Data Science
Overview
Our Data Science course offers a comprehensive overview of foundational and advanced concepts. It starts with an in-depth introduction to Python, covering basic syntax, data types, and essential libraries like NumPy and Jupyter Notebooks. The course then delves into statistical methods, exploratory data analysis (EDA), and machine learning techniques, including supervised, unsupervised, and semi-supervised learning. Advanced topics such as neural networks and deep learning are also covered, equipping students with the skills to tackle real-world data challenges through practical exercises and projects.
The program ends with a graduation project, where students apply what they’ve learned in a practical, real-world scenario. This project is an opportunity to demonstrate your skills and understanding of data engineering principles.
Course Details
Program Length: 8 weeks
Class Length: 2 hours
Original Price: 1200 €
With Growth Labs Academy Scholarship: 799 €*
Outcomes
By the end of this course, you will be proficient with:
- Gain proficiency in Python, Jupyter Notebooks, and NumPy.
- Develop a solid understanding of statistical methods, EDA, and data visualization.
- Apply machine learning and deep learning concepts, including neural networks, to solve real-world data challenges.
Please ensure to have a computer that meets the specified requirements:
- Supported operating systems: macOS, Linux, or Windows (Pro edition required).
- Latest OS version, fully up to date.
- All security updates installed.
- At least 100GB of free space on the hard drive.
- At least 16GB of RAM, 32GB RAM is strongly preferred.
- Support for video conferencing and screen-sharing, with a reliable webcam and microphone.
Python
- Intro to Python, basic syntax, data types, variable declarations, conditions, loops and strings
- Data structures, lists, tuples and ranges
- Dictionaries and sets
- Functions and modules
- Virtual Environments
Data Science – Introduction
- What is Data science?
- Where is Data Science Needed?
- Real-world examples
- What Data Scientist work?
Data Science Python
- Linear functions
- Plotting functions in Python
- Slope and intercept
Data Science – Statistics
- Percentiles
- Standard deviation
- Variance
- Correlation
- Correlation Matrix
- Correlation vs Causality
Data Science – Advanced
- Linear Regression
- Regression table
- Regression info
- Regression Coefficients
- Regression P-Value
- Regression R-Squared
- Linear Regression Case
Exploratory Data Analysis (EDA)
- Introduction to exploratory data analysis
- Data Cleaning and Preprocessing
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
- Feature Engineering
Machine Learning
- Data
- Features and Labels
- Algorithms
- Model Parameters
- Training Models
- Testing
- Validation
- Hyperparameters
- Loss Functions
- Evaluation Metrics
- Overfitting and Underfitting
- Bias and Variance
- Feature Engineering
Machine learning in practice
- Understanding the domain
- Clear goal definition
- Data quality
- Data integration
- Learning models
- Result interpretation
- Knowledge deployment
Deep Learning
- Neural Networks
- Deep Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer Learning
To successfully pass the class, students should aim to reach a minimum of 90% of the available points. We’ve created a flexible environment which will enable you to have the best learning experience and elevate you on to greater heights.
Punctuality, participation in discussions, completion of assignments, and demonstration of professional courtesy to others are required, in accordance with our Code of Conduct. Attendance will be taken at the beginning of every class. Passing requires at least 90% attendance. Students should always contact the instructors ahead of time if they are unable to attend all or part of the published class/lab hours.
