Master the Art of Data Science (6-Week Intensive)
Course Duration: 6 Weeks
Mentorship: Industry experts from top tech companies will guide you throughout the course.
Mode of Learning: Online, Self-paced + Weekly Live Sessions
Week 1: Introduction to Data Science & Python
- Introduction to Data Science: Overview of data science concepts, applications, and the data science lifecycle. Discussion of different roles in data science.
- Python for Data Science: Setting up the Python environment, data types, variables, control flow, functions, and introduction to key libraries: NumPy and Pandas.
- Data Wrangling Basics: Reading data from various sources (CSV, JSON, databases), basic data cleaning, and handling missing values.
- Project 1: Basic Data Exploration – Perform exploratory data analysis (EDA) on a simple dataset using Pandas and Python.
Outcome: Foundational knowledge of data science and Python, ability to perform basic data manipulation and exploration.
Week 2: Data Visualization & Statistical Analysis
- Data Visualization: Creating effective visualizations using libraries like Matplotlib and Seaborn to understand data patterns and trends.
- Statistical Analysis: Descriptive statistics, hypothesis testing, probability distributions, and basic statistical modeling.
- Introduction to SQL: Basic SQL queries for data retrieval and manipulation.
- Project 2: Data Visualization and Storytelling – Create visualizations to tell a story about a dataset and present findings.
Outcome: Proficiency in data visualization and statistical analysis techniques.
Week 3: Machine Learning Fundamentals
- Supervised Learning: Introduction to supervised learning algorithms, including linear regression, logistic regression, and decision trees.
- Model Evaluation: Metrics for evaluating machine learning models (accuracy, precision, recall, F1-score, ROC curves).
- Feature Engineering: Techniques for selecting, transforming, and creating relevant features for machine learning models.
- Project 3: Building a Predictive Model – Train and evaluate a predictive model using a supervised learning algorithm.
Outcome: Understanding of fundamental machine learning concepts and algorithms.
Week 4: Advanced Machine Learning Techniques
- Model Selection and Tuning: Techniques for selecting the best model and optimizing hyperparameters (Grid Search, Random Search, Cross-Validation).
- Ensemble Methods: Introduction to ensemble methods like Random Forest and Gradient Boosting.
- Unsupervised Learning: Introduction to clustering (k-means) and dimensionality reduction (PCA).
- Project 4: Model Optimization and Ensemble Methods – Improve the performance of a machine learning model using tuning and ensemble techniques.
Outcome: Knowledge of advanced machine learning techniques and model optimization strategies
Week 5: Big Data & Cloud Computing
- Introduction to Big Data: Concepts of big data, challenges, and tools for handling large datasets.
- Cloud Computing for Data Science: Introduction to cloud platforms (AWS, Azure, GCP) and their services for data storage and processing.
- Working with Spark (Optional): Basic introduction to Apache Spark for distributed data processing.
- Project 5: Working with Large Datasets – Process and analyze a large dataset using appropriate tools and techniques. This could involve using cloud-based services.
Outcome: Understanding of big data concepts and cloud computing for data science.
Week 6: Final Project & Career Prep
- Project 6: Final Data Science Project – Develop a complete data science project, from data collection and cleaning to model building, evaluation, and presentation. Students can choose a project based on their interests.
- Career Preparation:
- Portfolio Building: Refine and complete your portfolio with a professional presentation of your projects.
- Interview Prep & Resume Tips: Learn how to showcase your skills, work on technical interview questions, and perfect your resume.
- Industry Insights: Get advice on the latest industry trends, frameworks, and technologies.
Outcome: By the end of the course, you’ll have a strong portfolio and the confidence to apply for jobs in data science.
Major Projects:
- Basic Data Exploration (Week 1) – Core Skills: Python, Pandas, data cleaning.
- Data Visualization and Storytelling (Week 2) – Core Skills: Data visualization, statistical analysis.
- Building a Predictive Model (Week 3) – Core Skills: Supervised learning, model evaluation.
- Model Optimization and Ensemble Methods (Week 4) – Core Skills: Model tuning, ensemble methods.
- Working with Large Datasets (Week 5) – Core Skills: Big data, cloud computing.
- Final Data Science Project (Week 6) – Core Skills: Full data science lifecycle, project planning, and execution.
Mentorship at Eduveda Academy:
- Industry Mentor Assignment: You’ll be paired with a mentor from a top tech company who will provide personalized guidance on projects, career advice, and best practices.
- 1-on-1 sessions for project reviews and troubleshooting.
- Live Q&A Sessions weekly with mentors and instructors.
Final Notes:
- Weekly Live Sessions: These will cover key topics, provide updates, and allow you to ask questions.
- Peer Networking: Join the academy Slack group for collaboration and feedback.
This 6-week course is intense but designed to give you all the key skills to jumpstart your career in Data Science. You’ll leave with solid project experience, a polished portfolio, and the confidence to apply for jobs in the field.
