Python for Data Science & Machine Learning

From raw data to working machine-learning models: NumPy, pandas (version 3), data cleaning, groupby and joins, charts with matplotlib, the statistics you actually need, and scikit-learn for regression, classification, model evaluation and clustering — with real-world case studies and an end-to-end placement-prediction project. Every output and every chart on these pages is produced by the code shown.

1. 01What is Data Science? Roles, the Lifecycle and the Python ToolkitWhat data scientists, data analysts and ML engineers actually do, the six-step data science lifecycle, the Python libraries you will use, how to set up Jupyter, and a case study of a real business question answered with data.
2. 02NumPy — Arrays, Vectorised Maths, Broadcasting and Boolean FilteringWhy NumPy is fast, creating arrays, shapes and dtypes, vectorised maths without loops, broadcasting, indexing and slicing 2-D arrays, boolean masks, and the aggregations every data scientist uses daily.
3. 03pandas Basics — DataFrames, Reading CSV, Selecting, Filtering and SortingLearn pandas 3 the right way: Series and DataFrames, reading CSV files, inspecting data with head, info and describe, selecting columns and rows with loc and iloc, filtering with conditions, sorting and adding new columns.
4. 04Data Cleaning with pandas — Missing Values, Duplicates, Types, Text and DatesReal data is messy. Find and fix missing values, duplicates, wrong data types, inconsistent text and dates with pandas 3 — using a messy sign-up dataset as a case study, plus the copy-on-write rule that changed in pandas 3.
5. 05Analysing Data with pandas — groupby, Aggregations, merge (Joins) and Pivot TablesAnswer real questions with pandas: groupby with split-apply-combine, several aggregations at once, joining tables with merge (inner, left, outer), and pivot tables — through a college placement-report case study.
6. 06Data Visualisation with matplotlib — Bar, Line, Histogram, Scatter and Telling a StoryMake clear charts with matplotlib: choosing the right chart, bar charts, line charts for trends, histograms for distributions, scatter plots for relationships, subplots, and the design rules that make a chart convince a manager.
7. 07Statistics for Data Science — Averages, Spread, Distributions, Correlation and A/B TestsThe statistics you actually use at work: mean vs median, standard deviation and percentiles, the normal distribution, sampling, correlation vs causation, and A/B testing with a simple significance check — explained with tested Python code.
8. 08Machine Learning Basics with scikit-learn — Train/Test Split and Linear RegressionWhat machine learning really is, supervised vs unsupervised, features and targets, why we split data into train and test sets, and your first model — predicting packages with linear regression and judging it with MAE and R².
9. 09Classification — Logistic Regression, Decision Trees, Confusion Matrix, Precision and RecallPredict categories (placed or not, fraud or genuine) with logistic regression and decision trees, read a confusion matrix, and choose between accuracy, precision and recall — with a placement-prediction case study and an imbalanced-data trap.
10. 10Better Models — Overfitting, Cross-Validation, Pipelines, Scaling and Hyperparameter TuningWhy models that ace the training data fail in real life, how to see overfitting, cross-validation for honest scores, scaling and encoding inside a scikit-learn Pipeline (no data leakage), and tuning with GridSearchCV.
11. 11Unsupervised Learning — K-Means Clustering, the Elbow Method and PCAFind natural groups in data without labels: k-means clustering, choosing k with the elbow method, scaling before clustering, describing each segment for a business team, and PCA to compress many features into two — with a learner-segmentation case study.
12. 12End-to-End Data Science Project and Interview QuestionsPut everything together in one project — from question to a saved, reusable model and a one-paragraph recommendation — then revise the data science and machine learning questions asked in fresher interviews, with short answers.