This introduction show how OpenRefine can help any data project, from analytics, migration or reconciliation. OpenRefine powerful interface helps domains expert to explore, transform and enrich their data.
This is a Powerpoint Presentation based on the comparison of various available analytical tools. This includes various tools for business analytics and their detailed description.
This introduction show how OpenRefine can help any data project, from analytics, migration or reconciliation. OpenRefine powerful interface helps domains expert to explore, transform and enrich their data.
This is a Powerpoint Presentation based on the comparison of various available analytical tools. This includes various tools for business analytics and their detailed description.
Introduction To Data Science with Apache Spark ZaranTech LLC
Data science is an emerging work field, which is concerned with preparation, analysis, collection, management, preservation and visualization of an abundant collection of details. However, the term implies that the field is strongly connected to computer science and database
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Workshop with Joe Caserta, President of Caserta Concepts, at Data Summit 2015 in NYC.
Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends and actions, may be considered the "sexiest" job of the 21st century, but it requires an understanding of many elements of data analytics. This workshop introduced basic concepts, such as SQL and NoSQL, MapReduce, Hadoop, data mining, machine learning, and data visualization.
For notes and exercises from this workshop, click here: https://github.com/Caserta-Concepts/ds-workshop.
For more information, visit our website at www.casertaconcepts.com
Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skil...Simplilearn
In this presentation, we will decode the basic differences between data scientist, data analyst and data engineer, based on the roles and responsibilities, skill sets required, salary and the companies hiring them. Although all these three professions belong to the Data Science industry and deal with data, there are some differences that separate them. Every person who is aspiring to be a data professional needs to understand these three career options to select the right one for themselves. Now, let us get started and demystify the difference between these three professions.
We will distinguish these three professions using the parameters mentioned below:
1. Job description
2. Skillset
3. Salary
4. Roles and responsibilities
5. Companies hiring
This Master’s Program provides training in the skills required to become a certified data scientist. You’ll learn the most in-demand technologies such as Data Science on R, SAS, Python, Big Data on Hadoop and implement concepts such as data exploration, regression models, hypothesis testing, Hadoop, and Spark.
Why be a Data Scientist?
Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
Simplilearn's Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures, tools and analytics, and many more. The courseware also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualisation to model building and tuning. These skills will help you prepare for the role of a Data Scientist.
Who should take this course?
The data science role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:
IT professionals
Analytics Managers
Business Analysts
Banking and Finance professionals
Marketing Managers
Supply Chain Network Managers
Those new to the data analytics domain
Students in UG/ PG Analytics Programs
Learn more at https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training
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
Top 10 Data analytics tools to look for in 2021Mobcoder
This write-up has surrounded the top 10 tools used by data analysts, architects, scientists, and other professionals. Each tool has some specific feature that makes it an ideal fit for a specific task. So choose wisely depending on your business need, type of data, the volume of information, experience in analytical thinking.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Sustainability Investment Research Using Cognitive AnalyticsCambridge Semantics
In this webinar Anthony J. Sarkis, Chief Strategy Officer at Parabole, and Steve Sarsfield, VP Product at Cambridge Semantics, explore how portfolio managers are using the recently developed Parabole/ AnzoGraph DB integration as their underlying infrastructure for conducting ML and cognitive analytics at scale to exploit data to identify potential risks and new opportunities.
We explain how we use Grakn as part of a wider solution to deliver next generation Data Operations (Data Ops) tooling, enabling us to deliver sophisticated "Run Graph Analytics".
The Run Graph is a component to passively track and trace our data assets as they move across the organisation, and is used to quickly reverse engineer our global flows of data to better plan change and understand hidden dependencies. When operational failures do arise, we demonstrate how Grakn quickly allows us to assess the inferred impacts downstream, and to prioritise and communicate the impacts of outages to stakeholders.
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
Introduction To Data Science with Apache Spark ZaranTech LLC
Data science is an emerging work field, which is concerned with preparation, analysis, collection, management, preservation and visualization of an abundant collection of details. However, the term implies that the field is strongly connected to computer science and database
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Knowledge graphs - it’s what all businesses now are on the lookout for. But what exactly is a knowledge graph and, more importantly, how do you get one? Do you get it as an out-of-the-box solution or do you have to build it (or have someone else build it for you)? With the help of our knowledge graph technology experts, we have created a step-by-step list of how to build a knowledge graph. It will properly expose and enforce the semantics of the semantic data model via inference, consistency checking and validation and thus offer organizations many more opportunities to transform and interlink data into coherent knowledge.
Workshop with Joe Caserta, President of Caserta Concepts, at Data Summit 2015 in NYC.
Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends and actions, may be considered the "sexiest" job of the 21st century, but it requires an understanding of many elements of data analytics. This workshop introduced basic concepts, such as SQL and NoSQL, MapReduce, Hadoop, data mining, machine learning, and data visualization.
For notes and exercises from this workshop, click here: https://github.com/Caserta-Concepts/ds-workshop.
For more information, visit our website at www.casertaconcepts.com
Data Scientist vs Data Analyst vs Data Engineer - Role & Responsibility, Skil...Simplilearn
In this presentation, we will decode the basic differences between data scientist, data analyst and data engineer, based on the roles and responsibilities, skill sets required, salary and the companies hiring them. Although all these three professions belong to the Data Science industry and deal with data, there are some differences that separate them. Every person who is aspiring to be a data professional needs to understand these three career options to select the right one for themselves. Now, let us get started and demystify the difference between these three professions.
We will distinguish these three professions using the parameters mentioned below:
1. Job description
2. Skillset
3. Salary
4. Roles and responsibilities
5. Companies hiring
This Master’s Program provides training in the skills required to become a certified data scientist. You’ll learn the most in-demand technologies such as Data Science on R, SAS, Python, Big Data on Hadoop and implement concepts such as data exploration, regression models, hypothesis testing, Hadoop, and Spark.
Why be a Data Scientist?
Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data scientist you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
Simplilearn's Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures, tools and analytics, and many more. The courseware also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualisation to model building and tuning. These skills will help you prepare for the role of a Data Scientist.
Who should take this course?
The data science role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:
IT professionals
Analytics Managers
Business Analysts
Banking and Finance professionals
Marketing Managers
Supply Chain Network Managers
Those new to the data analytics domain
Students in UG/ PG Analytics Programs
Learn more at https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training
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
Top 10 Data analytics tools to look for in 2021Mobcoder
This write-up has surrounded the top 10 tools used by data analysts, architects, scientists, and other professionals. Each tool has some specific feature that makes it an ideal fit for a specific task. So choose wisely depending on your business need, type of data, the volume of information, experience in analytical thinking.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Sustainability Investment Research Using Cognitive AnalyticsCambridge Semantics
In this webinar Anthony J. Sarkis, Chief Strategy Officer at Parabole, and Steve Sarsfield, VP Product at Cambridge Semantics, explore how portfolio managers are using the recently developed Parabole/ AnzoGraph DB integration as their underlying infrastructure for conducting ML and cognitive analytics at scale to exploit data to identify potential risks and new opportunities.
We explain how we use Grakn as part of a wider solution to deliver next generation Data Operations (Data Ops) tooling, enabling us to deliver sophisticated "Run Graph Analytics".
The Run Graph is a component to passively track and trace our data assets as they move across the organisation, and is used to quickly reverse engineer our global flows of data to better plan change and understand hidden dependencies. When operational failures do arise, we demonstrate how Grakn quickly allows us to assess the inferred impacts downstream, and to prioritise and communicate the impacts of outages to stakeholders.
AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Conn...Cambridge Semantics
Thomas Cook, director of sales, Cambridge Semantics, offers a primer on graph database technology and the rapid growth of knowledge graphs at Data Summit 2020 in his presentation titled "AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World".
Big data analytics and machine intelligence v5.0Amr Kamel Deklel
Why big data
What is big data
When big data is big data
Big data information system layers
Hadoop echo system
What is machine learning
Why machine learning with big data
Assessing New Databases– Translytical Use CasesDATAVERSITY
Organizations run their day-in-and-day-out businesses with transactional applications and databases. On the other hand, organizations glean insights and make critical decisions using analytical databases and business intelligence tools.
The transactional workloads are relegated to database engines designed and tuned for transactional high throughput. Meanwhile, the big data generated by all the transactions require analytics platforms to load, store, and analyze volumes of data at high speed, providing timely insights to businesses.
Thus, in conventional information architectures, this requires two different database architectures and platforms: online transactional processing (OLTP) platforms to handle transactional workloads and online analytical processing (OLAP) engines to perform analytics and reporting.
Today, a particular focus and interest of operational analytics includes streaming data ingest and analysis in real time. Some refer to operational analytics as hybrid transaction/analytical processing (HTAP), translytical, or hybrid operational analytic processing (HOAP). We’ll address if this model is a way to create efficiencies in our environments.
Process Automation Trends in SAP® Supply Chain for 2023Precisely
As global supply chains continue to struggle due to ongoing disruptions, the need for process automation with SAP® supply chain processes has never been more pressing.
We recently sponsored a research study from SAPinsider, Process Automation in Supply Chain Benchmark Research Report, that sought to provide new insights and trends on how process automation can help to build robust supply chain capabilities required for today’s complex supply chain.
In this webinar, we will review this research report and discuss the top priorities for automation and the challenges it presents. Based on these results we will also discuss how Precisely Automate can address many of these challenges and help you achieve results in today’s challenging business environment.
Specifically, we will talk about:
· The current state of automation in supply chain
· The importance of resiliency and agility in managing dynamic supply chains
· Top strategic areas for process automation in supply chain
· How Precisely Automate drives automation success in SAP® supply chain processes
RDBMS gave us table schemas. A table schema, which is an essential metadata component, gave us the power to validate data types, and enforce constraints. In the age of varying data and schema-less data stores, how can we enforce these rules and how can we leverage metadata (even in RDBMS) to empower data validity, code checks, and automation.
This is a brief background into Big data (data lake) to put in context the importance of metadata from a governance perspective and more especially in todays heterogeneous big data platforms.
Apache CarbonData+Spark to realize data convergence and Unified high performa...Tech Triveni
Challenges in Data Analytics:
Different application scenarios need different storage solutions: HBASE is ideal for point query scenarios but unsuitable for multi-dimensional queries. MPP is suitable for data warehouse scenarios but engine and data are coupled together which hampers scalability. OLAP stores used in BI applications perform best for Aggregate queries but full scan queries perform at a sub-optimal performance. Moreover, they are not suitable for real-time analysis. These distinct systems lead to low resource sharing and need different pipelines for data and application management.
Delivering fast, powerful and scalable analyticsMariaDB plc
This session will provide insight on making the most of your data assets with analytics, and what you need for your next analytics project. We’ll showcase how the MariaDB AX solution delivers fast and scalable analytics using real-world use cases.
A wireless body area network (WBAN) is a special purpose sensor network designed to operate autonomously to connect various medical sensors and appliances , located inside and outside the body.
Big data business analytics | Introduction to Business AnalyticsShilpaKrishna6
Business analytics is the iterative, methodical and exploration of an organisations data with an emphasis on statistical analysis. Successful business analytics depends on data quality, skilled analysts who understand the Technologies and the business and an organisational commitment to using data to gain insight that informed business decisions.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
This video will give you an idea about Data science for beginners.
Also explain Data Science Process , Data Science Job Roles , Stages in Data Science Project
MapReduce is one of the most important and major component in Hadoop Ecosystem. Whenever we are having a large set of data then in the case of the huge data set will be divided into smaller pieces and processing will be done on them in parallel in MapReduce.
NoSQL is known as Not only SQL database, provides a mechanism for storage and retrieval of data.
In this section is discussing about two data models.
Aggregate Data Models
Distribution Data Models
Key-Value data model, Document data model, Column-family stores and Graph database are come under Aggregate data Models
Distribution data Models are Sharding, Master-slave replication and Peer-peer replication
Internet of Things(IoT) Applications
IoT has many applications. This video is talking about some of the iot applications,namely
Smart Home
Smart Wearables
Smart City
Smart Grid
Connected Cars
Connected Health
Smart Retail
Smart Farming
4 pillers of iot
1. M2M
(machine to machine)
2. WSN
(wireless sensor network)
3. RFID
(radio frequency identification device)
4. SCADA
(supervisory control and data acquisition)
Different number system used in computers to represent data.
Number system are of 4 types-Decimal,Binary,Octal&Hexadecimal
visit my channel for detailed explaination of conversions of number systems
https://youtu.be/elFs55aledc
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
10. Components of a big data pipeline are :
• The messaging system
• Message distribution support to various nodes for
further data processing.
• Data analysis system to derive decisions from data.
• Data storage system to store results and related
information.
• Data representation and reporting tools and alerts
system.
11. Important parameters that a big data pipeline
system must have -
• Compatible with big data
• Low latency
• Scalability
• Flexibility
• Economic
• A diversity that means it can handle various use cases.
12. In a Big Data pipeline system, the
two core processes are -
• The messaging system
• The data ingestion process
14. Batch Layer
Serving Layer
Incoming
data Queries
Real time Layer
Data Storage Layer
Batch
Engine
Real Time
Engine
Serving
Backend
Historical data Result data
18. • Review large amounts of data
• Spot trends
• Identify correlations and unexpected relationships
• Present the data to others
Big data visualisation techniques offer a fast
and effective way to :
19. Various Big Data Visualisation examples
include :
1. Linear
2. 2D/Planar/Geospatial
3. 3D/Volumetric
4. Temporal
5. Multidimensional
6. Tree/hierarchical
21. Big data privacy risks
1. Data breaches
2. Data brokerage
3. Data discrimination
22. Best practices for maintaining the
privacy of big data -
1. Employ real-time Monitoring
2. Implement homomorphic encryption
3. Avoid collecting too much data
4. Prevent internal threats
