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
1.5k
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
320
Probabilistic programming in Python
ronojoy
0
340
Mathematical Modelling
ronojoy
0
210
Data Science : Theory
ronojoy
2
1.3k
Data Science : Probability Theory
ronojoy
1
390
Active Brownian Motion
ronojoy
0
300
Does a droplet roll or slide ?
ronojoy
0
130
Bayesianism : a lightning introduction
ronojoy
2
110
Other Decks in Research
See All in Research
能動適応的実験計画
masakat0
2
680
データサイエンティストの採用に関するアンケート
datascientistsociety
PRO
0
1.1k
経済学と機械学習:因果推論と密度比推定を中心に
masakat0
0
110
電力システム最適化入門
mickey_kubo
1
760
Submeter-level land cover mapping of Japan
satai
3
160
SSII2025 [TS2] リモートセンシング画像処理の最前線
ssii
PRO
7
3k
最適決定木を用いた処方的価格最適化
mickey_kubo
4
1.8k
2025年度人工知能学会全国大会チュートリアル講演「深層基盤モデルの数理」
taiji_suzuki
24
17k
Google Agent Development Kit (ADK) 入門 🚀
mickey_kubo
2
1.3k
Computational OT #1 - Monge and Kantorovitch
gpeyre
0
210
「エージェントって何?」から「実際の開発現場で役立つ考え方やベストプラクティス」まで
mickey_kubo
0
130
【輪講資料】Moshi: a speech-text foundation model for real-time dialogue
hpprc
3
540
Featured
See All Featured
Six Lessons from altMBA
skipperchong
28
3.9k
How to Ace a Technical Interview
jacobian
278
23k
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
126
53k
Thoughts on Productivity
jonyablonski
69
4.8k
Making the Leap to Tech Lead
cromwellryan
134
9.4k
Building Adaptive Systems
keathley
43
2.7k
Dealing with People You Can't Stand - Big Design 2015
cassininazir
367
26k
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
30
2.2k
Measuring & Analyzing Core Web Vitals
bluesmoon
7
530
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
8
390
GitHub's CSS Performance
jonrohan
1031
460k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
26
2.9k
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