Advanced Data Science Program
Duration: 6-8 Weeks (20 Days)
Week 1: Data Fundamentals
• Day 1: Introduction to Data Science
- Understanding the role of data science in business.
- Overview of the training program and learning objectives.
• Day 2: Data Types and Sources
- Exploring structured and unstructured data.
- Identifying data sources within your organization.
• Day 3: Data Collection and Ethics
- Best practices for data collection.
- Ethical considerations in data handling.
Week 2: Data Preprocessing and Cleaning
• Day 1: Data Cleaning Techniques
- Strategies for handling missing data.
- Detecting and dealing with outliers.
• Day 2: Data Transformation
- Feature engineering: Creating new variables.
- Data scaling and normalization.
• Day 3: Advanced Data Cleaning
- Handling imbalanced datasets.
- Dimensionality reduction techniques.
Week 3: Exploratory Data Analysis (EDA)
• Day 1: EDA Foundations
- Descriptive statistics and data distributions.
- Visualizing data with Matplotlib and Seaborn.
• Day 2: Statistical Inference
- Hypothesis testing and confidence intervals.
- Correlation analysis and statistical tests.
• Day 3: Advanced EDA
- Time series analysis and forecasting.
- Data storytelling and visualization best practices.
Week 4: Machine Learning Essentials
• Day 1: Introduction to Machine Learning
- Supervised vs. unsupervised learning.
- Model evaluation and validation.
• Day 2: Regression and Classification
- Linear regression and logistic regression.
- Decision trees and random forests.
• Day 3: Model Optimization
- Hyperparameter tuning and grid search.
- Ensemble methods and model stacking.
Week 5: Deep Learning and Neural Networks
• Day 1: Introduction to Neural Networks
- Basics of artificial neural networks (ANNs).
- Activation functions and network architectures.
• Day 2: Convolutional Neural Networks (CNNs)
- Image data processing with CNNs.
- Transfer learning and fine-tuning pretrained models.
• Day 3: Recurrent Neural Networks (RNNs)
- Sequence data analysis with RNNs and LSTMs.
- Natural language processing (NLP) applications.
Week 6: Big Data and Advanced Analytics
• Day 1: Introduction to Big Data
- Overview of big data technologies (e.g., Hadoop, Spark).
- Handling large datasets and distributed computing.
• Day 2: Advanced Analytics
- Clustering and dimensionality reduction.
- Time series forecasting with ARIMA and Prophet.
• Day 3: Capstone Project Kickoff
- Introduction to the capstone project.
- Forming project teams and selecting real-world data challenges.
Week 7-8: Capstone Projects and Certification
• Project Development (2-4 Weeks)
- Teams work on their capstone projects with guidance from instructors.
- Regular progress reviews and consultations.
• Capstone Project Presentations and Certification
- Teams present their projects to company executives.
- Certification ceremony and discussions about future learning paths and career advancement.
Discover the World of Data Science and Analytics
Our comprehensive training program covers essential data science and analytics topics, from data fundamentals to machine learning, deep learning, big data, and hands-on capstone projects. Gain practical skills and knowledge to excel in the data-driven world."
Contact us to book this course