Master the top 10 data analytics skills and platforms of 2023 for business success

Intro

Data analytics has become an essential aspect of business decision-making in recent years. With the increasing availability of data, companies require professionals who can extract meaningful insights from this data to drive business success. However, with the field of data analytics evolving at a rapid pace, professionals must stay up-to-date with the latest skills and platforms. In this article, we’ll cover the top 10 data analytics skills and platforms that professionals in the field should master in 2023. From data visualization to machine learning and cloud computing, we’ll explore the essential skills and platforms that can help data analysts provide valuable insights to their organizations. So, let’s dive in and explore the top data analytics skills and platforms for 2023.

Data Visualization

One of the essential skills for data analytics is data visualization. Visualization allows data analysts to effectively communicate their findings to decision-makers. Tools like Tableau, Power BI, and QlikView are popular platforms for data visualization. These tools can help businesses create interactive dashboards, charts, and graphs to communicate insights and trends effectively.

Machine Learning

Machine learning is another crucial skill for data analytics. It involves training algorithms to learn from data and make predictions or decisions based on that learning. Python, R, and TensorFlow are popular platforms for machine learning. TensorFlow is preferred for deep learning applications, while Scikit-learn is used for traditional machine learning. Understanding how to apply machine learning algorithms is an important skill for data analysts

Data Cleaning

Data cleaning involves the process of identifying and fixing inaccuracies, inconsistencies, and errors in datasets. This skill is important because dirty data can negatively impact the accuracy of insights derived from data analysis. Platforms like Trifacta, OpenRefine, and Talend are popular for data cleaning.

Data Warehousing

Data warehousing is the process of storing and managing data from various sources in a single location. It’s important to have an efficient data warehousing system in place to ensure quick and easy access to data. Platforms like Snowflake, Amazon Redshift, and Google BigQuery are popular for data warehousing.

Data Mining

Data mining involves the process of extracting patterns and insights from large datasets. This skill is important because it can help businesses make informed decisions based on trends and patterns in their data. Platforms like RapidMiner, KNIME, and SAS are popular for data mining.

Data Governance

Data governance involves the process of managing the availability, usability, integrity, and security of data used in an organization. This skill is important because it helps ensure data is used ethically and effectively. Platforms like Collibra, Informatica, and Alation are popular for data governance.

Data Science

Data science is a multidisciplinary field that involves using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Platforms like Anaconda, Jupyter, and Databricks are popular for data science.

Cloud Computing

Cloud computing involves the delivery of computing services over the Internet. It’s important for data analytics because it allows easy access to large datasets and powerful computing resources. Platforms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform are popular for cloud computing. Amazon Web Services is known for its scalability and flexibility, while Microsoft Azure is preferred for its integration with other Microsoft tools.

Business Intelligence

Business intelligence involves the process of analyzing data to help businesses make informed decisions. This skill is important because it helps businesses understand their data and use it to drive success. Platforms like MicroStrategy, Oracle BI, and IBM Cognos are popular for business intelligence.

Data Analytics Strategy

Finally, it’s important to have a solid data analytics strategy in place. This involves identifying the business goals and objectives, selecting appropriate data analytics tools and platforms, and creating a roadmap for implementation. The ability to develop and execute a data analytics strategy is crucial for success in data analytics.