Data Science: An Integrated Perspective

Data science turns raw data into answers you can act on: which products sell together, which customers are about to leave, what next month's demand looks like. It combines programming, statistics and a clear question, and in 2026 it also includes working with large language models. This guide maps the whole field, from the languages and maths to machine learning, data collection and visualisation, with a practical learning path at the end.

1. The data science workflow

2. Programming languages

If you learn only two, learn Python and SQL. pandas is the standard table library; Polars is a faster alternative for large files; DuckDB runs SQL directly on CSV and Parquet files on your laptop.

3. The mathematics you actually need

• Statistics and probability: distributions, averages and spread, sampling, confidence intervals, hypothesis tests and, above all, the difference between correlation and causation.
• Linear algebra: vectors and matrices, which is how data and models are represented; the basis of techniques such as principal component analysis (PCA).
• Calculus: derivatives and gradients, which is how models are trained by gradient descent.
• Discrete maths and algorithms: graphs, logic and complexity, which help you write code that finishes in reasonable time.

You don't need to derive every formula, but you should understand what a method assumes and when its answer can't be trusted.

4. Data analysis: wrangling, features and exploration

Data wrangling turns messy input into a clean table: consistent dates and units, missing values handled deliberately, duplicates removed, sources joined correctly.

Exploratory data analysis (EDA) means summary statistics and plots of each column and of pairs of columns, looking for skew, outliers, errors and patterns.

Feature engineering creates the inputs a model learns from, for example "days since last order" from a list of order dates. Good features usually beat a more complex model.

A short pandas example that loads, cleans and summarises sales data:

import pandas as pd

df = pd.read_csv("sales.csv", parse_dates=["order_date"])
df = df.drop_duplicates().dropna(subset=["amount"])
df["month"] = df["order_date"].dt.to_period("M")

monthly = df.groupby("month")["amount"].agg(["count", "sum", "mean"])
print(monthly.tail(12))

5. Machine learning

Three habits matter more than the choice of algorithm:

• Split your data into training and test sets, and judge the model only on data it has never seen.
• Watch for leakage: a feature that secretly contains the answer, such as a "cancelled date" column when predicting cancellations, makes a model look perfect and fail in real use.
• Compare against a baseline, such as "predict last month's value". A model that doesn't beat it isn't useful.

Large language models (LLMs) are now part of the toolkit. Much practical work uses them through an API, or combines them with your own documents through retrieval, rather than training them from scratch. See integrating the OpenAI and Claude APIs. For a worked classification project, see building a spam classifier with scikit-learn.

6. Collecting data: APIs and web scraping

Prefer an official API or a data download whenever one exists: it is more reliable and clearly permitted. When you must scrape:

• requests + BeautifulSoup for simple HTML pages;
• Scrapy for larger crawls with queues, retries and pipelines;
• Playwright for pages built by JavaScript;
• Python's built-in urllib works for basic requests, but requests or httpx are easier.

Scrape responsibly: read the site's terms and robots.txt, identify your crawler, keep request rates low and cache what you download. If you collect personal data about people in India, the Digital Personal Data Protection Act, 2023 applies. This is general information, not legal advice; see our DPDP Act guide.

7. Visualisation and communication

Choose the chart for the question: lines for change over time, bars for comparing categories, scatter plots for relationships between two numbers. Label axes and units, and state where the data came from.

8. A practical learning path

1. Python basics and SQL. Write queries that join, filter and group.
2. pandas and plotting. Analyse a real dataset you care about, end to end.
3. Statistics. Distributions, sampling and testing, applied to your own analyses.
4. scikit-learn. Regression and classification with proper train/test splits.
5. A portfolio project, published as a notebook or small web app with a clear write-up.
6. Then specialise: deep learning, natural language processing and LLMs, time series or data engineering.

9. Running data projects on Domain India

• Shared hosting (cPanel): the Setup Python App tool offers Python 3.9, 3.11 and 3.12, which suits a small Flask or Django app that presents results. Model training and long-running jobs don't belong on shared hosting, which has per-account resource limits; cron jobs run at most every 4 minutes. See deploy a Python app on shared hosting.
• App Platform: Python apps run from a Dockerfile you provide, and plans include a PostgreSQL database. It does not support WebSockets, so Streamlit and Jupyter, which need them, don't fit; a Flask or Dash app over plain HTTP does. See App Platform getting started.
• VPS: self-managed with root access, suitable for Jupyter, scheduled pipelines, databases and scrapers. Keep Jupyter off the public internet and reach it through an SSH tunnel. No VPS plan lists a GPU, so train large neural networks elsewhere. See developing and deploying Python applications on a VPS.

The card shows the live price, excluding 18% GST.

Ready to put a data project online? Compare VPS plans for notebooks and pipelines, the App Platform for small Python web apps, or open a support ticket to ask which fits your project.