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Phil Collins From EssayService On Using Analytics For Better Business Decisions

Phil Collins From EssayService
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How to Learn and Use Analytics

The modern socio-economic ecosystem is a gold mine for data, and the presence of AI and machine learning has made it easier to synthesize and gather data.

But having access to large swathes of big data is useless if you can’t find a way to analyze and use them to your benefit. Whether you run a multifaceted eCommerce platform, a digital agency, or even an essay writing service platform where college students can have professional writers rewrite my essay on time to improve their grades, you need analytical tools to stay competitive and relevant.

Through years of gathering and managing data, we’ve figured out ways of using analytics to generate better insights and make better business decisions. Let’s dive in!

Discover Your “Why”

Before you start learning how to use analytics, the first thing to do is outline your purpose. This will help you narrow down the learning scope to subjects, topics, and tools that are relevant to your field.

Let’s say you want to analyze an essay writing services review website, for example, the platform that offers the PaperWriter review by Essay-Reviews. In this case, your ultimate why should be to optimize engagement. To that effect, you can select tools, learning materials, and courses targeted toward that specific objective.

Narrow Down the Learning Scope

All data analysis efforts boil down to three categories: predictive, descriptive, and prescriptive.

Predictive analysis allows you to forecast things that might happen based on the available data. In the same vein, descriptive analysis describes what has already happened. And when you combine both of them, you can conduct prescriptive analysis to stay ahead of your competitors.

So once you figure out your reason, you need to narrow down the scope of your analysis to specific goals and targets.

Learn to Love Data

On the surface, data sounds fun. You get to play with incredible tools and programming languages. But deep down, data analysis is a tedious, tiring grind for individuals and institutions alike.

The only way you can appreciate your efforts is by loving data. So start working on projects that you feel passionately about. This way, you’ll have a fallback mechanism when things get too frustrating.

Find the Right Course

Some of us are self-starters, while others need a dedicated instructor to show them the ropes. Regardless of where you fall within this category, you need to find a data analytics course that focuses on your target field.

If you want a college degree in data analysis, programs like the UC Berkeley Data Analytics Bootcamp can help you in this regard. Alternatively, you can find other online courses on Udemy, Coursera, and LinkedIn Learning for your certification.

Before starting any course, go through a quick review to know if the topics covered are relevant to what you are trying to achieve.

Choose the Programming Language

Most data management tools come with no-code tools that provide an avenue for people without coding experience to analyze data. However, knowing a programming language is always valuable for a data analyst or enthusiast.

Some programming languages for data analysis include:

  • Python
  • MATLAB
  • PHP
  • Java
  • Javascript
  • R
  • C++

You don’t need to learn all these programming languages before you can start analyzing your data. You only need to master one of them. Experts recommend starting with Python if you are new to programming.

Apart from programming languages, you must be proficient in math and computer science. A solid background in these fields and tools will help you understand and utilize artificial intelligence and machine learning.

Choose the Right Data Management Platform

Most people opt for Google Analytics because it is the marquee data management tool for several industries. But other tools exist besides Google Analytics.

If you want to get the best out of the data available, use tools like Microsoft Power BI, Visual Analyzer, Cognos, and Oracle. These platforms guarantee better performance, tighter security, transparent reporting, and workflow efficiency.

You could also use cloud tools to tap into the additional security they provide. Some notable names include Google Cloud, Azure, SAP, and Amazon Web Services.

Learn How to Communicate

You can gather and analyze all the data you want, but if you can’t share your insights and findings, you won’t be able to use the data to its full capacity.

In essence, your data should tell a story clear enough that people not directly involved with the project can understand the direction at one glance.

Whether you are using a simple Excel graph or relying on heavy-duty tools like Visio and Tableau, you need to be able to break down large swathes of information into digestible chunks for the user.

Connect with the Like-Minded People

Data analysis is an ever-evolving field that requires constant research to keep up with new technologies. So while you familiarize yourself with automated tools, don’t forget about making human connections.

Reach out to other data analysts in your field—and even outside your industry. Join data communities to connect with people who share the same vision. Who knows, you might find someone working on a similar problem as you are.

And most importantly, you should keep an eye on the market as well as upcoming data analysis technologies.

Conclusion

Data analysis is constantly changing as innovative technologies and ideas become part of our daily lives. But to get the best data analysis in any field, you need to specify a goal beforehand. This will help you narrow down the scope to essentials. You also need to learn the right programming languages, master the right tools, and choose the best courses. Finally, and most importantly, use advanced data visualizers to communicate insights to others.

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