60 Hours · 6 Modules · Python · Machine Learning · Statistics

Data ScienceTraining

Clean raw data, analyze it, visualize it and turn it into a model. Build an end-to-end data science path that extends from Python to statistics, and from machine learning to model deployment and a capstone project.

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Project-Based Training
Career-Focused Path
MEB & International Certificate
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Don’t just analyze data; turn it into systems that produce predictions and decisions.

Send your application to start the data science path focused on Python, statistics and machine learning.

  • In-person or online training
  • MEB-Approved and International Certificate

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Curriculum

A clear learning path that moves from fundamentals to practice.

The six-module training moves from the data science ecosystem to Python, from data cleaning to statistics, and from machine learning to model deployment and the capstone project.

MODULE 01Introduction to Data Science & Python

Lay your analytical programming foundation.

Introduction to Data Science & Python
  • The data science ecosystem
  • Python installation and development environment
  • Python data structures
  • Control flow and loops
  • Functions and modules
  • Basic file operations
MODULE 02Data Analysis & Manipulation

Get raw data ready for analysis.

Data Analysis & Manipulation
  • Numerical computing with NumPy
  • Introduction to Pandas
  • Data cleaning
  • Missing data handling
  • Grouping and aggregation
  • Join / Merge
MODULE 03Data Visualization

Make analysis results easy to understand.

Data Visualization
  • Visualization principles
  • Matplotlib
  • Statistical charts
  • Interactive chart approach
  • Dashboard design
  • Data storytelling
MODULE 04Statistical Analysis

Draw reliable inferences from data.

Statistical Analysis
  • Descriptive statistics
  • Probability distributions
  • Hypothesis tests
  • Correlation and covariance
  • Confidence intervals
  • A/B tests
MODULE 05Machine Learning

Build prediction and classification models from data.

Machine Learning
  • Supervised / unsupervised learning
  • Linear regression
  • Logistic regression
  • Decision trees
  • KNN
  • Model evaluation metrics
MODULE 06Advanced Topics & Capstone Project

Turn the analysis into a working project.

Advanced Topics & Capstone Project
  • Dimensionality reduction
  • Clustering algorithms
  • Introduction to time series
  • Data science project life cycle
  • Model deployment
  • Capstone project
6 MODULES + CAPSTONE PROJECT

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By the End of the Program

Turn knowledge into real business output.

Turn the concepts you learn into technical, practical skills you can use in real scenarios.

✓
Process large data sets

Apply cleaning, merging and transformation processes with NumPy and Pandas.

✓
Make statistical inferences

Interpret data with hypothesis tests, correlation, confidence intervals and A/B tests.

✓
Visualize data

Communicate findings with charts, dashboards and data storytelling techniques.

✓
Develop machine learning models

Use regression, classification and model evaluation approaches.

✓
Create a capstone project

Build a portfolio project that applies the data science life cycle end to end.

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Turn raw data into decisions, predictions and products.

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