Python for Data Science – QUBIT INSTITUTE

About Course

A practical, portfolio‑oriented introduction to using Python to load, clean, explore, visualize, and model data. Learners write real code in Jupyter notebooks, analyze public datasets, and ship a mini‑project that demonstrates end‑to‑end data science workflow. Two delivery options are available: a 4‑week intensive for working professionals and an 8‑week paced track for students.

Python essentials for data work: syntax, data types, control flow, functions, virtual environments.
Jupyter + notebook workflows: reproducible analysis, markdown, project structure.
Data wrangling with pandas: importing CSV/Excel/JSON, joins, reshaping, missing data, feature engineering.
Numerical computing with NumPy: arrays, broadcasting, vectorization for speed.
Exploratory data analysis (EDA): descriptive stats, grouping, outlier handling, data profiling.
Visualization: Matplotlib/Seaborn/Plotly for univariate, bivariate, and multivariate visuals; best practices.
Introductory statistics for data science: distributions, sampling, confidence intervals, hypothesis testing.
Intro machine learning with scikit‑learn: problem framing, train/test split, regression, classification, metrics.
Data ethics and reproducibility: bias awareness, documentation, versioning.
Communication: turning analysis into clear insights with visuals and narratives.

Course Content

Module 1 — Python foundations for data
Get productive with Python quickly; write readable, reusable code.

Python syntax, variables, types (str, int, float, bool)
Lists, tuples, dicts, sets; slicing and comprehensions
Control flow (if/else, loops), functions, modules
Virtual environments and package management (pip/conda)
Good practices: naming, style, helpful built‑ins

Module 2 — Working like a data scientist in Jupyter
Reproducible analysis and clean notebook workflows.

Module 3 — Data ingestion and cleaning with pandas
Load, inspect, and make messy data usable

Module 4 — Combining and reshaping data
Build analysis‑ready tables from multiple sources.

Module 5 — Exploratory data analysis (EDA)
Understand structure, quality, and patterns before modeling.

Module 6 — Visualization for insight
Tell clear, accurate stories with charts.

Module 7 — Practical statistics for data science
Apply core stats to real questions.

Module 8 — Intro to machine learning with scikit‑learn
Frame problems, build baselines, and evaluate models.

Module 9 — Communicating results
From notebook to narrative.

Module 10 — Capstone mini‑project
End‑to‑end analysis on a real dataset with a clear question.

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About Course

What Will You Learn?

Course Content

Module 1 — Python foundations for data Get productive with Python quickly; write readable, reusable code.

Python syntax, variables, types (str, int, float, bool)

Lists, tuples, dicts, sets; slicing and comprehensions

Control flow (if/else, loops), functions, modules

Virtual environments and package management (pip/conda)

Good practices: naming, style, helpful built‑ins

Module 2 — Working like a data scientist in Jupyter Reproducible analysis and clean notebook workflows.

Jupyter notebooks vs. scripts; markdown and code cells

File structure for projects; data directories

Inline visuals; exporting to HTML/PDF

Versioning notebooks; using checkpoints

Module 3 — Data ingestion and cleaning with pandas Load, inspect, and make messy data usable

Reading CSV/Excel/JSON; preview, dtypes, memory usage

Selecting/filtering; boolean masks; query; assign

Handling missing data; deduplication; type conversions

Dates/times; categorical data; text columns

Tidy data principles; wide↔long reshaping (melt/pivot)

Module 4 — Combining and reshaping data Build analysis‑ready tables from multiple sources.

Joins/merges (inner/left/right/outer)

Concatenate/append; hierarchical indexes

GroupBy aggregations; window/rolling ops

Feature engineering: bins, ratios, encoded flags

Module 5 — Exploratory data analysis (EDA) Understand structure, quality, and patterns before modeling.

Descriptive stats; distributions; skew/kurtosis

Outliers and robust summaries

Correlations; pairwise exploration

Data profiling checklists and EDA templates

Module 6 — Visualization for insight Tell clear, accurate stories with charts.

Matplotlib/Seaborn basics and style guides

Univariate: hist, kde, box/violin

Bivariate: scatter, regplot, bar/line with CIs

Multivariate: faceting, heatmaps, pairplots

Plotly for interactive dashboards (intro)

Module 7 — Practical statistics for data science Apply core stats to real questions.

Sampling, CLT intuition

Confidence intervals; standard errors

Hypothesis testing (t‑test, chi‑square) and p‑values

Effect sizes and practical significance

Module 8 — Intro to machine learning with scikit‑learn Frame problems, build baselines, and evaluate models.

Problem types: regression vs. classification

Train/validation/test; cross‑validation

Pipelines and preprocessing (scaling, encoding)

Models: Linear/Logistic Regression, k‑NN, Decision Trees

Metrics: RMSE/MAE, accuracy, precision/recall, ROC‑AUC

Avoiding leakage; simple feature importance

Module 9 — Communicating results From notebook to narrative.

Structuring an analysis report (context→methods→findings→limits)

Visual design for stakeholders; annotation best practices

Reproducible exports; brief slide‑deck storytelling

Module 10 — Capstone mini‑project End‑to‑end analysis on a real dataset with a clear question.

Project scoping and success criteria

Data acquisition → cleaning → EDA → model/baseline → insights

Deliverables: polished notebook, 1‑page summary, 5‑slide readout

Optional: lightweight Streamlit app or interactive Plotly report

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