• Nov 3, 2023

    The 9 Best Data Analytics Tools for Data Analysts in 2023

  • Thinking about starting a new career as a data analyst? Here’s all you need to know about data analytics tools that will lead the data science industry in 2023.

    Can you imagine a plumber without a toolbox? No, right? The same goes for data practitioners. Without their data analytics tools, data practitioners couldn’t analyze data, visualize data, extract value from data, and do the many cool things that data practitioners do in their daily work.

    Depending on the data science career you’re most interested in –whether data analyst, data scientist, data engineer, etc–, your data science toolbox will be slightly different.

    In this article, you will understand the role of the business analyst, the essential technical and soft skills to succeed as a business analyst, and how you can best showcase your skills through your resume and portfolio.

    • Collecting, processing, and performing statistical data analyses to spot important patterns.
    • Transforming and manipulating large data sets into usable forms, such as reports or presentations.
    • Communicating data insights with data visualizations and data storytelling techniques.

    Now that you know what a data analyst does, let’s get into their toolbox!

  • Criteria for Selecting a Data Analytics Tool

    Data science is a rapidly evolving industry. New software and tools, including data analytics tools, are launching daily. With such a vibrant ecosystem, choosing the right tools can be challenging, especially if you are a newcomer in data science.

    Like any toolbox, there is no tool that is best in absolute terms to address all the problems you will face as a data analyst. Instead, the key is to have a complete and balanced set of tools that will help you succeed in every scenario.

    Every case is different, and you or the company you work for will have to consider several factors to find the best fit for your needs. Below, you can find a list of factors you should assess before choosing your data analytics tools:

  • Identifying business needs

    This is probably the most important factor. While it’s important to ask yourself what tools you want to learn and what tools you like the most, you should never forget to ask what your company –or the company you would love to work in– needs. This makes sense: if the rest of the team uses Python, learning R first may not be the smartest decision.

    At the same time, you should always keep an eye on recent trends and cutting-edge data technologies so you can learn them and increase your value in the team.

  • The Business Analyst vs Business Analytics Professional

    We can break the responsibilities of a business analyst into four core components:

    • Decode: Researching, finding, and implementing an efficient and often innovative solution to various business problems across an organization is the bread-and-butter of the business analyst's role.
    • Optimize: From creating and implementing business processes that improve efficiency and productivity across multiple business units to analyzing and troubleshooting analytical systems, the role of the business analyst is always heavily focused on optimization.
    • Document: Business analysts
  • Scalability and flexibility

    The big data landscape has changed dramatically in the last few years. Today, data-driven companies deal with massive amounts of data, with varying formats, that are often processed in near real-time.

    Against this backdrop, scalability and flexibility are crucial for success. The data science technology landscape is rapidly growing, with many tools and applications specifically designed to address the challenges associated with increasing data volume, velocity, and variability. Hence, when choosing your tools, always keep in mind how much flexibility and scalability you or your team need.


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