Data Analytics History
Learn basics of Data Analytics
Data Analytics(DA) is the process of examining data sets and draw conclusions from them. LiveEdu.tv is a great place to start learning and improve your Data Analytics skills with a section dedicated to Data Analytics tutorials and resources. Here you can watch how data analytics works. You can also search for any data analytics topic in our video library and premium projects. Join this data analytics community and improve your career.
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Data Analytics Introduction
Data analytics is an important sub-field of Data Science. Without Data analytics, it is impossible to make sense from huge data at the disposal of companies and enthusiast data scientists or data wranglers. What then is Data analytics? Data Analytics(DA) is the process of examining data sets and drawing conclusions from them. Specialized systems and software are used to achieve results. Almost every company uses data analytics to find more about their business, their end user, and the competition. Without DA, it is not possible to make informed decisions. Scientists also use DA, for academics purpose to verify or disapprove various scientific models.
The sole purpose of Data Analytics is to help understand the world around us. Companies use DA to understand their current situation, how they can use existing data to improve their revenues and help improve the operational efficiency. Data Analytics is also used for the purpose of the refining customer service, understand market trends and trying to get better in comparison to the competition.
History of Data Analytics
Since Data analytics is rooted in the field of Statistics, it has a long history associated with it. The first statistical project was done by Ancient Egyptians. Data analytics is used in collection mechanism. With statistics and data analytics, Herman Hollerith's Tabulating Machine enabled easy data recording on punch cards. It took the US over seven years to completely collect data and report the findings without the use of Data Analytics. With the Tabulating machine, the same task was done in 18 months, saving both time and budget.
Data analytics also played a key role in Relational databases and computation. Other than statistics and computing, the data warehouse and BI heavily used Data Analytics. In 2004, data analytics played a crucial role in the Google Web search. The next big use of data analytics came with the cloud boom. A lot of technologies in Big Data analysis on the cloud and enriched applications are deployed and communicate.
Data Analytics Tools
To work effectively, you need the right tools. There are plenty of Data Analytics tools online. Let’s list the 5 best Data Analytics tools.
- Tableau Public Tableau Public is an excellent data analytics tool. It is simple and intuitive to use. It provides amazing visualization and offers insight into the data. It is a great tool if you are learning as it provides a great playground to experiment and learn.
- OpenRefine OpenRefine is a data-cleaning software. The tool will help you clean your data for the next phase, i.e., Data Analysis. The tool was formerly known as GoogleRefine.
- Knime Knime is a great tool that enables you to analyze, manipulate and model your data. It uses visual programming to achieve all the results. You can also integrate machine learning and data mining components using the modular data pipelining concept.
- Google Fusion Tables If you are looking for a free version of Google sheet, then Google Fusion tables is all you need. You can do data mapping, analysis, visualization and tons of other cool stuff.
- Wolfram Alpha Wolfram Alpha is a computational engine and can be used for data analysis. It is a knowledge engine and will provide you directly access to tons of curated data from all around the world.
Education Ecosystem Data Analytics Project Creators
If you are wondering where to get started to learn Data Analytics, we will encourage you to check our Data Analytics section to view Data Analytics Project Creators on Education Ecosystem or check out our video library. Let’s list top 5 Data Analytics Project Creators on Education Ecosystem.
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Streamer Name: paintballbob
Project Name: Had a baby!BRB
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Streamer Name: xzvf
Project Name: Google TensorFlow. First time be gentle
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Streamer Name: jebpublic
Project Name: Analytics With Network Devices
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Streamer Name: devNubby
Project Name: Going HAM on Some Python
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Streamer Name: Koo
Project Name: Data Analysis Automation System (JAVA8 Style)
Big Data Best Books
There are plenty of Big Data books online. The best way to start learning Big Data is to invest in the books. So, why the wait? Let’s go through the best books for learning Big Data. These books are categorized into Beginner, Intermediate and Advanced. So pick the book that best suits you.
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Data Analytics for Beginners: Basic Guide to Master Data Analytics
by Paul Kinley
This book Data Analytics for Beginners will teach you How you can use data analytics to improve your business and how to plan data analysis to know exactly what your target group wants, How to implement descriptive analysis. You will learn the exact techniques that are required to master Data Analytics
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Data analytics: The Ultimate Beginner's Guide
by Lee Maxwell
The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data. It emphasizes more on machine learning and mining methods required for processing and decision-making.
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Head First Data Analysis: A learner's guide to big numbers, statistics, and good decisions
Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool.
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Data Analysis Using SQL and Excel
Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools.
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SPSS Statistics for Data Analysis and Visualization
by Keith McCormick, Jesus Salcedo, Jason Verlen
SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example.
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Performing Data Analysis Using IBM SPSS
by Lawrence S. Meyers, Glenn C. Gamst, A. J. Guarino
Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output.
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Advanced Analytics with Spark: Patterns for Learning from Data at Scale
by Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.
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Advanced Data Analysis and Modelling in Chemical Engineering
by Denis Constales, Gregory S. Yablonsky, Dagmar R. D'hooge, Joris W. Thybaut, Guy B. Marin
Advanced Data Analysis and Modeling in Chemical Engineering provides the mathematical foundations of different areas of chemical engineering and describes typical applications. The book presents the key areas of chemical engineering, their mathematical foundations, and corresponding modeling techniques.
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R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics)
Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.
Data Analytics Projects
The best way to learn is to evolve yourself with Projects. Let’s look at some of the best Data Analytics projects that you can follow. You can also find Data Analytics projects on Education Ecosystem. If you are interested in finding those, check the Education Ecosystem Data Analytics Project Creators section for more information.
Titanic Data Set Titanic Data set is a good way to get started with your data analytics journey. The data set is huge and can be used to find amazing hypothesis. It has 12 columns and 891 rows. You can get the data and start working on your project.
Explore this project!Insurance is all about numbers. Using the dataset will give a good idea on how Insurance companies work. Get the data set and start exploring.
Explore this project!The data sample offers an interesting perspective on how humans use smartphones. The data can help you gauge the frequency and other habits that you can go with using a smartphone.
Explore this project!The data enables you to see into the mind of the HubWay users. When a hubway user checks a bike out from a station, tons of data is shared by them. With this dataset, you can explore different avenues that the data offer.
Explore this project!Data Analytics Community
Data Analytics has a universal presence, which is evident from the community available. Not only will you find multiple websites that can help you meet up with like-minded people, but also offline meetups that can help grow Data Analytics and its related fields.
- Big Data Community Group This is the home of data related subjects. It is basically a home for data science which equates same love for data analysis.
- DMA Analytics Community If you are into market Data Analytics, then this community is for you. They share and improve upon old and new customer-centric, data-driven marketing.
- Education Ecosystem Here you can find all the awesome Project Creators who love to share their knowledge about Data Analytics.
Data Analytics Gurus
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Stephen Wolfram
Stephen Wolfram (born 29 August 1959) is a British-American computer scientist, physicist, and businessman. He is known for his work in computer science, mathematics, and in theoretical physics. He is the author of the book A New Kind of Science. In 2012 he was named an inaugural fellow of the American Mathematical Society. Check out his Education Ecosystem Project.
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Mike Gualtieri
Mike Gualtieri is one of the top Data Analytics gurus in the market. He does regular podcasts and keeps himself busy with blogs. He is also a researcher who is invested heavily in the future and how analytics can help unfold the mystery. He currently has 30,000 twitter followers.
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Vincent Granville
Vincent Granville is well-known for his contribution to AnalyticBridge newsletter. He is also the chief scientist and an expert when it comes to working with predictive modeling, data science, business analytics and text mining.
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Doug Laney
Doug Laney loves to dispatch his work at Gartner. He is also invested in other fields and use his data analytics knowledge in other fields.
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Gregory Piatetsky
Gregory Piatetsky is an avid data mining and analytics expert and regularly speak for the respective fields both through conference and consultancy. If you want regular updates, you can follow his Twitter feed for more information.
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Anil Batra
Anil Batra is a digital marketing and analytics executive. He is also an entrepreneur. He is famous for his presentation skills and has talked about key things. You can find him on his personal website.
Data Analytics Conferences
As Data Analytics is a trending topic in the market, there are many conferences out there that you can attend. Let’s list some of the best Data Analytics conferences out there.