Curriculum
Eureka Data Academy Bootcamp teaches highly applicable skills for data analysis and visualization that can benefit professionals and companies in any industry.
It’s a fact: Companies care about what you can do, not what you say you can do. For that reason, our curriculum teaches you how to put what you’ve learned to work on real-world data analysis projects, from visualizing bike-sharing data in New York City to mapping earthquakes worldwide in real time. Our students learn how to navigate, engineer, and translate complex data systems into useful resources that employers need.
Beginner Level Syllabus
This 12-week course is designed to introduce you to the fundamental concepts and techniques in data science. It covers the basics of Python programming, data manipulation, and visualization, building a strong foundation for more advanced topics.
Introduction To Data Science:Week 1
Description:
What You Will Learn:
Understand the basics of Data Science, its applications, and the tools used in the field.
- Introduction to Data Science and its applications
- Overview of the data science pipeline
- Basic Python programming
- Tools: Jupyter Notebook, Anaconda
Basic Python ProgrammingWeek 2
Description:
What You Will Learn:
Learn Python syntax, data types, control structures, and essential libraries for data science.
- Python syntax and data types
- Control structures and functions
- Libraries for data science: pandas, NumPy
Data Types And StructuresWeek 3
Description:
What You Will Learn:
Dive into pandas for data manipulation, focusing on data frames, series, and basic operations.
- Introduction to pandas
- Data frames, series, and operations
- Basic data manipulation
Data VisualizationWeek 4
Description:
What You Will Learn:
Master data visualization techniques using Matplotlib and Seaborn to create and customize plots.
- Data visualization with Matplotlib
- Advanced visualization with Seaborn
- Creating and customizing plots
Descriptive StatisticsWeek 5
Description:
What You Will Learn:
Explore measures of central tendency, dispersion, and data distribution visualization.
- Measures of central tendency
- Measures of dispersion
- Data distribution and visualization
Probability TheoryWeek 6
Description:
What You Will Learn:
Understand basic probability concepts, conditional probability, and probability distributions.
- Basic probability concepts
- Conditional probability
- Probability distributions
Hypothesis TestingWeek 7
Description:
What You Will Learn:
Learn about hypothesis testing, types of errors, confidence intervals, and p-values.
- Null and alternative hypotheses
- Types of errors
- Confidence intervals and p-values
Introduction To Machine LearningWeek 8
Description:
What You Will Learn:
Get introduced to machine learning, including supervised and unsupervised learning, and basics of regression and classification.
- Supervised vs. unsupervised learning
- Regression basics: Linear Regression
- Classification basics: Logistic Regression
Data WranglingWeek 9
Description:
What You Will Learn:
Techniques for handling missing data, data transformation, scaling, and an introduction to SQL for data querying.
- Handling missing data
- Data transformation and scaling
- Introduction to SQL for data querying
Feature EngineeringWeek 10
Description:
What You Will Learn:
Learn techniques for feature selection, extraction, and dimensionality reduction.
- Feature selection techniques
- Feature extraction methods
- Dimensionality reduction
Model EvaluationWeek 11
Description:
What You Will Learn:
Understand model evaluation metrics, cross-validation techniques, and model tuning and optimization.
- Model evaluation metrics
- Cross-validation techniques
- Model tuning and optimization
Capstone ProjectWeek 12
Description:
What You Will Learn:
Apply your knowledge in a capstone project involving project planning, data collection, preprocessing, model building, and evaluation.
- Project planning and scope definition
- Data collection and preprocessing
- Model building and evaluation
- Presentation and reporting
Intermediate Level Syllabus
This 12-week intensive program is designed to deepen your understanding of advanced data science techniques and tools. It will cover various aspects of data science, from advanced Python programming to neural networks and cloud computing. The course culminates in a capstone project where you will apply what you have learned to a real-world problem.
Advance Python TechniquesWeek 1
Description:
What You Will Learn:
This week covers advanced Python concepts, including list comprehensions, lambda functions, error handling, and debugging.
- Advanced Python concepts
- List comprehensions and lambda functions
- Error handling and debugging
Data Visualization And ReportingWeek 2
Description:
What You Will Learn:
Learn how to create effective visualizations and tell stories with your data using Matplotlib and Seaborn.
- Introduction to data visualization
- Creating effective visualizations with Matplotlib and Seaborn
- Reporting and storytelling with data
Advanced Data Analysis With PandasWeek 3
Description:
What You Will Learn:
Dive into advanced functionalities of pandas for data wrangling, manipulation, and time series analysis.
- Advanced pandas functionalities
- Data wrangling and manipulation
- Time series analysis
Data Manipulation With SqlWeek 4
Description:
What You Will Learn:
Master SQL for data analysis, including advanced queries and techniques for joining and merging datasets.
- SQL for data analysis
- Advanced SQL queries
- Joining and merging datasets
Bayesian StatisticsWeek 5
Description:
What You Will Learn:
Explore Bayesian statistics, including Bayes' theorem, Bayesian inference, and statistical modeling.
- Bayes' theorem and applications
- Bayesian inference
- Bayesian statistical modeling
Advanced Hypothesis TestingWeek 6
Description:
What You Will Learn:
Learn about parametric and non-parametric tests, ANOVA, chi-square tests, and resampling methods.
- Parametric and non-parametric tests
- ANOVA and chi-square tests
- Resampling methods: Bootstrap, Cross-validation
Advanced Regression TechniquesWeek 7
Description:
What You Will Learn:
This week covers advanced regression techniques, including Ridge and Lasso regression, and model selection.
- Ridge and Lasso regression
- Polynomial regression
- Model selection techniques
Ensemble MethodsWeek 8
Description:
What You Will Learn:
Discover ensemble methods such as decision trees, Random Forest, and Gradient Boosting.
- Decision trees
- Random Forest
- Gradient Boosting
Introduction To Neural NetworksWeek 9
Description:
What You Will Learn:
Get introduced to the basics of neural networks, activation functions, and building neural networks with Keras.
- Basics of neural networks
- Activation functions and loss functions
- Building neural networks with Keras
Big Data ConceptsWeek 10
Description:
What You Will Learn:
Understand the fundamentals of Big Data, including the Hadoop ecosystem and working with Spark.
- Introduction to Big Data
- Hadoop ecosystem
- Working with Spark
Cloud Computing For Data ScienceWeek 11
Description:
What You Will Learn:
Learn about cloud services, data storage solutions, and cloud-based machine learning.
- Cloud services overview (AWS, Google Cloud)
- Data storage solutions
- Cloud-based machine learning
Capstone ProjectWeek 12
Description:
What You Will Learn:
Apply your knowledge in a capstone project involving project planning, data collection, advanced analysis, and model deployment.
- Project planning and data collection
- Advanced data analysis and modeling
- Model deployment and performance monitoring
- Presentation and reporting
Advanced Level Syllabus
This 12-week advanced program focuses on deep learning, neural networks, reinforcement learning, and generative AI. It is designed for those who have a solid understanding of data science and wish to delve deeper into advanced topics and techniques.
Deep Learning ConceptsWeek 1
Description:
What You Will Learn:
Gain an understanding of deep learning and neural network architectures.
- Introduction to deep learning
- Neural network architectures (CNN, RNN, LSTM)
- Model training and optimization
Neural Network ArchitecturesWeek 2
Description:
What You Will Learn:
Explore various neural network architectures including CNNs, RNNs, and LSTMs.
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM) networks
Transfer Learning And Fine-TuningWeek 3
Description:
What You Will Learn:
Learn the concepts of transfer learning and how to fine-tune pre-trained models.
- Concepts of transfer learning
- Fine-tuning pre-trained models
- Applications of transfer learning
Model Interpretability And ExplainabilityWeek 4
Description:
What You Will Learn:
Understand the importance of model interpretability and techniques for explaining models.
- Importance of model interpretability
- Techniques for explaining models
- Case studies on model interpretability
Text Preprocessing And Feature ExtractionWeek 5
Description:
What You Will Learn:
Master text preprocessing, cleaning, normalization, tokenization, and feature extraction.
- Text cleaning and normalization
- Tokenization and vectorization
- Feature extraction methods
Advanced Nlp TechniquesWeek 6
Description:
What You Will Learn:
Dive into advanced NLP techniques including sequence models, word embeddings, and transformers.
- Sequence models
- Word embeddings (Word2Vec, GloVe)
- Transformers and BERT
Image Preprocessing And AugmentationWeek 7
Description:
What You Will Learn:
Learn techniques for image data augmentation and preprocessing for computer vision.
- Image data augmentation techniques
- Preprocessing for computer vision
- Building image datasets
Advanced Computer Vision TechniquesWeek 8
Description:
What You Will Learn:
Explore advanced computer vision techniques like object detection, segmentation, and transfer learning.
- Object detection and segmentation
- Transfer learning in computer vision
- Advanced applications in computer vision
Introduction To Reinforcement LearningWeek 9
Description:
What You Will Learn:
Get introduced to the basics of reinforcement learning, Markov Decision Processes, and Q-learning.
- Basics of reinforcement learning
- Markov Decision Processes (MDP)
- Q-learning and Deep Q Networks (DQN)
Advanced Topics In Reinforcement LearningWeek 10
Description:
What You Will Learn:
Explore advanced reinforcement learning algorithms and their applications.
- Policy gradients
- Advanced reinforcement learning algorithms
- Applications of reinforcement learning
Llms And Generative AiWeek 11
Description:
What You Will Learn:
Learn about Large Language Models, generative models, and their applications and ethical considerations.
- Introduction to Large Language Models (LLMs)
- Training and fine-tuning LLMs
- Generative models: GANs, VAEs
- Applications and ethical considerations of generative AI
Capstone ProjectWeek 12
Description:
What You Will Learn:
Apply your advanced knowledge in a capstone project involving complex problem definition, data collection, advanced modeling, and comprehensive presentation.
- Defining a complex data science problem
- Advanced data collection and preprocessing
- Developing and deploying advanced models
- Comprehensive project presentation and documentation
Student's Testimony and Review
The program helped me build my team work skills. We had to work in a group for the final project. Working with different people from different parts of the world was a nice experience.
Ifeoma
I am Godspower Uyanga, the Founder and CEO of Gworldsoft Solutions Limited, a leading software company specializing in training for Data Analysis, Data Science, Machine Learning, Data Engineering, Frontend Development, Backend Development, Cybersecurity, UI/UX Design, research, and more. I joined DYLA Training in Data Science and Machine Learning in 2022, which significantly helped me gain valuable experience. After completing the training, I was given a certificate and a coach, with a wealth of experience, this helped me register my company as a Data Science and machine learning solutions firm before expanding our offerings as a software company. This bootcamp training is the key to standing out in your tech career.
Godspower Uyanga, 2022 Bootcamp Graduate