Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Data Science 101
Search
Ronojoy Adhikari
September 29, 2015
Research
4
1.6k
Data Science 101
Presentation at the Data Science 101 workshop at Orangescape.
Ronojoy Adhikari
September 29, 2015
Tweet
Share
More Decks by Ronojoy Adhikari
See All by Ronojoy Adhikari
Hydrodynamic and phoretic interactions of active particles in Python
ronojoy
0
150
IMSc Review Presentation
ronojoy
0
330
Probabilistic programming in Python
ronojoy
0
350
Mathematical Modelling
ronojoy
0
210
Data Science : Theory
ronojoy
2
1.3k
Data Science : Probability Theory
ronojoy
1
400
Active Brownian Motion
ronojoy
0
310
Does a droplet roll or slide ?
ronojoy
0
140
Bayesianism : a lightning introduction
ronojoy
2
110
Other Decks in Research
See All in Research
EcoWikiRS: Learning Ecological Representation of Satellite Images from Weak Supervision with Species Observation and Wikipedia
satai
3
120
Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets
satai
3
260
カスタマーサクセスの視点からAWS Summitの展示を考える~製品開発で活用できる勘所~
masakiokuda
2
190
Minimax and Bayes Optimal Best-arm Identification: Adaptive Experimental Design for Treatment Choice
masakat0
0
170
MIRU2025 チュートリアル講演「ロボット基盤モデルの最前線」
haraduka
15
7.9k
数理最適化と機械学習の融合
mickey_kubo
16
9.3k
20250624_熊本経済同友会6月例会講演
trafficbrain
1
600
多言語カスタマーインタビューの“壁”を越える~PMと生成AIの共創~ 株式会社ジグザグ 松野 亘
watarumatsuno
0
120
2025/7/5 応用音響研究会招待講演@北海道大学
takuma_okamoto
1
180
心理言語学の視点から再考する言語モデルの学習過程
chemical_tree
2
570
Adaptive Experimental Design for Efficient Average Treatment Effect Estimation and Treatment Choice
masakat0
0
110
GPUを利用したStein Particle Filterによる点群6自由度モンテカルロSLAM
takuminakao
0
240
Featured
See All Featured
Measuring & Analyzing Core Web Vitals
bluesmoon
9
580
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
131
19k
Become a Pro
speakerdeck
PRO
29
5.5k
Practical Orchestrator
shlominoach
190
11k
[RailsConf 2023] Rails as a piece of cake
palkan
57
5.8k
For a Future-Friendly Web
brad_frost
180
9.9k
Building Flexible Design Systems
yeseniaperezcruz
328
39k
Context Engineering - Making Every Token Count
addyosmani
1
19
Keith and Marios Guide to Fast Websites
keithpitt
411
22k
The Power of CSS Pseudo Elements
geoffreycrofte
77
5.9k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
Why Our Code Smells
bkeepers
PRO
339
57k
Transcript
Data Science 101: insight, not numbers Ronojoy Adhikari The Institute
of Mathematical Sciences Chennai, India Orangescape Chennai, India Wednesday, 30 September 15
The purpose of computing is insight, not numbers. Wednesday, 30
September 15
The purpose of computing is insight, not numbers. Wednesday, 30
September 15
The purpose of computing is insight, not numbers. Richard Hamming
Wednesday, 30 September 15
What is the purpose of data science ? Wednesday, 30
September 15
What is the purpose of data science ? Insight, not
numbers! Wednesday, 30 September 15
Data science Wednesday, 30 September 15
Wednesday, 30 September 15
Data Wednesday, 30 September 15
Data Domain knowledge Wednesday, 30 September 15
Data Domain knowledge Data curation Wednesday, 30 September 15
Data Domain knowledge Data curation Mathematical model Wednesday, 30 September
15
Data Domain knowledge Data curation Mathematical model A/B testing Wednesday,
30 September 15
Data Domain knowledge Data curation Mathematical model A/B testing Machine
learning Wednesday, 30 September 15
Data Domain knowledge Data curation Mathematical model A/B testing Machine
learning Machine inference Wednesday, 30 September 15
Data Domain knowledge Data curation Mathematical model A/B testing Machine
learning Machine inference Value from data Wednesday, 30 September 15
1. Problem or question ? Wednesday, 30 September 15
Wednesday, 30 September 15
Let the data speak for themselves! Ronald Fisher Wednesday, 30
September 15
Let the data speak for themselves! Ronald Fisher The data
cannot speak for themselves; and they never have, in any real problem of inference. Edwin Jaynes Wednesday, 30 September 15
Classification Regression Clustering Dimensionality reduction Wednesday, 30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes Wednesday,
30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes Wednesday,
30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes predict
values, given other values Wednesday, 30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes predict
values, given other values Wednesday, 30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes predict
values, given other values group similar things together Wednesday, 30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes predict
values, given other values group similar things together Wednesday, 30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes predict
values, given other values group similar things together keeping only the relevant variables Wednesday, 30 September 15
Classification Regression Clustering Dimensionality reduction predict class, given attributes predict
values, given other values group similar things together keeping only the relevant variables Wednesday, 30 September 15
3. Frame a hypothesis (mathematical models) Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal probability is a state of knowledge
Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal probability is a state of knowledge
probability is a frequency Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal probability is a state of knowledge
probability is a frequency Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal probability is a state of knowledge
ML : toolbox for processing data probability is a frequency Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal probability is a state of knowledge
ML : toolbox for processing data probability is a frequency Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal probability is a state of knowledge
ML : toolbox for processing data ML : learning generative models of data probability is a frequency Wednesday, 30 September 15
Bayesian Blackbox Frequentist Causal probability is a state of knowledge
ML : toolbox for processing data ML : learning generative models of data probability is a frequency Wednesday, 30 September 15
Wednesday, 30 September 15
Wednesday, 30 September 15
Wednesday, 30 September 15
We are building a causal learning and inference engine that
will beat the current state-of-art! Wednesday, 30 September 15
We are building a causal learning and inference engine that
will beat the current state-of-art! Thank you for your attention! Wednesday, 30 September 15