Data Analyst Roles & Responsibilities | EdurekaEdureka!
YouTube: https://youtu.be/nhyomoZS30M
( ** Data Analyst Master's Program: https://www.edureka.co/masters-program/data-analyst-certification ** )
This Edureka tutorial on "Data Analyst Roles and Responsibilities" will explain, what are the Roles and Responsibilities of a Data Analyst in the Industry. It also explains who is a Data Analyst and what does it takes to become one.
Following topics are included in the PPT:
Determine Organizational Goals
Mining Data
Data Cleaning
Analyzing Data
Pinpointing Trends and Patterns
Creating Reports with Clear Visualizations
Maintaining Databases and Data Systems
Follow us to never miss an update in the future.
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Data Analyst Roles & Responsibilities | EdurekaEdureka!
YouTube: https://youtu.be/nhyomoZS30M
( ** Data Analyst Master's Program: https://www.edureka.co/masters-program/data-analyst-certification ** )
This Edureka tutorial on "Data Analyst Roles and Responsibilities" will explain, what are the Roles and Responsibilities of a Data Analyst in the Industry. It also explains who is a Data Analyst and what does it takes to become one.
Following topics are included in the PPT:
Determine Organizational Goals
Mining Data
Data Cleaning
Analyzing Data
Pinpointing Trends and Patterns
Creating Reports with Clear Visualizations
Maintaining Databases and Data Systems
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
BDVe Webinar Series - Designing Big Data pipelines with Toreador (Ernesto Dam...Big Data Value Association
In the Internet of Everything, huge volumes of multimedia data are generated at very high rates by heterogeneous sources in various formats, such as sensors readings, process logs, structured data from RDBMS, etc. The need of the hour is setting up efficient data pipelines that can compute advanced analytics models on data and use results to customize services, predict future needs or detect anomalies. This Webinar explores the TOREADOR conversational, service-based approach to the easy design of efficient and reusable analytics pipelines to be automatically deployed on a variety of cloud-based execution platforms.
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
Learning Objectives
Prepare and use data flow diagrams to understand, evaluate, and document information systems.
Prepare and use flowcharts to understand, evaluate, and document information systems
Big Data Warehousing Meetup: Dimensional Modeling Still Matters!!!Caserta
Joe Caserta went over the details inside the big data ecosystem and the Caserta Concepts Data Pyramid, which includes Data Ingestion, Data Lake/Data Science Workbench and the Big Data Warehouse. He then dove into the foundation of dimensional data modeling, which is as important as ever in the top tier of the Data Pyramid. Topics covered:
- The 3 grains of Fact Tables
- Modeling the different types of Slowly Changing Dimensions
- Advanced Modeling techniques like Ragged Hierarchies, Bridge Tables, etc.
- ETL Architecture.
He also talked about ModelStorming, a technique used to quickly convert business requirements into an Event Matrix and Dimensional Data Model.
This was a jam-packed abbreviated version of 4 days of rigorous training of these techniques being taught in September by Joe Caserta (Co-Author, with Ralph Kimball, The Data Warehouse ETL Toolkit) and Lawrence Corr (Author, Agile Data Warehouse Design).
For more information, visit http://casertaconcepts.com/.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
BDVe Webinar Series - Designing Big Data pipelines with Toreador (Ernesto Dam...Big Data Value Association
In the Internet of Everything, huge volumes of multimedia data are generated at very high rates by heterogeneous sources in various formats, such as sensors readings, process logs, structured data from RDBMS, etc. The need of the hour is setting up efficient data pipelines that can compute advanced analytics models on data and use results to customize services, predict future needs or detect anomalies. This Webinar explores the TOREADOR conversational, service-based approach to the easy design of efficient and reusable analytics pipelines to be automatically deployed on a variety of cloud-based execution platforms.
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
Learning Objectives
Prepare and use data flow diagrams to understand, evaluate, and document information systems.
Prepare and use flowcharts to understand, evaluate, and document information systems
Big Data Warehousing Meetup: Dimensional Modeling Still Matters!!!Caserta
Joe Caserta went over the details inside the big data ecosystem and the Caserta Concepts Data Pyramid, which includes Data Ingestion, Data Lake/Data Science Workbench and the Big Data Warehouse. He then dove into the foundation of dimensional data modeling, which is as important as ever in the top tier of the Data Pyramid. Topics covered:
- The 3 grains of Fact Tables
- Modeling the different types of Slowly Changing Dimensions
- Advanced Modeling techniques like Ragged Hierarchies, Bridge Tables, etc.
- ETL Architecture.
He also talked about ModelStorming, a technique used to quickly convert business requirements into an Event Matrix and Dimensional Data Model.
This was a jam-packed abbreviated version of 4 days of rigorous training of these techniques being taught in September by Joe Caserta (Co-Author, with Ralph Kimball, The Data Warehouse ETL Toolkit) and Lawrence Corr (Author, Agile Data Warehouse Design).
For more information, visit http://casertaconcepts.com/.
Business Intelligence and Multidimensional DatabaseRussel Chowdhury
It was an honor that my employer assigned me to study with Business Intelligence that follows SQL Server Analysis
Services. Hence I started and prepared a presentation as a startup guide for a new learner.
* Thanks to all the contributions gathered here to prepare the doc.
Data warehouse 18 logical dimensional model for data warehouseVaibhav Khanna
Dimensions and Dimension Tables and their relation to the Fact tables in data warehouse dimensional data model. Illustrated using examples of Fact table and Dimension table
Workshop on "Data Management - The Foundation of all Analytics" given by John Aidoo, Data Analytics Manager at Central Insurance Company, Van Wert, Ohio.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. • What is Data Warehouse (DW)?
• Purpose and Definition
• DW Architecture
• Multidimensional Model
• Schema
Discussion
3. What is Data Warehouse (DW)?
“Data warehousing is a collection of methods, techniques, and tools
used to support knowledge workers—senior managers, directors,
managers, and analysts—to conduct data analyses that help with
performing decision-making processes and improving information
resources.”
4. Purpose and Definition
• DW is a store of information organized in a unified data model
• Data collected from a number of different sources
• Finance, billing, website logs, personnel, …
• Purpose of a data warehouse (DW): support decision making
• Easy to perform advanced analysis
• Ad-hoc analysis and reports
• Data mining: discovery of hidden patterns and trends
5. DW
• Solution: new analysis environment (DW) where data are
Subject oriented (versus function oriented)
Integrated (logically and physically)
Time variant (data can always be related to time)
Stable (data not deleted, several versions)
• Data from the operational systems are
Extracted
Cleansed
Transformed
Aggregated
Loaded into the DW
12. Multidimensional Model
• The multidimensional model
• Its only purpose: data analysis
• It is not suitable for OLTP systems
• More built in “meaning”
• What is important
• What describes the important
• What we want to optimize
• Easy for query operations
• Data is divided into:
• Facts
• Dimensions
• Facts are the important entity: a sale
• Facts have measures that can be aggregated
• Dimensions describe facts
• A sale has the dimensions Product, Store and Time
• Facts “live” in a multidimensional cube
14. • Dimensions are the core of multidimensional databases
• Dimensions are used for
• Selection of data
• Grouping of data at the right level of detail
• Dimensions consist of dimension values
• Dimension values may have an ordering
• Used for comparing cube data across values
• Especially used for Time dimension
• Dimensions have hierarchies with levels
• Typically 3-5 levels (of detail)
• Dimension values are organized in a tree structure
• Dimensions have a bottom level and a top level
• Dimensions should contain much information
Dimensions
15. Facts
• Facts represent the subject of the desired analysis
• The ”important” in the business that should be analyzed
• A fact is identified via its dimension values
• A fact is a non-empty cell
• The fact table stores facts
• One column for each measure
• One column for each dimension (foreign key to dimension table)
• Dimensions keys make up composite primary key
16. Measures
• Measures represent the fact property that the users want to study
and optimize
• A measure has two components
• Numerical value: (revenue)
• Aggregation formula (SUM): used for aggregating/combining a number of
measure values into one
• Measure value determined by dimension value combination
• Measure value is meaningful for all aggregation levels
18. Schema: Star Schema
• One fact table
• De-normalized dimension tables
• A dimension table stores a
dimension
• Optimized for querying large data
sets to support to support OLAP
cubes / analytical applications
