This course provides an overview of data analytics and business intelligence. It teaches students how to analyze and tell stories with data, which is an in-demand skill as data collection increases. The course covers topics such as SQL, data modeling, Power BI, data warehousing, and big data to prepare students for careers as data analysts. Upon completing the hands-on training, students will feel confident to begin work in the industry analyzing, extracting, transforming, and loading data according to business requirements.
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Just finished a basic course on data science (highly recommend it if you wish to explore what data science is all about). Here are my takeaways from the course.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
Data Science is a form of science that focuses on dealing with huge chunks of data by using modern data analysis tools and techniques to discover hidden patterns, meaningful insights, and make critical business decisions.
A Data Science professional has to utilize complicated machine learning algorithms to develop predictive models. There could be multiple sources present in different formats used in data analysis.
Want to pursue career in Data Science? Have knowledge of limited opportunities? Don't worry!
This e- book helps readers to know about top career opportunities one can pursue in Data Science. Further info.- https://www.henryharvin.com/business-analytics-course-with-python
Big data, Machine learning and the AuditorBharath Rao
Check an insight as to how an Auditor can leverage Analytics, machine learning, and Technology to achieve absolute assurance and to effectively control the Fraud Risk present in the Enterprise.
Data and Analytics Career Paths, Presented at IEEE LYC'19.
About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
Reinventing Auditing with Machine LearningAndrew Clark
Internal Audit is responsible for providing the 3rd line of defense assurance over the effectiveness of controls in mitigating enterprise risks. We are primarily a judgment-based operation, relying on "humanness" to ascertain if risks are sufficiently being mitigated. This sort of environment makes it difficult to employ machine learning, given the ambiguity of decisions and the need for interpretability to back up decisions that were made. However, these limitations give us the ability to become more imaginative, finding unique ways to employ machine learning. In this talk, Andrew will provide two examples of prototypes being used in audit, an unsupervised machine learning exploratory "clustering" environment to provide insight into looking at data in new ways; and a supervised NLP model that classifies audit reports into different classes for use in reporting.
How relevant is Predictive Analytics relevant today?Steven Mugerwa
This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the
Howdy!Take a look at this article and discover cool graduation thesis sample that we prepared for you. Get more here https://www.graduatethesis.org/graduate-thesis-sample/
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
Best Data Analytics Certification Course Training Institute in Malaysia: 360DigiTMG is the best Data Analytics using Python Training Institute In Malaysia providing Data Analytics Training Classes by real-time faculty with course material.
Adding Open Data Value to 'Closed Data' ProblemsSimon Price
Drawing on cutting edge examples from the University of Bristol and the City of Bristol, Simon will discuss innovative applications of data science that derive business value from open data through enriching and integrating with confidential 'closed data'. He also highlights recent technological advances that are enabling open data science on highly sensitive closed data.
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
System Dynamics, Analytics & Big Data (16th Conference of the UK Chapter of t...Michael Mortenson
This talk investigates the relationship between system dynamics, analytics and big data. Drawing on both a historical analysis and text analytics, similarities and differences are identified, and some suggestions on how future research may provide value for the System Dynamics community.
Learn All about Data Science from the Best Private University in KarnatakaREVA University
Completing Masters in Data Science degree can reshape your career path, though it demands dedication and time to gain the necessary skills and land the right job. To assist you, we've crafted a detailed plan for building a career in Data Science.
Big Data Courses In Mumbai at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
Attend The Data Science Course in Bangalore From ExcelR. Practical Data Science Course in Bangalore Sessions With Assured Placement Support From Experienced Faculty. ExcelR Offers The Data Science Course in Bangalore.
Data Science is a form of science that focuses on dealing with huge chunks of data by using modern data analysis tools and techniques to discover hidden patterns, meaningful insights, and make critical business decisions.
A Data Science professional has to utilize complicated machine learning algorithms to develop predictive models. There could be multiple sources present in different formats used in data analysis.
Want to pursue career in Data Science? Have knowledge of limited opportunities? Don't worry!
This e- book helps readers to know about top career opportunities one can pursue in Data Science. Further info.- https://www.henryharvin.com/business-analytics-course-with-python
Big data, Machine learning and the AuditorBharath Rao
Check an insight as to how an Auditor can leverage Analytics, machine learning, and Technology to achieve absolute assurance and to effectively control the Fraud Risk present in the Enterprise.
Data and Analytics Career Paths, Presented at IEEE LYC'19.
About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
Reinventing Auditing with Machine LearningAndrew Clark
Internal Audit is responsible for providing the 3rd line of defense assurance over the effectiveness of controls in mitigating enterprise risks. We are primarily a judgment-based operation, relying on "humanness" to ascertain if risks are sufficiently being mitigated. This sort of environment makes it difficult to employ machine learning, given the ambiguity of decisions and the need for interpretability to back up decisions that were made. However, these limitations give us the ability to become more imaginative, finding unique ways to employ machine learning. In this talk, Andrew will provide two examples of prototypes being used in audit, an unsupervised machine learning exploratory "clustering" environment to provide insight into looking at data in new ways; and a supervised NLP model that classifies audit reports into different classes for use in reporting.
How relevant is Predictive Analytics relevant today?Steven Mugerwa
This is my view on how relevant is Predictive Analytics relevant today. Although its a high level view, it gives great insights to a person who is looking for somewhere to begin. This was an essay for the
Howdy!Take a look at this article and discover cool graduation thesis sample that we prepared for you. Get more here https://www.graduatethesis.org/graduate-thesis-sample/
Data Science is the Sexiest job in 21st century. Big Data Concept is going to rule the 21st century. Here is the presentation to give complete information and overview of data science big data.
Best Data Analytics Certification Course Training Institute in Malaysia: 360DigiTMG is the best Data Analytics using Python Training Institute In Malaysia providing Data Analytics Training Classes by real-time faculty with course material.
Adding Open Data Value to 'Closed Data' ProblemsSimon Price
Drawing on cutting edge examples from the University of Bristol and the City of Bristol, Simon will discuss innovative applications of data science that derive business value from open data through enriching and integrating with confidential 'closed data'. He also highlights recent technological advances that are enabling open data science on highly sensitive closed data.
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
System Dynamics, Analytics & Big Data (16th Conference of the UK Chapter of t...Michael Mortenson
This talk investigates the relationship between system dynamics, analytics and big data. Drawing on both a historical analysis and text analytics, similarities and differences are identified, and some suggestions on how future research may provide value for the System Dynamics community.
Learn All about Data Science from the Best Private University in KarnatakaREVA University
Completing Masters in Data Science degree can reshape your career path, though it demands dedication and time to gain the necessary skills and land the right job. To assist you, we've crafted a detailed plan for building a career in Data Science.
Big Data Courses In Mumbai at Asterix Solution is designed to scale up from single servers to thousands of machines, each offering local computation and storage. With the rate at which memory cost decreased the processing speed of data never increased and hence loading the large set of data is still a big headache and here comes Hadoop as the solution for it.
http://www.asterixsolution.com/big-data-hadoop-training-in-mumbai.html
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.
A data analyst collects, cleans, and interprets data sets to provide an answer or solve an issue. They work in a variety of industries, including business, banking, criminal justice, science, medical, and government. Data analyst jobs in US can take various shapes depending on the question you're attempting to answer. Being a data analyst might also lead to other employment opportunities. Many data analysts eventually become data scientists.
Businesses cannot compete without data. Every organization produces and consumes it. Data trends are hitting the mainstream and businesses are adopting buzzwords such as Big Data, data vault, data scientist, etc., to seek solutions for their fundamental data issues. Few realize that the importance of any solution, regardless of platform or technology, relies on the data model supporting it. Data modeling is not an optional task for an organization’s data remediation effort. Instead, it is a vital activity that supports the solution driving your business.
This webinar will address emerging trends around data model application methodology, as well as trends around the practice of data modeling itself. We will discuss abstract models and entity frameworks, as well as the general shift from data modeling being segmented to becoming more integrated with business practices.
Takeaways:
How are anchor modeling, data vault, etc. different and when should I apply them?
Integrating data models to business models and the value this creates
Application development (Data first, code first, object first)
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
Self-Service data analysis holds the promise of more rapid time-to-value for both business and IT users as advanced tooling & visualization helps make sense of raw and source data sets. Does this mean that the paradigm of ‘design-then-build’ that’s typical of data modeling is no longer relevant? Or is it more relevant than ever, as more eyes on the data means more questions about core business definitions.
Join Donna Burbank for this webinar to discuss the realities of where data modeling fits in this new paradigm.
Emerging opportunities in the age of dataEjaz Siddiqui
We live in a data-driven world. There are more than 4 billion people around the world using the internet.
This show an unprecedented spread and growth of digital devices. These digital devices (Mobiles, Computers, Watches, IoT etc) are the factories for creating data. It means we live in the Age of Data, and it’s expanding at astonishing rates. We may need to unplug and take a break from time to time, but data never sleeps.
This generation of huge data presents many new challenges as well as opportunities. There would be huge opportunity for the people who could collect, process, manage, drive insights and make useful decisions from this data. Certain fields are becoming very important and necessary to manage and process this data.
LDM Webinar: Data Modeling & Business IntelligenceDATAVERSITY
Business Intelligence (BI) is a valuable way to use information to show the overall health and performance of the organization. At its core is quality, well-structured data that allows for successful reporting and analytics. A data model helps provide both the business definitions as well as the structural optimization needed for successful BI implementations.
Join this webinar to see how a data model underpins business intelligence and analytics in today’s organization.
Translating AI from Concept to Reality: Five Keys to Implementing AI for Know...Enterprise Knowledge
Lulit Tesfaye explains how foundational knowledge management and knowledge engineering approaches can play a key role in ensuring enterprise Artificial Intelligence (AI) initiatives start right, quickly demonstrate business value, and “stick” within the organization. The presentation includes real world case studies and examples of how organizations are approaching their data and AI transformations through knowledge maturity models to translate organizational information and data into actionable and clickable solutions. Originally delivered at data.world Summit, Spring 2022.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
Is your business intelligence team backlogged with information demands? You're not alone. Business users have an insatiable appetite for information, yet current delivery methods can't keep pace.
Join us for an educational webinar discussing how to meet users' demands for information with your existing business intelligence platform and staff.
During our session, you will learn how to automate business intelligence solution delivery.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
This Data Science course emphasises on Project-Based Learning to meet the learning needs of students from various background and make them job-ready. Learn Data Science like a pro and our methodology invoke thought process in the learner to solve problems. Post completion of the course, learners could independently build a Data Science solution using Machine Learning models. You would be offered a chance to secure an internship with relevant industries and participate in our hackathons.
Top 10 tredning technologies to learn in 2021Lokesh Agarwal
In this world of digitalization, technologies are expanding rapidly. As the world foremost tech news contributor, it is the duty of us to keep everyone updated with the newest trends of the top 10 trending technologies in 2021. Technology and programming language are so important in day to day lifestyle to make the livelihood more facile. These computer scientists and professionals are regularly making the bests out of anything. Technology has taken a face of more productiveness and give the best to the nation. In the present scenario, everything is done through the technical process, you don’t have to bother about doing work, everything will be done automatically. In this article, some important technologies which are new in the market are explained according to the career preferences. So let’s have a look into the top 10 trending technologies in2021 and its impression in the coming future.
In this world of digitalization, technologies are advancing rapidly. As the world’s foremost tech news contributor, it is our duty to keep everyone updated with the latest top 10 trending technologies in 2021.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. FUNDAMENTALS OF DATA ANALYTICS
Course Overview
In this course you will learn the skill of how to tell a story with data.
World Economic Forum forecasts that data analysts will be in demand due
to increase in data collection and usage. According to the Economist,
organizations view data analysis as one of the most crucial future specialties
due to the value that can be derived from data. Data is considered to be the
new oil of the digital age & one of the most valuable resources for an
organization; thus protecting and analyzing data is becoming extremely
important. Furthermore, Data is more abundant and accessible than ever in
today’s business environment. In fact, according to Forbes, 2.5 quintillion
bytes of data are created each day. With an ever-increasing skill gap in
data analytics, the value of data analysts is continuing to grow, creating
new jobs and career advancement opportunities.
Professionals planning to enter the Data Science and Analytics field
will have their pick of jobs and enjoy lucrative salaries. According to an IBM
report, data and analytics jobs are predicted to increase by 15 percent to
2.72 million jobs by 2020, with the most significant demand for data analysts
is in finance, insurance & information technology.
This Course is designed to teach data analysis and business
intelligence in a hands-on manner and prepare the participants for a career
in this field. The course will provide instruction and hands-on training for
students to feel confident and to begin work in the industry as a Business
Intelligence Analyst or a Data Analyst.
A Data Analyst profile can be associated with mining, extraction,
transformation, cleansing, modeling and loading of data according to
business requirements. For those with an analytical mind, this is the place
to be. There is limited talent available in the industry for this work and the
scope is growing fast. Every industry wants Business Intelligence in order
to make smarter decisions.
2. Course Content and Topics:
The program will provide instruction and training for the participants to feel
confident to start working in the industry. By the end of this program
participants would have learned the following:
• Introduction to the world of data
• Database Management System a Basic Over View
• Database Structure and SQL Select statements
• Filtering Data in SQL
• Sorting data in SQL
• Functions in SQL
• Set operators and derived tables in SQL
• Joins and subqueries in SQL
• Data definition language (DDL)
• Normalization and database design
• Connecting data to Power BI
• Data storytelling and data transformation using Power BI
• How to filters data and create hierarchies
• Calculated fields and Measures
• Sharing and Publishing Dashboards
• Security Features in Power BI
• Understanding Big Data and Data warehousing
• Class Project or Exam
Note: After completing this course you should be able to sit for the
following Certification Examinations - Analyzing and Visualizing Data with
Microsoft Power BI (Exam 70 - 778)
