In the context of this assignment on Mongo, queries will be designed and executed on a mongo collection, simple operations on mongo will be executed with python while mapreduce jobs will also be designed and executed on a mongo collection.
In the context of this assignment on Redis, relational data are
inserted into a redis database while sql queries are properly
edited and transformed in order to retrieve information from the
redis database.
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
This document discusses using R for statistical analysis with MongoDB as the database. It introduces MongoDB as a NoSQL database for storing large, complex datasets. It describes the rmongodb package for connecting R to MongoDB, allowing users to query, aggregate, and analyze MongoDB data directly in R without importing entire datasets into memory. Examples show performing queries, aggregations, and accessing results as native R objects. The document promotes R and MongoDB as a solution for big data analytics.
This is a very ^2 basic introduction to R.
The purpose of this presentation is to prepare you with all that you have to know about fundamentals of using R to operate data frames, which you can easily get by importing data from relational database table or csv/text file.
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
Enhancing Spark SQL Optimizer with Reliable StatisticsJen Aman
This document discusses enhancements to the Spark SQL optimizer through improved statistics collection and cost-based optimization rules. It describes collecting table and column statistics from Hive metastore and developing 1D and 2D histograms. New rules estimate operator costs based on output rows and size. Join order, filter statistics, and handling unique columns are discussed. Future work includes faster histogram collection, expression statistics, and continuous feedback optimization.
In the context of this assignment on Redis, relational data are
inserted into a redis database while sql queries are properly
edited and transformed in order to retrieve information from the
redis database.
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
This document discusses using R for statistical analysis with MongoDB as the database. It introduces MongoDB as a NoSQL database for storing large, complex datasets. It describes the rmongodb package for connecting R to MongoDB, allowing users to query, aggregate, and analyze MongoDB data directly in R without importing entire datasets into memory. Examples show performing queries, aggregations, and accessing results as native R objects. The document promotes R and MongoDB as a solution for big data analytics.
This is a very ^2 basic introduction to R.
The purpose of this presentation is to prepare you with all that you have to know about fundamentals of using R to operate data frames, which you can easily get by importing data from relational database table or csv/text file.
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
Enhancing Spark SQL Optimizer with Reliable StatisticsJen Aman
This document discusses enhancements to the Spark SQL optimizer through improved statistics collection and cost-based optimization rules. It describes collecting table and column statistics from Hive metastore and developing 1D and 2D histograms. New rules estimate operator costs based on output rows and size. Join order, filter statistics, and handling unique columns are discussed. Future work includes faster histogram collection, expression statistics, and continuous feedback optimization.
The document discusses Oracle system catalogs which contain metadata about database objects like tables and indexes. System catalogs allow accessing information through views with prefixes like USER, ALL, and DBA. Examples show how to query system catalog views to get information on tables, columns, indexes and views. Query optimization and evaluation are also covered, explaining how queries are parsed, an execution plan is generated, and the least cost plan is chosen.
Get up to Speed (Quick Guide to data.table in R and Pentaho PDI)Serban Tanasa
1) The document provides a quick guide to using data.table in R and Pentaho Data Integration (PDI) for fast data loading and manipulation. It discusses benchmarks showing data.table is 2-20x faster than traditional methods for reading, ordering, and transforming large data.
2) The outline discusses how to use basic data.table functions for speed gains and to overcome R's scaling limitations. It also provides a very brief overview of PDI's capabilities for Extract/Transform/Load (ETL) workflows without writing code.
3) The benchmarks section shows data.table is up to 500% faster than traditional R methods for reading large CSV files and orders of magnitude faster for sorting and aggregating
Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements of interactive visualizations and dissipate a lot expensive network bandwidth. Current solutions for lowering the volume of time series data disregard the properties of the resulting visualization and achieve only poor visualization quality.
In this work, we introduce M4, a simple aggregation-based time series dimensionality reduction technique that is superior to existing approaches, in that it provides lower visualization errors at higher data reduction ratios. Focusing on the semantic of line charts, as the predominant form of time-series visualization, we explain in detail, why current data reduction techniques fail and how our approach achieves superiority by respecting the process of line rasterization. We describe how to incorporate the proposed aggregation model already at the query-level in a visualization-driven query-
rewriting system. Our approach is generic and applicable to any visualization system that relies on relational data sources. Using real world data sets from high tech manufacturing, stock markets, and engineering domains we demonstrate that our visualization-oriented data aggregation can reduce data volumes by up to two orders of magnitude, while preserving perfect visualizations.
This document discusses extendible hashing, which is a hashing technique for dynamic files that allows efficient insertion and deletion of records. It works by using a directory to map hash values to buckets, and dynamically expanding the directory size and number of buckets as needed to accommodate new records. When a bucket overflows, it is split into two buckets, and the directory is expanded to distinguish them. The directory size can also be contracted when buckets can be combined due to deletions. Alternative approaches like dynamic hashing and linear hashing that address the same problem of dynamic files are also overviewed.
A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
Using the python lib NetworkX to calculate stats on a Twitter network, and then display the results in several D3.js visualizations. Links to demos and source files. I'm @arnicas and live at www.ghostweather.com.
This document describes the binary sort algorithm, a non-comparison sorting algorithm that sorts data by examining bits of information one bit at a time from most significant to least significant. It has a time complexity of O(kn) where k is the number of bits in each data item and n is the number of items. The algorithm works by recursively sorting subsets of the data based on the value of each bit, placing 0s in one group and 1s in another. It is an in-place, linear sorting algorithm.
Contents:
1. Direct Address Table
2. Hashing
3. Characteristics of a good hash function
4. Collision Resolution using Chaining and Probing
5. Static vs Dynamic Hashing
6. Extendible Hashing
7. B+ tree vs Hashing
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ )
Map Reduce: Beyond Word Count by Jeff Patti
Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline
Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
The document discusses query processing and optimization. It describes several key activities in query processing including translating queries to a format executable by the database, applying optimization techniques, and evaluating the queries. It then provides details on three specific operations: selection using linear searches and indices, sorting, and join operations. It explains different algorithms for implementing each operation and factors to consider when choosing algorithms such as indexing and data sizes.
NetworkX is a Python language software package and an open-source tool for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. NetworkX can load, store and analyze networks, generate new networks, build network models, and draw networks. It is a computational network modelling tool and not a software tool development. The first public release of the library, which is all based on Python, was in April 2005.
The document summarizes several papers presented at SIGMOD 2011 related to Hadoop and distributed data processing. It then provides more detail on Apache Hadoop's real-time capabilities at Facebook, the Nova system for continuous Pig/Hadoop workflows, and an approach for loading data from Hadoop into parallel data warehouses more efficiently.
The document describes building regression and classification models in R, including linear regression, generalized linear models, decision trees, and random forests. It uses examples of CPI data to demonstrate linear regression and predicts CPI values in 2011. For classification, it builds a decision tree model on the iris dataset using the party package and visualizes the tree. The document provides information on evaluating and comparing different models.
Dremel interactive analysis of web scale datasetsCarl Lu
Dremel is an interactive query system that can analyze large web-scale datasets containing trillions of records in seconds. It uses a columnar data layout and multi-level query execution trees to distribute queries across thousands of servers. Dremel's nested data model and column-striped storage allows it to efficiently retrieve and analyze only the necessary columns from large datasets. Experimental results demonstrated Dremel's ability to process queries over datasets containing trillions of records and petabytes of data in seconds using a cluster of thousands of servers.
This document outlines algorithms for query processing and optimization in database systems. It discusses translating SQL queries to relational algebra, algorithms for sorting and joining large datasets that exceed available memory, including nested loop joins, sort-merge joins, and hash joins. It also describes query optimization techniques and factors that influence query performance.
Introduction to Pandas and Time Series Analysis [Budapest BI Forum]Alexander Hendorf
As senior consultant of German management consultancy Königsweg, Alexander is guiding enterprises and institutions through change processes of digitalisation and automation.
Alexander always loved data almost as much as music and so no wonder he’s organiser of local meet ups and one of the 25 mongoDB Community Masters.
He loves to share this expertise and engages in the global community as organiser and program chair of the EuroPython conference, speaker and trainer at multiple international conferences as mongoDB World, EuroPython, Cebit or PyData.
This document discusses various programming concepts in R including operators, control flow statements, and functions. It covers arithmetic, relational, and logical operators. It also covers if/else statements, for loops, while loops, and the repeat loop. Commonly used functions like summary(), str(), and length() are also mentioned. The document is intended to teach basic concepts in R programming.
Document clustering for forensic analysis an approach for improving compute...Madan Golla
The document proposes an approach to apply document clustering algorithms to forensic analysis of computers seized in police investigations. It discusses using six representative clustering algorithms - K-means, K-medoids, Single/Complete/Average Link hierarchical clustering, and CSPA ensemble clustering. The approach estimates the number of clusters automatically from the data using validity indexes like silhouette, in order to facilitate computer inspection and speed up the analysis process compared to examining each document individually.
5. working on data using R -Cleaning, filtering ,transformation, Samplingkrishna singh
This document discusses various techniques for working with and preparing data for analysis in R, including loading and exploring data, handling different data formats, cleaning data by dealing with missing values and outliers, transforming data through normalization and discretization, sampling data for modeling, and visualizing data. It provides examples of using functions like read.table(), class(), summary(), str(), names(), dim(), ifelse(), merge(), and graphing techniques like histograms, boxplots, and scatter plots to examine relationships in the data.
This document contains C++ code for implementing various sorting algorithms, data structures like stacks and queues, and their basic operations. It includes code for bubble sort, selection sort, insertion sort algorithms. Data structures implemented are stacks and queues with functions like push, pop, enqueue, dequeue etc. The main function allows the user to choose between sorting algorithms, stack and queue operations and displays the results.
The document discusses algorithms and techniques for query processing and optimization in relational database management systems. It covers translating SQL queries into relational algebra, algorithms for operations like selection, projection, join and sorting, using heuristics and cost estimates for optimization, and an overview of query optimization in Oracle databases.
MongoDB Project: Relational databases to Document-Oriented databasesLamprini Koutsokera
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
The document discusses Oracle system catalogs which contain metadata about database objects like tables and indexes. System catalogs allow accessing information through views with prefixes like USER, ALL, and DBA. Examples show how to query system catalog views to get information on tables, columns, indexes and views. Query optimization and evaluation are also covered, explaining how queries are parsed, an execution plan is generated, and the least cost plan is chosen.
Get up to Speed (Quick Guide to data.table in R and Pentaho PDI)Serban Tanasa
1) The document provides a quick guide to using data.table in R and Pentaho Data Integration (PDI) for fast data loading and manipulation. It discusses benchmarks showing data.table is 2-20x faster than traditional methods for reading, ordering, and transforming large data.
2) The outline discusses how to use basic data.table functions for speed gains and to overcome R's scaling limitations. It also provides a very brief overview of PDI's capabilities for Extract/Transform/Load (ETL) workflows without writing code.
3) The benchmarks section shows data.table is up to 500% faster than traditional R methods for reading large CSV files and orders of magnitude faster for sorting and aggregating
Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements of interactive visualizations and dissipate a lot expensive network bandwidth. Current solutions for lowering the volume of time series data disregard the properties of the resulting visualization and achieve only poor visualization quality.
In this work, we introduce M4, a simple aggregation-based time series dimensionality reduction technique that is superior to existing approaches, in that it provides lower visualization errors at higher data reduction ratios. Focusing on the semantic of line charts, as the predominant form of time-series visualization, we explain in detail, why current data reduction techniques fail and how our approach achieves superiority by respecting the process of line rasterization. We describe how to incorporate the proposed aggregation model already at the query-level in a visualization-driven query-
rewriting system. Our approach is generic and applicable to any visualization system that relies on relational data sources. Using real world data sets from high tech manufacturing, stock markets, and engineering domains we demonstrate that our visualization-oriented data aggregation can reduce data volumes by up to two orders of magnitude, while preserving perfect visualizations.
This document discusses extendible hashing, which is a hashing technique for dynamic files that allows efficient insertion and deletion of records. It works by using a directory to map hash values to buckets, and dynamically expanding the directory size and number of buckets as needed to accommodate new records. When a bucket overflows, it is split into two buckets, and the directory is expanded to distinguish them. The directory size can also be contracted when buckets can be combined due to deletions. Alternative approaches like dynamic hashing and linear hashing that address the same problem of dynamic files are also overviewed.
A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
Using the python lib NetworkX to calculate stats on a Twitter network, and then display the results in several D3.js visualizations. Links to demos and source files. I'm @arnicas and live at www.ghostweather.com.
This document describes the binary sort algorithm, a non-comparison sorting algorithm that sorts data by examining bits of information one bit at a time from most significant to least significant. It has a time complexity of O(kn) where k is the number of bits in each data item and n is the number of items. The algorithm works by recursively sorting subsets of the data based on the value of each bit, placing 0s in one group and 1s in another. It is an in-place, linear sorting algorithm.
Contents:
1. Direct Address Table
2. Hashing
3. Characteristics of a good hash function
4. Collision Resolution using Chaining and Probing
5. Static vs Dynamic Hashing
6. Extendible Hashing
7. B+ tree vs Hashing
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ )
Map Reduce: Beyond Word Count by Jeff Patti
Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline
Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
The document discusses query processing and optimization. It describes several key activities in query processing including translating queries to a format executable by the database, applying optimization techniques, and evaluating the queries. It then provides details on three specific operations: selection using linear searches and indices, sorting, and join operations. It explains different algorithms for implementing each operation and factors to consider when choosing algorithms such as indexing and data sizes.
NetworkX is a Python language software package and an open-source tool for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. NetworkX can load, store and analyze networks, generate new networks, build network models, and draw networks. It is a computational network modelling tool and not a software tool development. The first public release of the library, which is all based on Python, was in April 2005.
The document summarizes several papers presented at SIGMOD 2011 related to Hadoop and distributed data processing. It then provides more detail on Apache Hadoop's real-time capabilities at Facebook, the Nova system for continuous Pig/Hadoop workflows, and an approach for loading data from Hadoop into parallel data warehouses more efficiently.
The document describes building regression and classification models in R, including linear regression, generalized linear models, decision trees, and random forests. It uses examples of CPI data to demonstrate linear regression and predicts CPI values in 2011. For classification, it builds a decision tree model on the iris dataset using the party package and visualizes the tree. The document provides information on evaluating and comparing different models.
Dremel interactive analysis of web scale datasetsCarl Lu
Dremel is an interactive query system that can analyze large web-scale datasets containing trillions of records in seconds. It uses a columnar data layout and multi-level query execution trees to distribute queries across thousands of servers. Dremel's nested data model and column-striped storage allows it to efficiently retrieve and analyze only the necessary columns from large datasets. Experimental results demonstrated Dremel's ability to process queries over datasets containing trillions of records and petabytes of data in seconds using a cluster of thousands of servers.
This document outlines algorithms for query processing and optimization in database systems. It discusses translating SQL queries to relational algebra, algorithms for sorting and joining large datasets that exceed available memory, including nested loop joins, sort-merge joins, and hash joins. It also describes query optimization techniques and factors that influence query performance.
Introduction to Pandas and Time Series Analysis [Budapest BI Forum]Alexander Hendorf
As senior consultant of German management consultancy Königsweg, Alexander is guiding enterprises and institutions through change processes of digitalisation and automation.
Alexander always loved data almost as much as music and so no wonder he’s organiser of local meet ups and one of the 25 mongoDB Community Masters.
He loves to share this expertise and engages in the global community as organiser and program chair of the EuroPython conference, speaker and trainer at multiple international conferences as mongoDB World, EuroPython, Cebit or PyData.
This document discusses various programming concepts in R including operators, control flow statements, and functions. It covers arithmetic, relational, and logical operators. It also covers if/else statements, for loops, while loops, and the repeat loop. Commonly used functions like summary(), str(), and length() are also mentioned. The document is intended to teach basic concepts in R programming.
Document clustering for forensic analysis an approach for improving compute...Madan Golla
The document proposes an approach to apply document clustering algorithms to forensic analysis of computers seized in police investigations. It discusses using six representative clustering algorithms - K-means, K-medoids, Single/Complete/Average Link hierarchical clustering, and CSPA ensemble clustering. The approach estimates the number of clusters automatically from the data using validity indexes like silhouette, in order to facilitate computer inspection and speed up the analysis process compared to examining each document individually.
5. working on data using R -Cleaning, filtering ,transformation, Samplingkrishna singh
This document discusses various techniques for working with and preparing data for analysis in R, including loading and exploring data, handling different data formats, cleaning data by dealing with missing values and outliers, transforming data through normalization and discretization, sampling data for modeling, and visualizing data. It provides examples of using functions like read.table(), class(), summary(), str(), names(), dim(), ifelse(), merge(), and graphing techniques like histograms, boxplots, and scatter plots to examine relationships in the data.
This document contains C++ code for implementing various sorting algorithms, data structures like stacks and queues, and their basic operations. It includes code for bubble sort, selection sort, insertion sort algorithms. Data structures implemented are stacks and queues with functions like push, pop, enqueue, dequeue etc. The main function allows the user to choose between sorting algorithms, stack and queue operations and displays the results.
The document discusses algorithms and techniques for query processing and optimization in relational database management systems. It covers translating SQL queries into relational algebra, algorithms for operations like selection, projection, join and sorting, using heuristics and cost estimates for optimization, and an overview of query optimization in Oracle databases.
MongoDB Project: Relational databases to Document-Oriented databasesLamprini Koutsokera
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
This document provides an overview of a machine learning course that teaches Pandas basics. The course aims to teach students how to handle and visualize data, apply basic learning algorithms, develop supervised and unsupervised learning techniques, and build machine learning models. The document outlines the course objectives, outcomes, syllabus including data preprocessing, feature extraction, and data visualization techniques. It also provides references for further reading.
Computer Science CS Project Matrix CBSE Class 12th XII .pdfPranavAnil9
The following project is based on Matrices and Determinants. It is a menu based program with data file and SQL Connectivity. The program is capable of performing all the complex functions of matrices and determinants that are mentioned in the Class 12th Math’s book. The ‘Menu’ of the program upon which it executes is as follows:
1: Generate a Random Matrix
2: Addition
3: Subtraction
4: Multiplication by a Scalar
5: Multiplication by a Matrix
6: Calculate Determinant
7: Calculate Minor
8: Calculate Cofactor
9: Calculate Adjoint
10: Transpose
11: Inversion
This document provides a lab manual for the course GE3171 - Problem Solving and Python Programming Laboratory (REG-2021). It contains details of various programming exercises to be completed as part of the course curriculum. The exercises cover topics like:
1. Developing flow charts and Python programs for real-life problems like electricity billing, retail shop billing, etc.
2. Python programming using simple statements, expressions, and calculations.
3. Scientific problems using conditionals and iterative loops to generate number series and patterns.
4. Implementing applications using lists, tuples to represent library items, car components, construction materials.
5. Implementing applications using sets and dictionaries for language analysis and car
A talk given by Julian Hyde at DataCouncil SF on April 18, 2019
How do you organize your data so that your users get the right answers at the right time? That question is a pretty good definition of data engineering — but it is also describes the purpose of every DBMS (database management system). And it’s not a coincidence that these are so similar.
This talk looks at the patterns that reoccur throughout data management — such as caching, partitioning, sorting, and derived data sets. As the speaker is the author of Apache Calcite, we first look at these patterns through the lens of Relational Algebra and DBMS architecture. But then we apply these patterns to the modern data pipeline, ETL and analytics. As a case study, we look at how Looker’s “derived tables” blur the line between ETL and caching, and leverage the power of cloud databases.
The document outlines the program structure for the second year of engineering at the University of Mumbai for semesters 3 and 4. It includes details of the courses, teaching scheme, examination scheme, labs, and syllabus. Some of the key courses include Data Structures, Database Management Systems, Principles of Communication, and Paradigms and Computer Programming Fundamentals. The syllabus covers topics like Java fundamentals, OOP concepts, inheritance, packages, interfaces, exception handling, multithreading, I/O streams, and GUI programming using AWT and Swing. Students will complete labs related to the coursework and a mini project to develop a front-end or backend application using Java.
This document provides an overview of Python libraries for data analysis and data science. It discusses popular Python libraries such as NumPy, Pandas, SciPy, Scikit-Learn and visualization libraries like matplotlib and Seaborn. It describes the functionality of these libraries for tasks like reading and manipulating data, descriptive statistics, inferential statistics, machine learning and data visualization. It also provides examples of using these libraries to explore a sample dataset and perform operations like data filtering, aggregation, grouping and missing value handling.
The document provides an overview of popular Python libraries for data science such as NumPy, SciPy, Pandas, SciKit-Learn, matplotlib, and Seaborn. It discusses the key features and uses of each library. The document also demonstrates how to load data into Pandas data frames, explore and manipulate the data frames using various methods like head(), groupby(), filtering, and slicing. Summary statistics, plotting and other analyses can be performed on the data frames using these libraries.
This document provides an overview of popular Python libraries for data science and analysis. It discusses NumPy for efficient numerical computations, SciPy for scientific computing functions, Pandas for data structures and analysis, Scikit-Learn for machine learning algorithms, and Matplotlib and Seaborn for data visualization. It also describes common operations in Pandas like reading data, selecting and filtering data, descriptive statistics, and grouping data.
This document provides an overview of popular Python libraries for data science, including NumPy, SciPy, Pandas, Scikit-Learn, matplotlib and Seaborn. It describes the main functions of each library, such as NumPy for multidimensional arrays and mathematical operations, Pandas for data structures and data manipulation, Scikit-Learn for machine learning algorithms, and matplotlib and Seaborn for data visualization. The document also covers reading and exploring data frames, selecting and filtering data, aggregating and grouping data, handling missing values, and basic statistical analysis and graphics.
This document provides an overview of popular Python libraries for data science and analysis. It discusses NumPy for efficient numerical computations, SciPy for scientific computing functions, Pandas for data structures and manipulation, Scikit-Learn for machine learning algorithms, and Matplotlib and Seaborn for data visualization. It also describes common operations in Pandas like reading data, exploring data frames, selecting columns and rows, filtering, grouping, and descriptive statistics.
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this blog, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
This document provides the instructions and sample questions for a Computer Science exam. It details the structure and format of the exam, which consists of two parts (A and B) with multiple choice, short answer, long answer, and descriptive questions. Part A contains two sections - short answer questions to be answered in one word/line, and case study questions with subparts. Part B contains three sections - short answer questions worth 2 marks each, long answer questions worth 3 marks each, and a very long answer question worth 5 marks. All programming questions must be answered in Python. The document provides examples of different types of questions on topics like SQL, cybercrime, networks, Python programming, and more.
This document is an assignment booklet for a course on Programming and Data Structures. It provides instructions for completing and submitting the assignment, which is worth 20% of the student's total grade. The assignment contains 7 questions testing knowledge of C programming concepts like data types, operators, functions, file handling, pointers, arrays and data structures like linked lists and binary trees. Students must submit the completed assignment by the given deadline.
XII - 2022-23 - IP - RAIPUR (CBSE FINAL EXAM).pdfKrishnaJyotish1
The document provides study material and sample papers for Class XII students of Kendriya Vidyalaya Sangathan Regional Office Raipur for the 2022-23 session. It lists the subject coordination by Mrs. Sandhya Lakra, Principal of KV No. 4 Korba and the content team comprising of 7 PGT Computer Science teachers from different KVs. The compilation, review and vetting is done by Mr. Sumit Kumar Choudhary, PGT CS of KV No. 2 Korba NTPC. The document contains introduction and concepts related to data handling using Pandas and Matplotlib libraries in Python.
The document discusses a movie streaming service called JetFlix that maintains a database of movies viewed by users in a binary format. It provides the code for map-reduce functions to determine the most popular movie among users by counting the number of views for each movie across all users. Sample outputs are also given for the map phase for two users and the data structure after the shuffle phase.
This document provides an overview of popular Python libraries for data science, including NumPy, SciPy, Pandas, Scikit-Learn, matplotlib and Seaborn. It describes what each library is used for, such as NumPy for multidimensional arrays and mathematical operations, Pandas for data manipulation and analysis, and Scikit-Learn for machine learning algorithms. It also discusses reading and exploring data frames, selecting and filtering data, aggregating and grouping data, handling missing values, and data visualization.
Chapter 16-spreadsheet1 questions and answerRaajTech
This document discusses spreadsheets and Excel. It defines key spreadsheet concepts like workbooks, cells, cell addresses, and formulas. It describes built-in Excel functions for date/time, arithmetic, statistical, logical, and financial calculations. The document also covers charts, macros, and databases in Excel. Spreadsheets allow users to enter, manipulate, and analyze numerical data using formulas and functions in a tabular format.
Here are the answers to the Pandas questions:
1. A pandas Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively called index.
2. A DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). It is a DataFrame that contains columns, which may be of different value types (numeric, string, boolean etc.).
3. To create an empty DataFrame:
```python
import pandas as pd
df = pd.DataFrame()
```
4. To fill missing values in a DataFrame, we can use fillna()
Similar to mongoDB Project: Relational databases & Document-Oriented databases (20)
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
1. mongoDB Project
Relational databases & Document-Oriented databases
Athens University of Economics and Business
Dpt. Of Management Science and Technology
Prof. Damianos Chatziantoniou
| lkoutsokera@gmail.com
| stratos.gounidellis@gmail.com
Lamprini Koutsokera (8130074)
Stratos Gounidellis (8130029)
BDSMasters
2. SQL Server vs. mongoDB
2
Description Microsoft’s relational DBMS One of the most popular
document stores
Database model Relational DBMS Document store
Implementation language C++ C ++
Data scheme yes schema-free
Triggers yes no
Replication methods yes, depending the SQL-Server Edition Master-slave replication
Partitioning methods tables can be distributed across Sharding
several files, sharding through
federation
References: [1]
5. Required installations
5
References: [2]
1. Determine which MongoDB build you need.
2. Download MongoDB for Windows
3. Install MongoDB Community Edition.
4. Set up the MongoDB environment.
Mongodb installation
Required python packages installation
7. Python coding [1] – table parsing
7
Part 1 - Queries and the Aggregation Pipeline [1]
. . .
load() function
mongo shell
prep.js file
References: [3]
8. Python coding [1] – table parsing
8
Query 1 : How many students in your database are currently taking at least 1 class (i.e. have a class with
a course_status of “In Progress”)?
Part 1 - Queries and the Aggregation Pipeline [2]
Query 2 : Produce a grouping of the documents that contains the name of each home city and the
number of students enrolled from that home city.
9. Python coding [1] – table parsing
9
Query 3 : Which hobby or hobbies are the most popular?
Part 1 - Queries and the Aggregation Pipeline [3]
the most popular
the top 5 popular
10. Python coding [1] – table parsing
10
Query 4 : What is the GPA (ignoring dropped
classes and in progress classes) of the best student?
Part 1 - Queries and the Aggregation Pipeline [4]
Query 5 : Which student has the largest number of
grade 10’s?
11. Python coding [1] – table parsing
11
Query 6 : Which class has the highest average
GPA?
Part 1 - Queries and the Aggregation Pipeline [5]
Query 7 : Which class has been dropped the most
number of times?
12. Python coding [1] – table parsing
12
Query 8 : Produce of a count of classes that have been COMPLETED by class type. The class type is found
by taking the first letter of the course code so that M102 has type M.
Part 1 - Queries and the Aggregation Pipeline [6]
13. Python coding [1] – table parsing
13
Query 9 : Produce a transformation of the documents so that the documents now have an additional boolean
field called “hobbyist” that is true when the student has more than 3 hobbies and false otherwise.
Part 1 - Queries and the Aggregation Pipeline [7]
14. Python coding [1] – table parsing
14
Query 10 : Produce a transformation of the documents so that the documents now have an additional field that
contains the number of classes that the student has completed.
Part 1 - Queries and the Aggregation Pipeline [8]
15. Python coding [1] – table parsing
15
Query 11 : Produce a transformation of the documents in
the collection so that they look like the following output.
The GPA is the average grade of all the completed
classes. The other two computed fields are the number of
classes currently in progress and the number of classes
dropped. No other fields should be in there. No other fields
should be present.
Part 1 - Queries and the Aggregation Pipeline [9]
16. Python coding [1] – table parsing
16
Query 12 : Produce a NEW collection (HINT: Use $out in the aggregation pipeline) so that the new documents
in this correspond to the classes on offer. The structure of the documents should be like the following output.
The _id field should be the course code.
The course_title is what it was before. The numberOfDropouts is the number of students who dropped out. The
numberOfTimesCompleted is the number of students that completed this class. The currentlyRegistered array is
an array of ObjectID’s corresponding to the students who are currently taking the class. Finally, for the students
that completed the class, the maxGrade, minGrade and avgGrade are the summary statistics for that class.
Part 1 - Queries and the Aggregation Pipeline [10]
17. Python coding [1] – table parsing
17
Part 1 - Queries and the Aggregation Pipeline [11]
18. Python coding [1] – table parsing
18
Part 2 - Python & MongoDB [1]
python_mongodb.py: Implement simple operations on mongo database.
Connect to mongo database and collection. Connect to mongo database and collection and insert
a record.
19. Python coding [1] – table parsing
19
Part 2 - Python & MongoDB [2]
Connect to mongo database and collection and insert
multiple records.
Connect to mongo database and collection and print
its content.
python_mongodb.py: Implement simple operations on mongo database.
20. Python coding [1] – table parsing
20
Part 2 - Python & MongoDB [3]
Connect to mongo database and collection and update
Its documents.
Connect to mongo database and collection and print
specific field.
python_mongodb.py: Implement simple operations on mongo database.
21. Python coding [1] – table parsing
21
Part 2 - Python & MongoDB [4]
Connect to mongo database and collection and convert the
collection to a dataframe.
Connect to mongo database and collection and import
data from a dataframe.
python_mongodb.py: Implement simple operations on mongo database.
22. Python coding [1] – table parsing
22
Part 2 - Python & MongoDB [5]
1. Clone this repository:
2. Install the required python packages.
3. Run python_mongodb.py to implement basic operations (insert_one, insert_many,
update, delete_one, delete_many, etc.) on mongodb.
python_mongodb.py: Implement simple operations on mongo database.
24. Python coding [1] – table parsing
24
Part 3 - MapReduce (Word Count) [1]
MapReduce 1 : Write a map reduce job on the
students collection similar to the classic word
count example. More specifically, implement a
word count using the course title field as the text.
In addition, exclude stop words from this list. You
should find/write your own list of stop words. (Stop
words are the common words in the English
language like “a”, “in”, “to”, “the”, etc.)
References: [4,5]
27. Python coding [1] – table parsing
27
Part 3 - MapReduce (Average grade) [1]
MapReduce 2 : Write a map reduce job on the students
collection whose goal is to compute average
GPA scores for completed courses by
home city and by course type (M, B, P, etc.).
References: [4,5]