Data, Data, and more Data!!!

Big data is exactly what is sounds like, large volumes of data sets. Today’s data is complex and comes in diverse forms that are constantly changing. Big data can be conveyed through volume, velocity, and variety. These elements, often called the “3 V’s of Big Data” define how data is expressed.

Volume refers to the vast amount of space that big data takes up. Devin Pickell puts data into perspective in an articled titled, “What is Big Data? A complete Guide.” Pickell explains that there is about, “2,5 quintillion bytes of data created daily.” According to Pickell, this would be the equivalent of 2.5 quintillion pennies wrapping around the earth five times. (Pickell, D.) Velocity explains how fast data is being generated. Advancements in technology continue to deliver data at faster rates. A good example of this is the newly developed 5G technology that is 500% faster than 4G LTE. Finally, variety refers to the many different types of data that in being produced today. Other elements of big data can include veracity, value, and variability. Veracity refers to data you can trust, Value determines the importance of the data, and variability determines what the data can do. (Botelho, B. & Bigelow, S.L.)

Data can be broken down into structured data and unstructured data. Before the invention of the internet, information was being stored in punch cards. Holes punched into punch cards crated a code that was used to program information into a machine. In the early stages of the internet, data storage began to be transitioned to a more technical format using relational databases. These databases allowed data to be stored in tables, making the information easy to research and understand. People could draw inferences from these numbers and values and how this data related to other data based on the way the information was organized into rows and columns. This type of data is structured data. Fast forward to the current day, and technology has advanced immensely. Now information comes in the form of email, text, images, video files, audio files, mobile activity, social media post, medical records, satellite imagery, surveillance imagery, raw data, etc. This is unstructured data, meaning it is not easily categorized or understood. Unstructured data requires more advanced tools for analysis.

Data Analysis is the process of breaking down the information provided by the data. This includes data collection, data modeling and data transformation. The field of data analysis involves various types, methods and tools of research that help organization understand what happened, why an event occurred or did not occur, what will or will not happen and how an event may unfold. Analyzing data helps organizations make data-based decisions by tracking customers behavior and interaction, using resources wisely and solve problems or prevent them from occurring in the future. Data Science is like data analysis but unlike data analysis, data science does not involve using an organizations existing data to create solutions. Data Science is the practice of discovering new ideas and looking into the future to make connections and reveal new information. (Springboard India) Data Science is a combination of mathematics, statistics, algorithms, predictive modeling, and machine learning. The data science life cycle includes capturing, preparing, and maintaining, processing, analyzing, and communicating. (IBM Cloud Education). Capturing involves looking all the available data and where it comes from. Preparing and maintaining includes deeper analysis of the data. Processing involves putting the data through predictive analysis, machine learning or deep learning algorithms (IBM Cloud Education). Analysis is deciding how the data can help with future insights. Communication involves organizing and sharing the data.

There is so much data to process and many people need to dive deeper into these concepts to gain working knowledge or be able to apply these concepts to their roles and organizations.

Big Data is a fast-growing field that cuts across all sectors and industries. With the rising demand for skilled Data scientists, check out some top-paying Big Data jobs:

1. Big Data Engineer
   Annual Salary Range: $130,000-$222,000
2. Data Architect
   Annual Salary Range: $119,750-$193,500
3. Data Warehouse Manager
   Annual Salary Range: $80,000-$160,250
4. Database Manager
   Annual Salary Range: $111,250-186,500
5. Business Intelligence Analyst
   Annual Salary: $87,500-$185,000
6. Data Scientist
   Annual Salary Range: $105,750-$180,250
7. Data Modeler
   Annual Salary Range: $80,750-$170,000
8. Database Developer
   Annual Salary Range: $101,250-$172,750
9. Database Administrator
   Annual Salary Range: $79,250-$160,500
10. Data Analyst
    Annual Salary Range: $83,750-$142,500
    (Burnham, 2020)

Phoenix TS offers data courses that can be a starting point for your data career or aid those already in the field. These courses include:

• Data Analysis Level 1 Training
• Data Analysis Level 2 Training
• DP-100T01: Designing and Implementing a Data Science Solution on Azure
• DP-201T01: Designing an Azure Data Solution
• Big Data on AWS Training
• Supervised Machine Learning: Classification Algorithms Training
• Supervised Machine Learning: Regression and Time-Series Analysis Training
• Data Science for Leaders: Building a Data Driven Strategy Training
• Data Science for Leaders: Data Science Methodology Training
• Introduction to Microsoft Power BI Training
• Manipulating and Understanding Data in Excel Training
• Supervised Machine Learning: Classification Algorithms Training
• Supervised Machine Learning: Regression and Time-Series Analysis Training
• Analytics Training
• Analytics, Basic Statistics And Metrics Training
• Analyzing Data with Power BI (MS 20778) Training

CONTACT: To find the data class best suited for your needs visit, phoenixts.com or call a Phoenix TS Training Consultant @240.667.7757

References:

Botelho, B. & Bigelow, S.L. (2021). Big data. Retrieved March 20, 2020, from https://searchdatamanagement.techtarget.com/definition/big-data

Calzon, B. (2021, March 25). Your Modern Business Guide To Data Analysis Methods And Techniques. Retrieved March 23, 2021, from https://www.datapine.com/blog/data-analysis-methods-and-techniques/

IBM Cloud Computing (2020, May 15). Data Science. Retrieved March 22, 2021, from https://www.ibm.com/cloud/learn/data-science-introduction

Pickell, D. (2018, August 22). What is Big Data? A Complete Guide. Retrieved March 22, 2020, from https://learn.g2.com/big-data

Pickell, D. (2018, November 16). Structured vs. Unstructured Data. Retrieved March 20, 2020, from https://learn.g2.com/structured-vs-unstructured-data

Springboard India.  (2019, August 12). Data Science vs. Data Analytics – How to decide which one is right for you? Retrieved March 20, 2019, from https://medium.com/@springboard_ind/data-science-vs-data-analytics-how-to-decide-which-one-is-right-for-you-41e7bdec080e

Vaidya, N. (2020, July 28). Data Science vs Big Data vs Data Analytics. Retrieved March 22, 2020, from https://www.edureka.co/blog/data-science-vs-big-data-vs-data-analytics/