Top Data Analytics Tools for 2023: How to Become a Successful Data Analyst?

The world has gone digital, many transactions are happening online, and tons of user data are generated daily. Organisations collect these data and observe users' trends to make business decisions. They deploy mathematical and analytical methods to fast interpret data and make data-driven decisions.

What Is Data Analytics?

The main objective of data analysis is to examine, purify, transform, and present data in a more readable format. The goal is to find relevant information, make recommendations, and assist decision-making. It can be summed up as a series of operations on a data set to draw conclusions.

Businesses today have access to a pool of data, and with the correct tools, they can grasp different facets of their industry, market, clients, and other insights. They can make the best judgements at every level to keep risks to a minimum and revenues to a maximum with the help of these insights.

What Are Some Applications of Data Analytics?

Data analytics is the process of extracting insights and information from data. It is a crucial aspect of modern businesses as it enables organizations to make informed decisions based on evidence and data. Here are some of the top applications of data analytics in various industries:

• Logistics: Logistics companies use data analytics to optimize their supply chain processes. By analyzing data such as delivery times, transportation costs, and inventory levels, companies can improve the efficiency of their operations and reduce costs.
• Web Search: Web search engines like Google use data analytics to provide relevant search results to users. They collect data on users’ search history, location, and preferences to personalize the search results and improve the user experience.
• Manufacturing: Manufacturing companies use data analytics to monitor and optimize production processes. By analyzing data such as machine performance, energy consumption, and product quality, they can identify areas for improvement and increase efficiency.
• Education: Data analytics is increasingly used to improve student outcomes. Educators can identify strengths and weaknesses by analysing student performance data and tailor their teaching methods to meet individual needs.
• Healthcare: In healthcare, data analytics improves patient outcomes and reduces costs. By analyzing patient data, healthcare providers can identify patterns and trends, predict future outcomes, and make informed decisions about patient care.
• Insurance: Insurance companies use data analytics to assess and manage risk. By analyzing data such as policyholder demographic information and claims history, they can predict future claims and set insurance premiums accordingly.

Data analytics has a wide range of applications across different industries. By leveraging data, organizations can improve their processes, increase efficiency, and make informed decisions.

Learn the industry-standard software tools required for a data science professional. Enrolling in Skill Lync’s PG Program in Data Analytics and Data Science, you can acquire the in-demand skills and get a chance to work on industry-level projects.

Benefits of Enrolling in Skill-Lync’s PG Course

• You will gain practical expertise using some trending data analytics tools.
• You learn from leading experts who know complex mathematical implementations at an advanced stage.
• You work on 18 projects which can be posted on LinkedIn to prove your credibility.
• You intuitively learn the applications of SQL in data analytics

What Are Some Popular Data Analytics Tools in 2023?

1) Tableau: Tableau is a data visualisation tool used to analyse and present data in an interactive and easy-to-understand way. It enables users to create insightful dashboards and analytical reports quickly. Through drag-and-drop features, users can quickly create visualisations and share insights with colleagues. Tableau also provides an expansive library of resources, including data connectors, tutorials, and templates.

2) Microsoft Excel: It can quickly process large amounts of data, allowing users to visualise, analyse and manipulate it quickly. Excel is also very user-friendly, making creating and modifying data models, charts, and pivot tables easy. Excel has various statistical and financial functions, allowing users to perform complex calculations in seconds. It even boasts various infographic features that make it suitable for data analysis, such as the ability to filter, sort, and summarise data and create sophisticated charts and graphs. These features make it ideal for data analysts to analyse and visualise data sets quickly.

3) Python: It has several built-in libraries, such as NumPy and Pandas, which allow users to perform complex calculations and analyses quickly and easily. Python also has visualisation libraries, such as Matplotlib and Seaborn, which allow users to visualise data interactively. Python can connect to various databases, allowing easy access to large datasets. It also has a large and vibrant community of users, many of whom are data analysts, which makes it easier to find help when needed.

4) Microsoft Power BI: This powerful data visualisation tool can help data analysts quickly transform complex data into interactive visuals. Besides the visualisation tool, it offers many features.

• Query Engine: Power BI has a powerful section for answering queries. It can assist you in analysing various sources of data with ease.
• Automation: This software offers a range of automation features that can help simplify and speed up the data analysis process.
• Collaboration: Power BI’s collaboration features make it easy for data analysts to share and collaborate on data insights with colleagues.

5) SAS: This provides efficient data management and analytics capabilities to help professionals quickly analyse complex data sets and gain deeper insights. Additionally, SAS offers comprehensive data visualisation capabilities that allow analysts to quickly generate charts and graphs to help them understand the data better. 

Data analysts can also use SAS to apply sophisticated analytics techniques such as predictive analytics and machine learning to uncover hidden patterns and trends in data. Finally, SAS provides users with numerous business intelligence tools to help them make data-driven decisions.

Skill-Lync's data analytics courses can help you better understand the world of data analysis. The courses teach you how to interpret data, create effective visualisations, and what factors to consider while making data-driven decisions.

Additionally, you will gain insight into the tools and techniques used for data analysis, such as Excel, Tableau and Power BI. Talk to our experts to know more about our offerings.