UNIT - I
Introduction to Data Science
SCHOOL OF mechanical EGINEERING AND TECHNOLOGY
Data Science
T. Y. BTECH
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introduction to data science, Schemes and Mind Maps of Basics of Data Warehousing
This document introduces the concept of data science and its importance in today's world. It discusses the various applications of data analytics and the 5 Vs of big data. It also covers the data science life cycle and the importance of data preparation. the importance of Python in data science and introduces the Pandas library. It also covers the various operations that can be performed on data using Pandas. the problems associated with dirty data and the importance of data preprocessing.
Typology: Schemes and Mind Maps
2021/2022
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UNIT - I
Introduction to Data Science
SCHOOL OF mechanical EGINEERING AND TECHNOLOGY
Data Science
T. Y. BTECH
Data Science: Why all the Excitement?
Exciting new effective applications of data analytics e.g., Google Flu Trends: Detecting outbreaks two weeks ahead of CDC data New models are estimating which cities are most at risk for spread of the Ebola virus. Prediction model is built on Various data sources, types and analysis.
Graph Data 4 Lots of interesting data has a graph structure:
- (^) Social networks
- (^) Communication networks
- (^) Computer Networks
- (^) Road networks
- (^) Citations
- (^) Collaborations/Relationships
- (^) … Some of these graphs can get quite large (e.g., Facebook
user graph)
What can you do with the data? 5
Crowdsourcing + physical modeling + sensing + data assimilation
to produce:
From Alex Bayen, UCB
DATA SCIENCE – WHAT IS IT?
Data Science – A Definition 9 Data Science is the science which uses computer science, statistics and machine learning, visualization and human-computer interactions to collect, clean, integrate, analyze, visualize, interact with data to create data products.
Jeff Hammerbacher’s Model 11
- Identify problem
- Instrument data sources
- Collect data
- Prepare data (integrate, transform, clean, filter, aggregate)
- Build model
- Evaluate model
- Communicate results
Data Scientist’s Practice Digging Around in Data Hypothesize Model Large Scale Exploitation Evaluate Interpret Clean, prep
Data Science: Getting Value out of Data
Data Science: Getting Value out of Data
Data Science: Getting Value out of Data
Why the Increased Interest in Data Science?
Applications
- (^) Climate change and weather
- (^) Traffic control
- (^) Agriculture
- (^) Personalised healthcare
- (^) Twitter data analysis
- (^) Facebook information links
- (^) Pollution and Weather
Contrast: Databases Databases Data Science Data Value “Precious” “Cheap” Data Volume Modest Massive Examples Bank records, Personnel records, Census, Medical records Online clicks, GPS logs, Tweets, Building sensor readings Priorities Consistency, Error recovery, Auditability Speed, Availability, Query richness Structured Strongly (Schema) Weakly or none (Text) Properties Transactions, ACID* CAP* theorem (2/3), eventual consistency Realizations SQL NoSQL: MongoDB, CouchDB, Hbase, Cassandra,… ACID = Atomicity, Consistency, Isolation and Durability CAP = Consistency, Availability, Partition Tolerance