Data Science in Python

Hereby the curriculum per day:

• Day 1: Introduction to Python & Data Science Basics

  Topics Covered:

  • Introduction to Data Science: What is Data Science?
  • Overview of the Data Science Workflow
  • Setting up Python environment: Jupyter Notebook, Anaconda, etc.
  • Python basics: Syntax, Variables, Data Types (Strings, Lists, Tuples, Dictionaries)

  Hands-On Exercise:

  • Writing basic Python scripts
  • Simple data manipulations using lists and dictionaries

  Assignment:

  • Practise Python basics: write code that manipulates data structures (lists, dictionaries).

• Day 2: Data Handling and Manipulation with Pandas

  Topics Covered:

  • Introduction to Pandas
  • Reading data from CSV, Excel, and databases
  • DataFrames: Creating, Inspecting, and Manipulating
  • Data Cleaning: Handling missing data, duplicates, and outliers

  Hands-On Exercise:

  • Load a dataset and perform basic cleaning and exploration
  • Data transformations with Pandas

  Assignment:

  • Clean a sample dataset and generate a summary report of the data (mean, median, mode, missing values).

• Day 3: Data Exploration and Visualisation

  Topics Covered:

  • Introduction to data visualisation
  • Overview of Matplotlib and Seaborn
  • Creating line plots, bar plots, scatter plots, and histograms
  • Visualising relationships in data with pair plots, heatmaps, etc.

  Hands-On Exercise:

  • Visualise key trends in a sample dataset using different plot types

  Assignment:

  • Create a series of visualisations to explore a new dataset.

• Day 4: Introduction to Numpy and Data Analysis

  Topics Covered:

  • Introduction to Numpy: arrays, matrices, and basic operations
  • Indexing, slicing, and reshaping arrays
  • Mathematical operations on Numpy arrays
  • Data manipulation with Numpy: Filtering and Boolean indexing

  Hands-On Exercise:

  • Use Numpy to perform complex data manipulations and matrix operations

  Assignment:

  • Complete a data analysis task using Numpy to filter and process a dataset.

• Day 5: Statistics for Data Science

  Topics Covered:

  • Descriptive statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Probability distributions: Normal distribution, Binomial distribution, etc.
  • Introduction to Inferential Statistics: Hypothesis Testing (T-tests, Chi-square tests)
  • Correlation and Covariance

  Hands-On Exercise:

  • Compute basic statistical measures on a dataset using Pandas and Numpy
  • Conduct hypothesis tests on sample data

  Assignment:

  • Analyse the statistical properties of a dataset and report key findings.

• Day 6: Introduction to Machine Learning Concepts

  Topics Covered:

  • What is Machine Learning? Supervised vs. Unsupervised Learning
  • Introduction to Scikit-learn: a Python library for Machine Learning
  • Overview of Machine Learning Algorithms: Linear Regression, Logistic Regression, KNN
  • Data preprocessing: Scaling, encoding categorical data, splitting datasets

  Hands-On Exercise:

  • Implement basic regression models using Scikit-learn

  Assignment:

  • Build and evaluate a simple linear regression model on a dataset.

• Day 7: Supervised Learning - Classification & Regression

  Topics Covered:

  • Supervised Learning in-depth: Classification and Regression
  • Classification algorithms: Decision Trees, Random Forests, K-Nearest Neighbours
  • Model evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC Curve

  Hands-On Exercise:

  • Implement a classification algorithm and evaluate the model

  Assignment:

  • Build a classification model on a dataset and evaluate its performance using different metrics.

• Day 8: Unsupervised Learning & Course Conclusion

  Topics Covered:

  • Introduction to Unsupervised Learning: Clustering, Dimensionality Reduction
  • K-Means Clustering
  • Introduction to Principal Component Analysis (PCA)
  • Recap of the key concepts learned throughout the course

  Hands-On Exercise:

  • Perform clustering on a dataset and visualise the clusters

  Final Project:

  • Apply the concepts learned to a real-world dataset of your choice. Which could be your own data set. Complete a full analysis, from data cleaning and visualisation to building a predictive model.

• Course Wrap-Up

  • Review of course topics
  • Q&A session
  • Feedback collection