Overview – Duration: 6 months Class Schedule: Fridays and Saturdays – Total Hours per Week: 6 hours – Total Weeks: 36 weeks – Total Hours: 216 hours – Credit Units: Each module is assigned credit units, with 1 credit unit equaling approximately 10 hours of contact time.
Course Description:
The Diploma in Data Science is a comprehensive 6-month program designed to equip students with in-depth knowledge and practical skills in data science, advanced analytics, machine learning, and deep learning techniques. Through a structured and immersive curriculum, participants will gain mastery over essential programming tools, statistical analysis, data visualization, and modern machine learning models. This program builds on foundational data science concepts, progressively introducing advanced topics such as ensemble learning, deep learning frameworks (CNNs, RNNs, LSTMs), and time series modeling. The course concludes with a capstone project, allowing students to apply their knowledge to real-world data science challenges, providing both technical and domain-specific solutions. Students will engage in rigorous assignments, hands-on projects, and in-class exercises across various data science domains, preparing them for a career in industries such as healthcare, finance, e-commerce, and more. By the end of this course, graduates will have the skills to effectively analyze complex datasets, develop machine learning models, and present actionable insights to stakeholders.
What You Will Learn:
By the end of this 6-month Diploma in Data Science, participants will be able to:
- Master Python for Advanced Data Science:
- Apply advanced Python programming techniques, including object-oriented programming, regular expressions, and working with large datasets.
- Utilize Python libraries (Pandas, NumPy, Matplotlib, Seaborn) for efficient data analysis and visualization.
- Perform Advanced Data Wrangling and Visualization:
- Clean, manipulate, and preprocess large and complex datasets for analysis.
- Create advanced data visualizations to communicate insights using Matplotlib, Seaborn, and Plotly.
- Understand Statistical Analysis and Probability:
- Apply inferential statistics, probability distributions, and hypothesis testing to derive insights from data.
- Perform regression analysis and identify trends and patterns in data.
- Implement Machine Learning Models:
- Build and evaluate machine learning models using techniques such as supervised and unsupervised learning, classification, and clustering.
- Explore advanced machine learning algorithms, including ensemble methods (Bagging, Boosting), XGBoost, and LightGBM.
- Fine-tune models using cross-validation, grid search, and other optimization techniques.
- Gain Expertise in Deep Learning:
- Build, train, and optimize deep neural networks using frameworks like TensorFlow and Keras.
- Implement convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) for sequence modeling.
- Explore the applications of deep learning in real-world scenarios, such as healthcare and natural language processing.
- Analyze Time Series and Sequential Data:
- Develop and apply models to analyze time series data and make forecasts.
- Use LSTMs and GRUs for advanced sequence modeling tasks like stock price prediction and sentiment analysis.
- Work on Real-World Capstone Projects:
- Complete a capstone project, solving a real-world problem through data collection, preprocessing, model building, and evaluation.
- Present findings through a combination of technical reports, visualizations, and stakeholder presentations.
This diploma offers not only theoretical depth but also practical experience, ensuring participants graduate as proficient and confident data scientists ready to tackle complex data-driven challenges in the real world.
