All programsAenexz Tech
Industry Capstone
4.8
Data Science & Analytics
Python, Pandas, NumPy, Matplotlib, Seaborn, Plotly, Scikit-learn, statistics and ML — 10+ micro-projects plus an industry capstone (churn, fraud or forecasting).
Duration
8 Weeks
Level
Beginner
Prerequisites
Basic Computer Skills
Curriculum
Week 11 Week
Intro to Data Science & Python Basics
Set up Jupyter, learn the DS workflow and Python essentials.
- Data science lifecycle and roles
- Jupyter Notebook and Google Colab setup
- Python data types, control flow, functions
- Libraries setup: NumPy, Pandas, Matplotlib
- Reading CSV, Excel and JSON data
Week 21 Week
Data Manipulation with Pandas & NumPy
Wrangle real datasets end-to-end.
- NumPy arrays, broadcasting and vectorised math
- Pandas Series, DataFrame and indexing
- Cleaning missing / duplicate data
- Filtering, grouping, joining and aggregating
- Micro-project: CSV analysis + data cleaning
Week 31 Week
Data Visualization — Matplotlib, Seaborn & Plotly
Turn data into insight-driven charts and dashboards.
- Matplotlib fundamentals and subplots
- Seaborn statistical plots and heatmaps
- Plotly interactive charts and dashboards
- Storytelling with data
- Micro-project: Customer Behaviour Analysis
Week 41 Week
Statistics — Descriptive, Distributions & A/B Testing
The math foundation every data scientist needs.
- Descriptive statistics: mean, median, variance
- Probability distributions (Normal, Binomial, Poisson)
- Hypothesis testing and p-values
- A/B testing and confidence intervals
- Micro-project: Hypothesis testing + regression intro
Week 51 Week
ML Intro — Supervised & Unsupervised Learning
Build first models with Scikit-learn.
- Supervised vs unsupervised learning
- Linear and Polynomial Regression
- Regression evaluation: MAE, MSE, RMSE, R²
- Overfitting, underfitting and regularisation
- Train/test split and pipelines
Week 61 Week
Classification — Logistic Reg, Trees, Random Forest, SVM
Master the most-used classifiers in industry.
- Logistic Regression and Decision Trees
- Random Forest and ensemble intuition
- Support Vector Machines
- Naive Bayes for text problems
- Precision, recall, F1 and ROC-AUC
Week 71 Week
Model Selection, Clustering & PCA
Tune, validate and reduce dimensions.
- Cross-validation strategies
- Grid Search and Randomised Search
- K-Means clustering and elbow method
- PCA for dimensionality reduction
- Feature importance and interpretability
Week 81 Week
Major Project — Churn / Fraud / Forecasting
Full end-to-end industry capstone with presentation.
- Choose: Customer Churn / Fraud Detection / Sales Forecast
- EDA and feature engineering on real data
- Model build, evaluation and iteration
- Business insights and recommendations
- Presentation and certification review
Tools & Technologies
PythonJupyterGoogle ColabGitHubNumPyPandasMatplotlibSeabornPlotlyScikit-learnPower BI (intro)ExcelKaggle
Capstone Projects
Capstone
Customer Churn / Sales Forecasting / Fraud Detection
Micro
