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)

SYLLABUS:

Lesson 1: 

Introduction:

What is Data Science?,

Getting started with R, Exploratory Data Analysis, Review of probability and probability distributions

Lesson 2: 

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

Data Science for Kids

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