blockly big data
play

Blockly & Big Data CT @ VT Things to see today Blocks that - PowerPoint PPT Presentation

May 25, 2023 •339 likes •412 views

Introduction to Computational Thinking Blockly & Big Data CT @ VT Things to see today Blocks that produce representative slices of actual big data streams Blocks that correspond to programming language constructs See the

  1. Introduction to Computational Thinking Blockly & Big Data

  2. CT @ VT Things to see today  Blocks that produce representative slices of actual “big data” streams  Blocks that correspond to programming language constructs  See the Python tab  Examples of Blockly programs for manipulating the data streams  First look at the data streams for your projects Spring 201 2015 Slide 2

  3. CT @ VT Blockly Environment Spring 201 2015 Slide 3

  4. CT @ VT Categories of Blocks algorithm components data organizations “big data” sources Spring 201 2015 Slide 4

  5. CT @ VT Blocks Used Today Spring 201 2015 Slide 5

  6. CT @ VT Demonstrations  Block options  Output of data streams  Manipulation of data  Output of data  Plotting of data Spring 201 2015 Slide 6

  7. CT @ VT Next steps today  Work on the assigned Blockly and Big Data problems in the book  Work as an individual  Seek help from and provide help to your cohort  Go as far and as fast as you can remembering to keep everyone in your cohort on board  Cohort feedback/reports  Discussion of Data Streams Spring 201 2015 Slide 7

Recommend

Blockly Lists & Iteration CT @ VT Things we are seeing Using lists to represent a data

Blockly Lists & Iteration CT @ VT Things we are seeing Using lists to represent a data

Introduction to Computational Thinking Blockly Lists & Iteration CT @ VT Things we are seeing Using lists to represent a data stream Blockly blocks for iteration Using iteration to process a data stream (i.e., a list)

275 views • 10 slides
More Visualization CT @ VT Why Functions How can code be made available for others to use?

More Visualization CT @ VT Why Functions How can code be made available for others to use?

Introduction to Computational Thinking More Visualization CT @ VT Why Functions How can code be made available for others to use? Cut/paste of code (as we did for Blockly Python) works only for small amounts of code Need better

216 views • 9 slides
Few people understand it Opportunity Technology is everywhere Let kids become actors , not just

Few people understand it Opportunity Technology is everywhere Let kids become actors , not just

Problem Technology is everywhere Few people understand it Opportunity Technology is everywhere Let kids become actors , not just consumers Confidential content Basic behaviours Discover what Thymio can do Scratch & Blockly Scratch 3

353 views • 18 slides
Data Preparation Data cleaning Data integration and transformation (Data

Data Preparation Data cleaning Data integration and transformation (Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Data integration and transformation (Data preprocessing) Data reduction, Feature selection Discretization and concept hierarchy generation 2

891 views • 21 slides
DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data

DATA MINING LECTURE 2 What is data? The data mining pipeline What is Data Mining? Data mining is the use of efficient techniques for the analysis of very large collections of data and the extraction of useful and possibly unexpected

2.4k views • 94 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 28 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

455 views • 23 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 68 / 91 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4 2

579 views • 8 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 68 / 91 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4 2

729 views • 24 slides
Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval

Inf1-DA 20102011 III: 51 / 89 Part III Unstructured Data Data Retrieval: III.1 Unstructured data and data retrieval Statistical Analysis of Data: III.2 Data scales and summary statistics III.3 Hypothesis testing and correlation III.4

344 views • 17 slides
Data Preparation Data cleaning Discretization (Data preprocessing) Data

Data Preparation Data cleaning Discretization (Data preprocessing) Data

Data Preprocessing Why preprocess the data? Data Preparation Data cleaning Discretization (Data preprocessing) Data integration and transformation Data reduction, Feature selection 2 Why Prepare Data? Why Prepare

462 views • 15 slides
DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and

DATA QUALITY AND DATA DATA QUALITY AND DATA PROGRAMMING PROGRAMMING "Data cleaning and repairing account for about 60% of the work of data scientists." Christian Kaestner Required reading: Schelter, S., Lange, D., Schmidt, P.,

1.1k views • 86 slides
Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by

Data Acquisition Chapter 2 Data Acquisition 1 st step: get data Usually data gathered by some geophysical device Most surveys are comprised of linear traverses or transects Typically constant data spacing Perpendicular to

432 views • 12 slides
Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science

Data stories with The Pudding So what is data storytelling? Data academia & data science dense complex static/non-interactive often not accompanied by an easy-to-follow narrative Data art beautiful abstract

647 views • 5 slides
Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges:

Data Collection and Aggregation Data Collection and Aggregation 1 Challenges: data Challenges: data Data type: numerical sensor readings. Rich and massive data, spatially distributed and correlated. Data dynamics: data

714 views • 48 slides
Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data

DATA Business Statistics CONTENTS The role of data The data matrix Data types Aspects of data Obtaining data Further study THE ROLE OF DATA Data refers to observed facts there are 82 persons in this train the weight of

683 views • 22 slides
DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type

DataCamp Data Types for Data Science DataCamp Data Types for Data Science Data types Data type system sets the stage for the capabilities of the language Understanding data types empowers you as a data scientist DataCamp Data Types for Data

527 views • 23 slides
q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the

q.Datum Data Exchange The marketplace for big data Data is the new oil of the Internet and the new currency of the digital world . World Economic Forum 1 Problem: access to big data Data accessed & analyzed Data in organizations

543 views • 30 slides
Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

Concatenating data Cleaning Data in Python Combining data Data may not always come in 1

CLEANING DATA IN PYTHON Concatenating data Cleaning Data in Python Combining data Data may not always come in 1 huge file 5 million row dataset may be broken into 5 separate datasets Easier to store and share May have

426 views • 26 slides
Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What

Prioritizing Data and Purpose of Data Points What data do I have? What data do I trust? What data do I use most? Session Target: After todays session, you will be able to: 1. Insert/include data opportunities into PL models. 2.

710 views • 12 slides
Data Preparation Discretization Data cleaning (Data pre-processing) Data

Data Preparation Discretization Data cleaning (Data pre-processing) Data

Data Preparation Why prepare the data? Data Preparation Discretization Data cleaning (Data pre-processing) Data integration and transformation Data reduction, Feature selection 2 Why Prepare Data? Why Prepare Data?

426 views • 17 slides
Introduction to IoT data AN ALYZ IN G IOT DATA IN P YTH ON Matthias Voppichler IT Developer

Introduction to IoT data AN ALYZ IN G IOT DATA IN P YTH ON Matthias Voppichler IT Developer

Introduction to IoT data AN ALYZ IN G IOT DATA IN P YTH ON Matthias Voppichler IT Developer Course overview Collect and analyze IoT data Gather data API Endpoints Data Streams Visualize data Combine datasets Detect patterns ML Model

756 views • 27 slides
Modeling Data the different views on Data Mining Views on Data Mining Fitting the data

Modeling Data the different views on Data Mining Views on Data Mining Fitting the data

Modeling Data the different views on Data Mining Views on Data Mining Fitting the data Density Estimation Learning being able to perform a task more accurately than before Prediction use the data to predict future data

517 views • 29 slides
Abstract Data Types & Templates 1 Abstract Data Type 2 Data and Abstraction

Abstract Data Types & Templates 1 Abstract Data Type 2 Data and Abstraction

Abstract Data Types & Templates 1 Abstract Data Type 2 Data and Abstraction Operations on data are central to most solutions Think abstractly about data and its management Typically need to Organize data Add data Remove

756 views • 30 slides
Big Data Analytics Armistead Boyd SVP, Product & Data Partnerships October 25, 2016 What is

Big Data Analytics Armistead Boyd SVP, Product & Data Partnerships October 25, 2016 What is

Big Data Analytics Armistead Boyd SVP, Product & Data Partnerships October 25, 2016 What is Big Data Analytics? Data Future Data Past Data Present 1 Big Data Trends Data Driven Organization Other Executives 5% 8% Operations Data

620 views • 22 slides

More recommend