Data science for kids

This is a great place to add a tagline

Tell customers more about you. Add a few words and a stunning pic to grab their attention and get them to click.

This space is ideal for writing a detailed description of your business and the types of services that you provide. Talk about your team and your areas of expertise. 

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)


Lesson 1: 


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 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