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December 10, 2023
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6
 min read

2024's Top 10 Data Analytics Tools for Beginners: A Comprehensive Overview

2024's Top 10 Data Analytics Tools for Beginners: A Comprehensive Overview
Fig.1: A data visualization consultancy remains on top of market developments and trends. (Photo by Kindel Media on Pexels)

While data is essential for businesses, it's not very useful in its raw state. Every year, we create a massive amount of data, but making sense of it requires skilled professionals in data analytics. Companies that don't use data to make decisions are falling behind in today's competitive world. To make data useful, experts called data analysts use special tools to sort through it, find the important bits, and make plans based on that. There are lots of tools these experts can use, so it's important to know which ones might work best for your needs. Here are the 2024 tools for data analytics we'll talk about in detail. Let’s dive in without wasting any time! 

Contents

  • Data Analysis Tools In a Nutshell
  • Python
  • Tableau
  • R
  • Jupyter Notebook
  • SQL
  • PowerBI
  • PyCharm
  • Apache Spark
  • Looker Studio

Let’s dive in! 

Fig.2: A macbook with the coding screen on it
Fig.2: A macbook with the coding screen on it

Data Analysis Tools In a Nutshell 

Data analysis involves arranging data logically, clarifying it, making it understandable, and forming conclusions. Its goal is to extract valuable insights from data to facilitate informed decision-making. A primary objective of statistical data analysis is recognizing patterns. For instance, in retail, uncovering patterns in unstructured or semi-structured data can aid in making more effective decisions to enhance customer experiences. Data analytics tools encompass software used by analysts to conduct and execute analytical tasks, assisting businesses in making smarter decisions while reducing costs and boosting profits. These tools enable companies to scrutinize large data volumes, providing them with a competitive advantage. They support predictive analytics, business intelligence, and handling both structured and unstructured data. Additionally, as big data analytics tools evolve, they are increasingly incorporating machine learning and artificial intelligence. Now, let's take a look at the most useful data analytics tools together. 

  1. Python 

Python holds the top spot in popularity rankings across various programming languages and is considered essential for data analysts. It's an adaptable, open-source programming language widely used not only in data science but also in fields such as web development and video game creation. 

Python's versatility is evident in its ability to handle numerous data science tasks, thanks to its extensive collection of libraries. Supported by a vast user community, Python empowers a wide array of operations, from data preparation, visualization, and statistical analysis to deploying machine learning and deep learning models. Its straightforward and clear syntax makes it a favored choice for beginners entering the programming world.

Fig.3: "Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway." (Geoffrey Moore, Author)
Fig.3: "Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway." (Geoffrey Moore, Author)
  1. Tableau 

Tableau stands out as an excellent data analytics tool that allows you to create interactive dashboards and visualizations without needing extensive programming skills. It's highly regarded in the field thanks to its vast Tableau Community, which boasts over a million members, over 500 global user groups, and vibrant Community Forums. 

Tableau is favored by data scientists and analysts for its capacity to handle live datasets, allowing more focus on data analysis rather than data mining. Setting it apart from other data analysis tools, this business intelligence tool excels in managing extensive numerical data. Its user-friendly drag-and-drop interface enables data analysts to effortlessly adjust computations and explore diverse scenarios.

  1. R

R is a free programming language made specifically for data science and high-level computing. It's widely used in finance and academia, ideal for tasks like organizing, working with data, making visuals, and doing statistics or machine learning. Lots of people use R, and it has many special libraries for things like data analysis and machine learning. The Tidyverse is a famous group of these libraries in R. It includes dplyr for managing data and ggplot2 for making impressive data visualizations.

Fig.4: "The world is now awash in data, and we can see consumers in a lot clearer ways." (Max Levchin)
Fig.4: "The world is now awash in data, and we can see consumers in a lot clearer ways." (Max Levchin)
  1. Jupyter Notebook

Jupyter Notebook is a free web tool for making interactive documents. These documents include live code, equations, visuals, and text. Think of it as a more interactive version of a Microsoft Word document, especially made for data analysis! It's excellent for presenting work: Jupyter Notebook works in your web browser and works with more than 40 languages, including Python and R. It also connects with big data tools like Apache Spark and supports different outputs like HTML, images, videos, and more.

  1. SQL

A significant portion of the world's data is stored in databases. SQL (Structured Query Language) is a specialized language that allows programmers to interact with, modify, and retrieve data from these databases. Acquiring a strong grasp of databases and SQL is crucial if you aspire to become a data analyst.

Mastering SQL enables you to operate with various relational databases, including widely used systems like SQLite, MySQL, and PostgreSQL. Regardless of whether you opt for Python or R to begin your journey as a data analyst, considering learning SQL is highly recommended. Thanks to its straightforward, declarative syntax, SQL is relatively easy to learn compared to other languages. It proves immensely beneficial as you progress in this field.

Fig.5: "Data are becoming the new raw material of business." (Craig Mundie)
Fig.5: "Data are becoming the new raw material of business." (Craig Mundie)
  1. PowerBI 

Power BI is a widely recognized cloud-based tool for data analysis, boasting a community of over 13,000 members. Renowned for its swift insight generation, data analysis, and visualization capabilities, Power BI remains popular, continually introducing innovative features to its users. Recent updates include enhancements to the format pane, Canvas Zoom, and Field parameters, among others. This tool empowers users to effortlessly construct interactive visual reports and dashboards. Its robust data integration is a major highlight, seamlessly functioning with various cloud sources such as Google and Facebook analytics, text files, SQL servers, and Excel.

  1. Microsoft Excel 

Microsoft Excel is another data analysis tool with a very handy feature called pivot tables. Despite being around for over 30 years, Excel remains incredibly useful. It hasn't changed too much from its original form, just some improvements and added features. Excel helps in analyzing revenue trends, business operations, and marketing patterns. Many industries still value strong Excel skills because it makes data more accessible and manageable. In Excel, data is stored in cells, making it easy to organize and understand. It simplifies complex data, allowing modifications and computations without extra tools, even for big datasets.

  1. PyCharm

PyCharm, made by JetBrains, is an environment for developers who want to write better Python code. It's highly rated and offers helpful features like a debugger, test runner, and tools to work with databases. It's known for its smart code assistance, which helps find mistakes and gives suggestions. PyCharm supports different versions of Python and comes in three editions: a free Community version, a paid Professional version with more features, and a free Edu version for educational use. Overall, it's an essential tool for Python data analysts.

  1. Apache Spark 

Apache Spark is a top tool in big data and data engineering. It's fast, easy to use, and works well for analyzing large, unorganized datasets. Compared to similar tools like Apache Hadoop, it's about 100 times faster because it uses RAM instead of local memory. This makes it great for building machine learning models that handle lots of data. Spark has helpful features like the Spark shell for interactive analysis using Python or Scala, Spark SQL for exploring data with SQL, and MLLib for machine learning algorithms like clustering and regression.

  1. Looker Studio 

Looker Studio is a no-cost tool for creating dashboards and visualizing data, effortlessly connecting with various Google apps like Google Analytics, Google Ads, and Google BigQuery. This integration is especially beneficial for analyzing Google-related data. For instance, marketers can craft dashboards using Looker Studio to delve deeper into customer conversion and retention metrics from Google Ads and Analytics. It's worth noting that Looker Studio can handle data from different sources, as long as it's initially replicated to BigQuery through a data pipeline like Stitch. One of the crucial tool you should know if you are a beginner about data science. 

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