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Essential Tools To Enable Data Analysis

Essential Tools To Enable Data Analysis

Est. Reading: 3 minutes
The data analytics career sector has been seeing substantial growth over the years as more businesses rely on data analytics to be able to deliver the right products and services to the right people. Data analytics also help us identify any problem areas within the different industries and how those problems can be addressed and solved.

But why does data analytics matter so much? And what essential tools are there to help data analysts and scientists compile and complete their detailed research using data science? This article will explore some tools and which ones work for which projects, plus will also offer some insight on how most of these tools do the same thing and how you need to decide which ones best align with your projects.

Choosing Your Tools

Based on your project, there are several factors that contribute to choosing the perfect data analysis tools. These factors include but are not limited to the type of data you want to collect, the size of the data you are collecting and analysing, the level of technicality (is this a scientist or analyst job?), budget, and so much more. These help you form a clear idea of which tools you will need for your data analytics.

For Data Storage, Capturing & Editing

If you need tools that will help you store, capture and edit basic data easily, then the famous Google Sheet and Excel are your best friends (tools). Both tools work almost the same, and Google Sheet is free, while Excel isn’t. With these tools, you can also create graphs & charts because it is convenient to make, and Excel and Google Sheet offer a wide variety of chart types: from bar charts, stacked bar charts, clustered bar charts, pie charts, scatterplots, box & whisker plots, radar charts — you name it! Some common uses for these tools include:

  • Pivot Tables: Say you have a range of product sales data and want to answer basic questions such as “which products are popular during certain times?”, you can easily get answers by creating pivot tables.
  • Calculations: You want to perform calculations on the collected data. These tools provide multiple ways for you to do that.
  • Data manipulation: You want to clean or manipulate the data. Excel has multiple ways to edit certain parts of the data (e.g. deleting the 4–6th character positions in each cell, splitting a column into multiple). This way, you will not have to restart your data process due to tiny faults that can be easily corrected.

Pro-Tip: Invest in cloud-based data analysis tools which will offer you access to your files wherever and whenever you need. The world is moving digitally, after all, and if you are a data analyst or scientist, having your data easily accessible will benefit you a great deal. It shows evolution, preparedness and the ability to move with the times.

For Marketing, Surveys & Business Intelligence

If you are looking for something that allows you to form data analytics for marketing purposes, the likes of Polymer Search and Tableau are what you will need to be familiar with. These are the kind of tools that transform your data into interactive web applications in no time while you upload your data onto its web tool. It is also easier to share analytics with other people through such tools. With these tools, you can also access several analytics functions such as:

  • Interactive pivot tables: To answer certain questions about the data in a few clicks by choosing your variables such as “smart pivot.” Since everything is interactive, you don’t need to waste time setting up slicers, and you can easily filter in/out data by clicking on the tags.
  • Auto-Explainer: This allows you to generate summaries about the data, showing anomalies and top-performing combinations in the data.

For Big Data & Big Data Analytics

You’ve probably heard the term ‘Python’ a lot in your industry. That’s because it is one of the tools used in data analytics, responsible for data wrangling, machine learning, big data analytics and more. Python is a high-level programming language internally written in C, meaning you don’t have to deal with memory/RAM usage, where it’s stored and manipulating the bits and bytes, which can be time-consuming. Being a high-level programming language is why Python is the ideal programming language for analysing data. Since it is open-source, it has access to an insanely large amount of libraries. There are over 200,000 packages, and the popular ones include Matplotlib, Plotly, and Seaborn. Data analysts and scientists trust the likes of Python to help them perform high-level data analytics through tools that make it easy.

Do You Have The Tools To Join The Data World?

You may be considering taking on a career in data analytics or are looking to grow your already-flourishing data science career. Hopefully, this article has shed light on the tools used to get the job done; however, the job still needs more skilled people. So, invest in a data analysis short course and equip yourself with the right tools to thrive in the data science world.


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