This course is composed of two parts.
Part-1 : Syllabus as listed below
Part-2 : Project works (Our specialization- We will assign kids to different projects based on their interest level, age and enthusiasm level)
What is Data Science?,
Getting started with R, Exploratory Data Analysis, Review of probability and probability distributions
Supervised Learning, Regression, polynomial regression, local regression, k-nearest neighbors,
Lesson 3: Unsupervised Learning, Kernel density estimation, kmeans, Naive Bayes, Data and Data Scraping
Lesson 4: Classification, ranking, logistic regression Ethics, time series, advanced regression, finance
Lesson 5: Decision trees, Best practices, feature selection. Kaggle competition (final project) announced; Applying data science in a hybrid research environment
Lesson 6: Recommendation engines, dimensionality reduction, indexing large-scale data, and implementing / optimizing machine learning algorithms.
Lesson 7: Data visualization, data journalism, dashboards? Social network analysis
Lesson 8: Sampling, Stratification, Experimental design, pharma Siliconvalley4u.com scratch.ver.1 Observational causal modeling Sampling, data leakage, data incest
Lesson 9: Data engineering, sharding, Hadoop, mapreduce and proto buffers
Lesson 10: Data engineering