Data Science and Machine Learning with Python Certification in Oman

Python syntax, variables, data types

Conditional statements and loops

Functions and lambda functions

Lists, tuples, dictionaries, sets

Practice: Automate report filtering

Jupyter/Colab usage

NumPy arrays, indexing, reshaping

Vectorized operations

Use Case: Matrix operations for logistics

Load, clean, and merge data

Handling missing values

Filtering, slicing, groupby operations

Project: Merge Excel reports across departments

Mean, median, variance, std deviation

Skewness and kurtosis

Probability rules and distributions (normal, binomial)

Domain examples: Retail and HR

Central Limit Theorem

Confidence intervals

t-tests, chi-square, ANOVA

Correlation vs causation

Use Case: A/B testing for ecommerce

Histograms, barplots, boxplots

Heatmaps and correlation plots

Seaborn styling

Mini Project: Visualize retail sales

Feature engineering: scaling, encoding

Binning, outlier treatment

Practice: EDA on sales/finance dataset

Supervised vs unsupervised ML

ML workflow: split, train, evaluate

Metrics: accuracy, precision, recall, F1, ROC

Use Case: HR attrition model

Salary prediction using linear regression

Attrition classification with logistic regression

Confusion matrix, coefficients, ROC-AUC

Decision tree construction

Random forest ensemble

K-Nearest Neighbors (KNN)

GridSearchCV tuning

Use Case: Loan eligibility prediction

K-means clustering

Hierarchical clustering

PCA for feature reduction

Use Case: Customer segmentation

Connect CSV, Excel, and Python outputs

Power Query transformations

Visuals: cards, charts, slicers

Case: HR Dashboard (attrition analysis)

Data modeling

DAX calculated columns and measures

Multi-page dashboards

Case: Finance dashboard with forecasting

Export ML results to Power BI

Import predictions

Build ML-powered dashboards

Use Case: Telecom churn dashboard

**Sales performance predictor**

**HR attrition analyzer**

**Credit score or loan eligibility tool**