrieval-Augmented Generation (RAG)
Grounding LLMs with vector databases.
PDF Q&A Bot with an LLM
Chat with documents using embeddings.
AI Chatbot for Data Insights
Natural-language querying of your data.
Ethical AI: Bias, Hallucination & Privacy
Guardrails for production GenAI.
Tableau Interface & Connections
Connecting and exploring data in Tableau.
Tableau Charts & Maps
Bar, line, scatter, heatmap and geographic views.
Tableau Calculated Fields & Filters
Derived measures, parameters and interactivity.
Tableau Dashboards & Stories
Composing and publishing dashboards.
Power BI Desktop Interface
Navigation and the report canvas.
Power Query Data Transformation
Shaping and cleaning data in Power BI.
Power BI Data Modeling
Relationships and star schema basics.
DAX Basics
Measures and calculated columns.
Power BI Visualizations & Slicers
Interactive reports and filters.
Introduction to Cloud Computing
IaaS, PaaS and SaaS for data workloads.
AWS Core Services
S3, EC2 and IAM for data scientists.
Deploying a Streamlit App on EC2
Shipping a data app to the cloud.
Model Serving with Flask & FastAPI
Wrapping models as REST APIs.
Introduction to MLOps
Versioning, pipelines and monitoring.
GitHub for Code Versioning
Collaboration and reproducibility.
Deploy on Streamlit Cloud & Render
Free-tier deployment options.
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