Data Science Bootcamp

What you will learn

• Importance of ‘Data’ in Data Science
• Data Exploration and Visualization
• Feature Engineering
• Storytelling with Data
• Modeling a Real World Predictive Analytics Problem
• Supervised Learning and Classification
• Decision Tree Classification
• Building and evaluating a classification model
• Evaluation Metrics for Classification Models
• Generalization and Overfitting
• Tuning of Model Hyperparameters
• Bagging
• Random Forest
• Random Forest Hyperparameter Tuning
• Boosting Introduction
• Mechanics of Boosting and Pitfalls
• Introduction to Text Analytics
• Unsupervised Learning and k-means Clustering
• Collaborative and Content-based Recommendations
• Measures of Similarity
• Evaluation Metrics for Recommender Systems
• Online Experimentation
• Hypothesis Testing Fundamentals
• Running experiments in real-world
• Math Fundamentals
• Optimizing the Cost Function
• Evaluation of Regression Models
• Regularization
• Predicting prices of real-estate using a linear regression model
• Big Data Engineering
• Real-Time/IoT
• Deploying a Predictive Model as a Service
• Introduction to Big Data, Data Science and Predictive Analytics
• Fundamentals of Data Mining
• Introduction to R Programming
• Introduction to Azure Machine Learning
• Kaggle Capstone
• Naïve Bayes
• Logistic Regression
• Introduction to NoSQL Databases