# [DS-ML] Session Brief

## :beginner: Flow of session

:small_blue_diamond:Kisine kuch suna hai kya
:small_blue_diamond:Why this domain

## Background

:::success
1. What is DS, via Venn Diagram
2. What problems do we solve
3. Real life use cases (churn prediction, spam detection)
4. From Data Engineer to Data Science
:::

## :pencil: Where to start from :
1. Basic Analysis of datasets, univariate analysis, bivariate analysis.
2. Implementation of algorithms like Linear Regression, K-means.

### :small_blue_diamond: Planned Sessions & Workshops:
1. Getting started with Data Science
2. Statistical analysis of data
3. Dimensionality Reduction : Why and How
1. Hands on with libraries, EDA
2. Linear Regression
3. Implementing Classification Algorithms

## :book:Resources
1. [Getting Started with Python](https://jovian.com/learn/data-analysis-with-python-zero-to-pandas?enroll=t)
2. [Summer Analytics Course by IIT-CAG](https://www.caciitg.com/sa/course/)
2. [StatQuest](https://www.youtube.com/user/joshstarmer)

## Stuff
- [ ] **Start**
1. [Curriculum for beginners](https://towardsdatascience.com/a-complete-data-science-curriculum-for-beginners-825a39915b54)
2. [Data Processing](https://towardsdatascience.com/introduction-to-data-processing-using-descriptive-statistics-and-statistical-charts-in-python-9857a60c481b)
3. [Deep Learning](https://d2l.ai/)

**Projects** must have:
1. Data Cleaning
1. Exploratory Data Analysis
1. Data Visualisation

**Sample Projects**
* Fake News Detection (Natural Language Processing)
* Climate Change Impacts on the Global Food Supply
* Human Action Recognition (Computer Vision)
* Forest Fire Prediction
* Road Lane Line Detection (Computer Vision)

- **Hackathons**
1. Machinehack
1. DataHack
1. Helmholtz GPU Hackathon
1. Smart India Hackathon
1. Machacks
1. DataCrunchk