The 7 Secrets You Will Never Know About Data Analytics
Data analytics is an excellent tool which is used to inspect large and complex volumes of data for the purpose of deriving significant inferences from it The term can involve other things such as gathering, interpreting, analyzing, and reporting of data by the use of software and other systems designed for the purpose of data analysis.
Almost every industry today uses the technologies of data analytics to come up with wise decisions and conclusions. Businesses use it to predict events that may happen in the future such as machine failure and also to understand the trend and behavior of their customers. In addition, specialists such as researchers, engineers, and scientists use this technique to validate or invalidate models, hypothesis, and theories. Also, in order to sustain themselves, companies like Microsoft, Google, and Amazon need to use this tool. It, therefore, shows that every sphere of activity needs data-analytics to survive.
To help you understand more about this approach, this article points out seven secrets that no one else will ever mention about data analytics.
Secrets about Data Analytics You Will Never Know
1. Learning Data Analytics is Not Easy
This is one of the things that no one will ever tell you Learning data science and analytical methods can seem very straightforward at first. But to understand all the concepts needed to become a professional in this field can take years of hard work with a lot of research and experimentations. It’s estimated that for a beginner to reach a junior-level, they should spend about six to nine months of learning. This is when they will understand the fundamentals and get ready for the advanced courses. However, most learning platforms on the internet claim that one can learn and become a data analyst expert by just watching a YouTube video, or by reading some SOL, R, and Python manuals. This is just an illusion of knowledge. The real truth is that learning from novice to expert will require a good deal of time, practice, and hard work.
2. “Data Analytics” is Not ‘Data Science”
Many people confuse these two terms as being the same. The latter is a very expansive term that includes almost every concept in statistics. It also talks about every type of data A wide range of tools is also used including machine learning, neural networks, data visualization, Hadoop, and use of programming languages. This is not the case with data analytics. In fact, the former works with structured data only It’s smaller and you don’t need to keep finding or exploring new ideas. It involves the use of a few tools like data and statistical modeling. But as a whole, we can say that the terms borrow concepts from each other.
3. Warning The Science of Data Can be a wonderful Investment
Perhaps, this is because it’s not easy to learn. A particular report published in August last year shows that there is a great shortage of data analysts and scientists in the US. The report emphasizes that more than 151000 (from 140000 in the year 2011) individuals were needed especially in the cities of NY. and LA in 2018. This can indicate how great it is to learn the science of data. According to Glassdoor.com, research shows that jobs related to data science were placed in the first position in a row in the US. for three years. The site also indicates the score of a data scientist is 4,8 out of 5 with an average salary of $110000. Learning this course, therefore, guarantees you a job, a good salary, and a beautiful working environment.
4. A Distance Degree Can Be the Best Option to Learn Data Analytics
When it comes to pursuing a course in data-analytics, online and distance degrees can be great to start with To study distance learning masters degree in one of the top colleges and universities, one must possess a first degree in statistics, computer science, mathematics, or economics. The same applies to an M Tech distance learning degree in either European, Asian or American universities. One must also have various qualities and skills such as good report-writing skills, ability to pay attention to detail, and capacity to analyze complex and large sets of data, a good understanding of data analysis software like STRATA, SPSS, and etcetera. The degree is set to take a duration of about two years. This depends on the college or university you are studying at What is more important is that you will have a broad understanding of the course which you can apply confidently in any company.
5. You Must Possess Problem Solving Skills to Become a Smart Analyst
This is the first step to become a smart data analyst. Knowing how to work with software such as SPSS and STRATA will not help you much. Most people think that learning Excel, R, and Tally will magically turn them into great analysts. This is not the case. Developing a problem-solving skill is the best thing to start with and this is the only thing that can place you among the top data scientists like Kira Radinsky, Andrew Ng, and Dean Abbott. Next would be understanding how companies use data, learning how to apply analytics skills by use of tools such as Python, Hive, R, SAS, and etcetera, and developing both written and verbal communication skills.
6. Security is One Major Issue When Working with Data
Security. This is one thing that demands attention nowadays. Because data has great value, it is highly demanded by cyber thieves and other internet criminals. Asa data analyst, one must be smart when it comes to securing data With the advancement of project-implementations and technology, this issue should be taken seriously and all holes regarding security are patched. In fact, this needs to be dealt with first to avoid security problems later.
7. Online Retailers Use Data Analytics to Understand What Customers Want
Retailers like a-bay, Amazon, and Alibaba.com are among the most popular online retailers in the world. To better understand their customers, they use the knowledge of data analysis. So instead of looking and finding products everywhere, they recommend the best products based on customer’s previous buying experiences. Some of these companies may use collaborative-filtering to commend products since they have a wide range of users and data from across the world.
This is when they compare customers with other users and recommend products they have bought based on what customers have searched on the platform. They do all this by the help of data they collect from everyone using their site This data includes every product users have clicked, the information they have read in the site, shipping address, and etc. They can also know where you live and the amount of salary that you earn. By use of this, they recommend items that people within your class have bought.
In conclusion, the above is all you need to know about data-analytics. The information can be used to understand the current trends in the use of data, and how companies are using the technique to improve their business performance. It can also help those who may want to study it as a profession to become smart analysts.