Introduction to Data Analytics with R, Tableau & Excel | Data Analytics Career in 2019 & Beyond
Did you know? by 2020, every human being will create over 1.5 megabytes of data per second on average. In 2025, the sum of digital data will add up to 180 zettabytes, which is over 1600 trillion gigabytes. Considering these numbers, it is an understatement to say that the data is only BIG.
So, what is Big Data and how is it related to Data Analytics?
Big data is a large volume of data that consists of both structured and unstructured data forms. helps organizations to draw meaningful insights from their data to learn and grow. Thus, it’s the data that matters and not it’s volume.
Structured data is organized information that can be accessed with the help of simple search algorithms. While Unstructured data as the name suggests is less uniform and thus difficult to work with. The lack of structure makes compiling data at a time and energy-consuming task.
The Relation Between Big Data and Analytics:
The process of uncovering hidden patterns, unknown correlations, market trends, customer preferences and other useful information from both structured and unstructured data is called Data analytics.
The Benefits of Using Data Analytics.
• Analytics help organizations make informed decisions and choices.
• It boosts the overall performance of the organization by refining the financial processes, increasing visibility, providing insights and granting control over managerial processes.
• It detects fraud and flaws by keeping a close vigil.
• It further Improves the IT economy by increasing agility and flexibility of systems.
The above mentioned are just a few advantages, however, the list goes on.
Despite the growing interest in data analytics, there is an acute shortage of professionals with good data analytical skills. Thus, only 0.5% of the data we produce is analysed.
There is a serious shortage of skilled professionals. Thus, the ones who are called proficient data analysts must have certain skills.
They must possess a varied skill-set like computer science, data mining and business management to provide from the data they are working on.
Their computer science skills should include both programming skills and technical skills
• Programming Skills: Python, R, and Java
• Technical Skills: Knowledge of platforms like Hadoop, Hive, Spark, etc.,
Their data skills should include Warehousing Skills, Quantitative & Statistical Skills & Analytical & Interpretation Skills
• Warehousing Skills: Data scientist must possess good analytical skills
• Quantitative & Statistical Skills: As technology is a key aspect of big data analysis, quantitative and statistical skills are essential
• Analytical & Interpretation Skills: knacks to analyses and interpret data
The business skills are important to use the data effectively and to improve various aspects such as operations, finance, productivity, etc.,
These are the skills that make the data analytics professional an invaluable asset to the organization.
The lack of skilled data professionals is an opportunity in turn for upcoming data scientists to make their mark in the field of data analytics.
As the significance of data grows in the business world, the value of professionals working in analytics also increases. This is creating a variety of job roles amongst organizations and they are.
Data Analyst, Analytics Consultant, Business Analyst, Analytics Manager, Data Architect, Metrics and Analytics Specialist, Analytics Associate these are only some of the job titles that data analytics professionals can acquire in business organizations. The list is presumably greater.
The Chief Software Platforms are R, Tableau & Excel
R is one of the robust statistical computing solutions. Tableau is the foremost business intelligence platform that offers eminent data visualization and exploration capabilities. Coming to Excel, it is used for managing, manipulating and presenting data. When combined, Tableau, R and Excel offer the most powerful and complete data analytics solutions.
So, the demand for data analytics and its professionals is augmenting at a great pace. Organizations are interested in analysts to maximize their data potential, while professionals are interested in capitalizing on the analytical crunch in many parts of the world.
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