Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
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Data science and data analytics major similarities and distinctions (1)Robert Smith
Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Data Scientist Roles and Responsibilities | Data Scientist Career | Data Scie...Edureka!
** Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka "Data Scientist Roles and Responsibilities" PPT talks about the various Job Descriptions and specific skill sets for the different kinds of Data Scientists that are there. It explains why Data Science is the best career move, right now. Learn about various job roles and what they actually mean and the learning path to make a career in Data Science. Below are the topics covered in this module:
What is Data Science?
Who is a Data Scientist?
Types of Data Scientists
Skills Required to Become a Data Scientist
Data Science Masters Program @Edureka
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist
Instagram: https://www.instagram.com/edureka_lea...
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Data science and data analytics major similarities and distinctions (1)Robert Smith
Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
Data Science For Beginners | Who Is A Data Scientist? | Data Science Tutorial...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka "Data Science for Beginners" PPT talks about the basic concepts of Data Science, which includes machine learning algorithms as well as the roles & responsibilities of a Data Scientist. It also includes a demo using R Studio, that attempts to make sense of all the Data generated in the real world. This PPT talks about the most crucial aspects of data science and covers the following topics:
Why Data Science?
What is Data Science?
Who is a Data Scientist?
What does a Data Scientist do?
How to solve a problem in Data Science?
Data Science Tools
Demo
Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs
Check out our complete YouTube playlist here: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
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
Data Analyst vs Data Engineer vs Data Scientist | Data Analytics Masters Prog...Edureka!
** Data Analytics Masters' Program: https://www.edureka.co/masters-program/data-analyst-certification **
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Analyst vs Data Engineer vs Data Scientist" will help you understand the various similarities and differences between them. Also, you will get a complete roadmap along with the skills required to get into a data-related career. Below topics are covered in this tutorial:
Who is data analyst, data engineer and data scientist?
Roadmap
Required skill-sets
Roles and Responsibilities
Salary Perspective
Follow us to never miss an update in the future.
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
Slides from my presentation at the Data Intelligence conference in Washington DC (6/23/2017). See this link for the abstract: http://www.data-intelligence.ai/presentations/36
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
Introduction to Analytic fields. Data Analytics. What is Analytics. What it takes to be a Analyst, Different Profiles in Analytics fileds, Data science, data analytics, big data profiles, etc
Course - Machine Learning Basics with R Persontyle
This course is meant to be a fast-paced, hands-on introduction to Machine Learning using R. The course will be focusing mainly on basics of Machine Learning methods and practical implementation of these methods to solve real-world problems. This course aims to develop basic understanding of supervised learning methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: Classification and Regression.
For corporate bookings or to organize on-site training email hello@persontyle.comor call now +44 (0)20 3239 3141
www.persontyle.com
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
Want Free Career Counseling?
Just fill in your details, and one of our experts will call you!
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
Data Analyst vs Data Engineer vs Data Scientist | Data Analytics Masters Prog...Edureka!
** Data Analytics Masters' Program: https://www.edureka.co/masters-program/data-analyst-certification **
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Data Analyst vs Data Engineer vs Data Scientist" will help you understand the various similarities and differences between them. Also, you will get a complete roadmap along with the skills required to get into a data-related career. Below topics are covered in this tutorial:
Who is data analyst, data engineer and data scientist?
Roadmap
Required skill-sets
Roles and Responsibilities
Salary Perspective
Follow us to never miss an update in the future.
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
Slides from my presentation at the Data Intelligence conference in Washington DC (6/23/2017). See this link for the abstract: http://www.data-intelligence.ai/presentations/36
Data Science Training | Data Science Tutorial for Beginners | Data Science wi...Edureka!
***** Data Science Training - https://www.edureka.co/data-science *****
This Edureka tutorial on "Data Science Training" will provide you with a detailed and comprehensive training on Data Science, the real-life use cases and the various paths one can take to become a data scientist. It will also help you understand the various phases of Data Science.
Data Science Blog Series: https://goo.gl/1CKTyN
http://www.edureka.co/data-science
Introduction to Analytic fields. Data Analytics. What is Analytics. What it takes to be a Analyst, Different Profiles in Analytics fileds, Data science, data analytics, big data profiles, etc
Course - Machine Learning Basics with R Persontyle
This course is meant to be a fast-paced, hands-on introduction to Machine Learning using R. The course will be focusing mainly on basics of Machine Learning methods and practical implementation of these methods to solve real-world problems. This course aims to develop basic understanding of supervised learning methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: Classification and Regression.
For corporate bookings or to organize on-site training email hello@persontyle.comor call now +44 (0)20 3239 3141
www.persontyle.com
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
Want Free Career Counseling?
Just fill in your details, and one of our experts will call you!
Call us: +918308103366
WhatsApp Us: https://wa.me/+918308103366
Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Data Analytics has become a crucial part of the IT industry, as businesses strive to extract meaningful insights from the massive amounts of data they generate. APTRON's Data Analytics Training in Gurgaon is designed to equip learners with the knowledge and skills required to become proficient in the field.
An efficient data science team is crucial for deriving value from the humongous data a business collect. Learn how the data science team can help in this regard.
The analytics market is abuzz where professionals from various disciplines and background are leveraging data in their daily activities to get maximum insights and help a business to grow.
Challenges Of A Junior Data Scientist_ Best Tips To Help You Along The Way.pdfvenkatakeerthi3
One of the most fascinating fields today that is enabling businesses to improve their operations is data science.
Databases, network servers and official social media pages.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
If you’re learning data science, you’re probably on the lookout for cool data science projects. Look no further! We have a wide variety of guided projects that’ll get you working with real data in real-world scenarios while also helping you learn and apply new data science skills.
The projects in the list below are also designed to help you get a job! Each project was designed by a data scientist on our content team, and they’re representative examples of the real projects working data analysts and data scientists do every day. They’re designed to guide you through the process while also challenging your skills, and they’re open-ended so that you can put your own twist on each project and use it for your data science portfolio.
You can complete each project right in your browser, or you can download the data set to your computer and work locally! If you work on our site, you’ll also be able to download your code at any time so that you can continue locally, or upload your project to GitHub.
The sky is the limit here and what you decide to look into further is completely up to you and your imagination!
1. Learning by Doing
Learning by doing refers to a theory of education expounded by American philosopher John Dewey. It is a hands-on approach to learning, meaning students must interact with their environment in order to adapt and learn. This way of learning sharpen your current skills and knowledge and also helps in gaining new skills that could only be acquired by doing.
Car driving is a perfect example of this, you can read as much as you would like about the theory of driving and the rules, and this is very important, and the more you understand the theory the better you get in the practical part. But you will only be able to drive better by applying this knowledge on the real road. In addition to that, there are some skills and knowledge that will be only gained by actually driving.
Data science is the same as driving. It is very important to have solid theoretical knowledge and to regularly increase them to be able to get better while working on a project. However, you should always apply this theoretical knowledge to projects. By this, you will deepen your understanding of these concepts and Knowledge, have a better point of view of how they work in a real-life, and will also show others that you have strong theoretical knowledge and are able to put them into practice.
There are different types of guided projects. One of them is a guided project for
There are a lot of benefits for it:
It removes the barriers between you and doing projects
Saves you much time thinking about the project and preparing the data.
It allows you to apply the theoretical knowledge without getting distracted by obstacles.
Practical tips that can save your effort and time in the future.
#datasciencefree
#rohitdubey
#teachtechtoe
#linkedin.com/in/therohitdubey
Data analytics presentation- Management career institute PoojaPatidar11
1. The basic definition of Data, Analytics, and Data Analytics
2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc
Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.
Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.
3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)
4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)
5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication
6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.
7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making
8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path
9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities
10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills
11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.
2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.
3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.
4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.
Understanding Data Science: Unveiling the Basics
What is Data Science?
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract insights and knowledge from data. It involves collecting, processing, analyzing, and interpreting large and complex datasets to solve real-world problems.
Importance of Data Science
In today's data-driven world, organizations are inundated with data from various sources. Data science allows them to convert this raw data into actionable insights, enabling informed decision-making, improved efficiency, and innovation.
Intersection of Data Science, Statistics, and Computer Science
Data science borrows heavily from statistics and computer science. Statistical methods help in understanding data patterns, while computer science provides the tools to process and analyze large datasets efficiently.
Key Components of Data Science
Data Collection and Storage
The first step in data science is gathering relevant data from various sources. This data is then stored in databases or data warehouses for further processing.
Data Cleaning and Preprocessing
Raw data is often messy and inconsistent. Data cleaning involves removing errors, duplicates, and irrelevant information. Preprocessing includes transforming data into a usable format.
Exploratory Data Analysis (EDA)
EDA involves visualizing and summarizing data to uncover patterns, trends, and anomalies. It helps in forming hypotheses and guiding further analysis.
Machine Learning and Predictive Modeling
Machine learning algorithms are used to build predictive models from data. These models can make predictions and decisions based on new, unseen data.
Data Visualization
Visual representations of data, such as graphs and charts, help in understanding complex information quickly. Data visualization aids in conveying insights effectively.
The Data Science Process
Problem Definition
The data science process begins with understanding the problem you want to solve and defining clear objectives.
Data Collection and Understanding
Collect relevant data and understand its context. This step is crucial as the quality of the analysis depends on the quality of the data.
Data Preparation
Clean, preprocess, and transform the data into a suitable format for analysis. This step ensures that the data is accurate and ready for modeling.
Model Building
Select appropriate algorithms and build predictive models using machine learning techniques. This step involves training and fine-tuning the models.
Model Evaluation and Deployment
Evaluate the model's performance using metrics and test datasets. If the model performs well, deploy it for making predictions on new data.
Technologies Driving Data Science
Programming Languages
Languages like Python and R are widely used in data science due to their extensive libraries and versatility.
Machine Learning Libraries
Libraries like Scikit-Learn and TensorFlow prov
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
Unlocking Insights_ The Power of Data Analytics in the Modern World.pptxAPTRON Solutions Noida
In a world overflowing with data, the ability to extract meaningful information is a valuable skill. Data Analytics Training in Noida at APTRON Solutions is your gateway to a rewarding career in this ever-evolving field. Our commitment to excellence, practical approach, and industry connections make us the ideal choice for aspiring data analysts in Noida. Join us today and embark on a journey towards becoming a proficient data analyst ready to tackle the challenges of tomorrow's data-driven world. Your future in data analytics starts here at APTRON Solutions Noida!
https://t.ly/_xoaj
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?
Want to learn data analytics or just grab the information about data analytics and its future? https://coursedekho.com/data-analytics-courses-in-surat/
The significance of Data Science has impressively increased over recent years. The contemporary period is the intersection of data analytics with emerging technologies that involve artificial intelligence (AI), machine learning (MI), and automation. And these three things have an ocean of career opportunities. In this post, I am sharing with you some best Data Analytics Courses in Surat, with a detailed course curriculum and placements guarantee.
#education
#data
#DataAnalytics
#DataScience
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UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Leading Change strategies and insights for effective change management pdf 1.pdf
Data science tutorial
1.
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Table Of Content :
What is Data Science? 4
Why Data Science? 5
Role of a Data Scientist 6
Solving Problems with Data Science 7
Tools for Data Science 9
i. R 9
ii. Python 9
iii. SQL 10
iv. Hadoop 10
v. Tableau 11
vi. Weka 11
Applications of Data Science 11
i. Data Science in Healthcare 11
ii. Data Science in E-commerce 12
iii. Data Science in Manufacturing 12
iv. Data Science as Conversational Agents 12
v. Data Science in Transport 12
Summary 12
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Data Science has become one of the most demanded jobs of the 21st century.
It has become a buzzword that almost everyone talks about these days. But
what is Data Science? In this article, we will demystify Data Science, the role
of a Data Scientist and have a look at the tools required to master Data
Science.
So, let’s start Data Science Tutorial.
What is Data Science?
“Data Science is about extraction, preparation, analysis, visualization, and
maintenance of information. It is a cross-disciplinary field which uses
scientific methods and processes to draw insights from data. ”
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With the emergence of new technologies, there has been an exponential
increase in data. This has created an opportunity to analyze and derive
meaningful insights from data. It requires special expertise of a ‘Data Scientist’
who can use various statistical & machine learning tools to understand and
analyze data. A Data Scientist, specializing in Data Science, not only analyzes
the data but also uses machine learning algorithms to predict future
occurrences of an event. Therefore, we can understand Data Science as a
field that deals with data processing, analysis, and extraction of insights from
the data using various statistical methods and computer algorithms. It is a
multidisciplinary field that combines mathematics, statistics, and computer
science.
Why Data Science?
So, after knowing what exactly Data Science is, you must explore why Data
Science is important. So, data has become the fuel of industries. It is the new
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electricity. Companies require data to function, grow and improve their
businesses. Data Scientists deal with the data in order to assist companies in
making proper decisions. The data-driven approach undertaken by the
companies with the help of Data Scientists who analyze a large amount of data
to derive meaningful insights. These insights will be helpful for the companies
who wish to analyze themselves and their performance in the market. Other
than commercial industries, healthcare industries also use Data Science.
where the technology is in huge demand to recognize microscopic tumors and
deformities at an early stage of diagnosis.
The number of roles for Data Scientists has grown by 650% since 2012. About
11.5 Million jobs will be created by 2026 according to the U.S. Bureau of
Labor Statistics. Also, the job of Data Scientist ranks among top emerging jobs
on Linkedin. All the statistics point towards the growing demand for Data
Scientists.
Role of a Data Scientist
You might want to know who is a Data Scientist and what are his/her roles in
different fields. A Data Scientist deals with both unstructured and structured
data. The unstructured data is present in a raw format that requires extensive
data pre-processing, cleaning and organization in order to impart a
meaningful structure to a dataset. The Data Scientist then investigates this
organized data and analyzes it thoroughly to derive information from it using
various statistical methodologies. We use these statistical methods to describe,
visualize and hypothesize information from the data. Then with the usage of
advanced machine learning algorithms, the data scientist predicts the
occurrence of events and takes data-driven decisions.
A Data Scientist deploys vast arrays of tools and practices to recognize
redundant patterns within the data. These tools range from SQL, Hadoop to
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Weka, R, and Python. Data Scientists usually act as consultants employed by
companies where they participate in various decision-making processes and
creation of strategies. In other words, Data Scientists use meaningful insights
from data to assist companies in taking smarter business decisions. For
example – Companies like Netflix, Google and Amazon are using Data Science
to develop powerful recommendation systems for their users. Similarly,
various financial companies are using predictive analytics and forecasting
methods to predict stock prices. Data Science has helped to create smarter
systems that can take autonomous decisions based on historical datasets.
Through its assimilation with emerging technologies like Computer Vision,
Natural Language Processing and Reinforcement Learning, it has manifested
itself to form a greater picture ofArtificial Intelligence.
Solving Problems with Data Science
When solving a real-world problem with Data Science, the first step towards
solving it starts with Data Cleaning and Preprocessing. When a Data Scientist
is provided with a dataset, it may be in an unstructured format with various
inconsistencies. Organizing the data and removing erroneous information
makes it easier to analyze and draw insights. This process involves the
removal of redundant data, the transformation of data in a prescribed format,
handling missing values etc.
A Data Scientist analyzes the data through various statistical procedures. In
particular, two types of procedures used are:
● Descriptive Statistics
● Inferential Statistics
Assume that you are a Data Scientist working for a company that
manufactures cell phones. You have to analyze customers using the mobile
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phones of your company. In order to do so, you will first take a thorough look
at the data and understand various trends and patterns involved. In the end,
you will summarize the data and present it in the form of a graph or a chart.
You therefore, apply Descriptive Statistics to solve the problem.
You will then draw ‘inferences’ or conclusions from the data. We will
understand inferential statistics through the following example – Assume that
you wish to find out a number of defects that occurred during manufacturing.
However, individual testing of mobile phones can take time. Therefore, you
will consider a sample of the given phones and make a generalization about
the number of defective phones in the total sample.
Now, you have to predict the sales of mobile phones over a period of two years.
As a result, you will use Regression Algorithms. Based on the given historical
sales, you will use regression algorithms to predict the sales over time.
Furthermore, you wish to analyze if customers will purchase the product
based on their annual salary, age, gender, and credit score. You will use
historical data to find out whether customers will buy (1) or not (0). Since
there are two outputs or ‘classes’, you will use a Binary Classification
Algorithm. Also, if there are more than two output classes we use Multivariate
Classification Algorithm to solve the problem. Both of the above-stated
problems are part of ‘Supervised Learning’.
There are also instances of ‘unlabeled’ data. In this, there is no segregation of
output in fixed classes as mentioned above. Suppose that you have to find
clusters of potential customers and leads based on their socio-economic
background. Since you do not have a fixed set of classes in your historical data,
you will use the Clustering Algorithm to identify clusters or sets of potential
clients. Clustering is an ‘Unsupervised Learning’ algorithm.
Self Driving cars have become a trending technology. The principle behind the
self-driving car is autonomy, that is, being able to take decisions without
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human interference. The traditional computers required human input to yield
output. Reinforcement Learning has solved the problem of
human-dependence. Reinforcement Learning is about taking specific actions
to accumulate maximum reward. You can understand this with the following
instance: Assume that you are training a dog to fetch ball. Then you reward
the dog with a treat or reward each time it fetches the ball. You do not give it a
treat if it does not fetch the ball. The dog will realize the reward of treats if it
fetches the ball back. Reinforcement Learning uses the same principle. We
give a reward to the agent based on its action and it will try to maximize the
reward.
A Data Scientist will require tools and software to tackle the above-mentioned
problems. We will now take a look at some of the tools that a Data Scientist
uses to those problems.
Tools for Data Science
Data Scientists use traditional statistical methodologies that form the core
backbone of Machine Learning algorithms. They also use Deep Learning
algorithms to generate robust predictions. Data Scientists use the following
tools and programming languages:
i. R
R is a scripting language that is specifically tailored for statistical
computing. It is widely used for data analysis, statistical modeling, time-series
forecasting, clustering etc. R is mostly used for statistical operations. It also
possesses the features of an object-oriented programming language. R is an
interpreter based language and is widely popular across multiple industries
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ii. Python
Like R, Python is an interpreter based high-level programming language.
Python is a versatile language. It is mostly used for Data Science and Software
Development. Python has gained popularity due to its ease of use and code
readability. As a result, Python is widely used for Data Analysis, Natural
Language Processing, and Computer Vision. Python comes with various
graphical and statistical packages like Matplotlib, Numpy, SciPy and more
advanced packages for Deep Learning such as TensorFlow, PyTorch, Keras
etc. For the purpose of data mining, wrangling, visualizations and developing
predictive models, we utilize Python. This makes Python a very flexible
programming language.
iii. SQL
SQL stands for Structured Query Language. Data Scientists use SQL for
managing and querying data stored in databases. Being able to extract
information from databases is the first step towards analyzing the data.
Relational Databases are a collection of data organized in tables. We use SQL
for extracting, managing and manipulating the data. For example A Data
Scientist working in the banking industry uses SQL for extracting information
of customers. While Relational Databases use SQL, ‘NoSQL’ is a popular
choice for non-relational or distributed databases. Recently NoSQL has been
gaining popularity due to its flexible scalability, dynamic design, and open
source nature. MongoDB, Redis, and Cassandra are some of the popular
NoSQL languages.
iv. Hadoop
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Big data is another trending term that deals with management and storage of
huge amount of data. Data is either structured or unstructured. A Data
Scientist must have a familiarity with complex data and must know tools that
regulate the storage of massive datasets. One such tool is Hadoop. While being
open-source software, Hadoop utilizes a distributed storage system using a
model called ‘MapReduce’. There are several packages in Hadoop such as
Apache Pig, Hive, HBase etc. Due to its ability to process colossal data quickly,
its scalable architecture and low-cost deployment, Hadoop has grown to
become the most popular software for Big Data.
v. Tableau
Tableau is a Data Visualization software specializing in graphical analysis of
data. It allows its users to create interactive visualizations and dashboards.
This makes Tableau an ideal choice for showing various trends and insights of
the data in the form of interactable charts such as Treemaps, Histograms, Box
plots etc. An important feature of Tableau is its ability to connect with
spreadsheets, relational databases, and cloud platforms. This allows Tableau
to process data directly, making it easier for the users.
vi. Weka
For Data Scientists looking forward to getting familiar with Machine Learning
in action, Weka is can be an ideal option. Weka is generally used for Data
Mining but also consists of various tools required for Machine
Learning operations. It is completely open-source software that uses GUI
Interface making it easier for users to interact with, without requiring any line
of code.
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Applications of Data Science
Data Science has created a strong foothold in several industries such as
medicine, banking, manufacturing, transportation etc. It has immense
applications and has variety of uses. Some of the following applications of
Data Science are:
i. Data Science in Healthcare
Data Science has been playing a pivotal role in the Healthcare Industry. With
the help of classification algorithms, doctors are able to detect cancer and
tumors at an early stage using Image Recognition software. Genetic Industries
use Data Science for analyzing and classifying patterns of genomic sequences.
Various virtual assistants are also helping patients to resolve their physical
and mental ailments.
ii. Data Science in E-commerce
Amazon uses a recommendation system that recommends users various
products based on their historical purchase. Data Scientists have developed
recommendation systems predict user preferences using Machine Learning.
iii. Data Science in Manufacturing
Industrial robots have made taken over mundane and repetitive roles required
in the manufacturing unit. These industrial robots are autonomous in nature
and use Data Science technologies such as Reinforcement Learning and Image
Recognition.
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iv. Data Science as Conversational Agents
Amazon’s Alexa and Siri by Apple use Speech Recognition to understand
users. Data Scientists develop this speech recognition system, that converts
human speech into textual data. Also, it uses various Machine Learning
algorithms to classify user queries and provide an appropriate response.
v. Data Science in Transport
Self Driving Cars use autonomous agents that utilize Reinforcement Learning
and Detection algorithms. Self-Driving Cars are no longer fiction due to
advancements in Data Science.
Summary
While Data Science is a vast subject, being an aggregate of several technologies
and disciplines, it is possible to acquire these skills with the right approach. In
the end, Data Science is a very robust field that best fits people who have a
knack for experimentation and problem-solving. With a large number of
applications, Data Science has become the most versatile career.