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  1. IT degree vs Certification: Everything you need to know

    Should I Pursue an IT Certification or Degree?

    Categories: IT Helpdesk

    Are you considering a career in IT? It's essential to understand the differences between a degree in IT and a certification in IT. Your choice of education in IT can ultimately affect your career in IT and your compensation over time. While both options provide valuable skills and knowledge, there are pros and cons to each. In this blog, we'll explore the advantages and disadvantages of pursuing an IT degree versus an IT certification.

    IT Certifications: Pros and Cons

    IT certifications have become increasingly popular in recent years, as they provide a more focused and specific education compared to a degree. However, they do come with their own set of pros and cons.

    Pros of an IT Certification

    One of the most significant advantages of an IT certification is credibility. Having a certification can demonstrate to employers that you have the skills and knowledge needed to perform a specific job. This can make you a more attractive candidate for job opportunities, promotions, and salary increases. It can also give you a sense of confidence in your abilities, which can improve your job performance and overall satisfaction.

    Certifications can also be earned while working full-time, making them a great option for those looking to improve their skills while still maintaining their current job. Many certifications offer online courses, self-paced learning, and flexible exam schedules, allowing you to balance work and education more easily.

    Cons of an IT Certification

    One significant disadvantage of an IT certification is limited career opportunities. Without a degree, some higher-level IT positions may be out of reach. Many employers require a degree or years of experience to be considered for senior-level positions, as they want candidates with a broader understanding of IT.

    Another disadvantage of an IT certification is the lack of depth. Certifications often provide a shallow understanding of the subject

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  2. Four things your team might get wrong about data visualization

    4 Things Your Team Might Get Wrong about Data Visualization

    Categories: Data Science

    Analysts can lack clarity in their analysis processes, overly focus on design over function, forget to consider context, and over-rely on automation when implementing data visualization.

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  3. Addressing the top four myths in modern automation

    Addressing, and Solving, 4 Myths in Modern Automation

    Categories: AI & Machine Learning

    Discover the truth behind common misconceptions about automation and understand the future of automation in various industries.

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  4. How can I handle large volumes of data?

    5 Strategies to Better Manage Large, Complicated Data Sets

    Categories: Data Analytics

    Companies can collect data from website traffic, social media, customer feedback or other sources to handle large data sets that provide new insights into operations.

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  5. Strategies to improve data cleaning and transformation

    6 Strategies to Improve Data Cleaning & Transformation Efficiency

    Categories: Data Science

    Validate your information, define your project’s scope, document the strategy, implement automation, and create data quality metrics to improve your organization’s data cleaning and transformation process.

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  6. Government programs that make it easy to learn information technology

    Learn IT Fundamentals through These 3 Government Programs

    Categories: Apprenticeships

    Participate in government IT programs like the Cybersecurity Education Training Assistance Program or National Initiative for Cybersecurity Education to deepen your knowledge of fundamental IT strategies.

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  7. Should I hire or reskill employees for my company?

    Should I Reskill My Employees or Hire New Ones?

    Categories: Data Analytics

    Hiring new employees or retraining current employees will depend on your company’s individual situation, workforce size, market, and other characteristics.

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  8. Priorities to keep in mind when building a data warehouse

    4 Things to Keep in Mind When Creating a Data Warehouse

    Categories: Data Science

    Building a data warehouse is a critical responsibility for any company that wants to make informed decisions based on data. A data warehouse is a repository of integrated data from different sources within an organization. It allows companies to analyze large amounts of data and make data-driven decisions. For example, a data warehouse can inform a company about customer behavior, sales trends, and inventory management. With this information, a company can make more informed and efficient decisions, leading to fewer errors and better outcomes.

    To build a data warehouse, several things need to be kept in mind. A data warehouse needs to be designed to meet the unique needs of a company, taking into account its specific goals and objectives. It requires a thoughtful approach to ensure that it is effective and efficient in its purpose. Below, we will identify four priorities to keep in mind when building a data warehouse.

    How Do I Build a Data Warehouse?

    Building a data warehouse requires careful planning and execution. It is a complex process that involves many different factors. To ensure that your data warehouse is effective and efficient, there are several things that you need to keep in mind. Below, we will identify four priorities to keep in mind when building a data warehouse.

    Priority #1: Define the Purpose of Your Data Warehouse

    Before starting the construction of a data warehouse, it's crucial to define its purpose. Determine what data you will collect, why you will collect it, and how you will use it. Understanding your goals and objectives is critical in designing an effective data warehouse. With a clear purpose, you can ensure that your data warehouse is designed to meet your specific needs.

    Once you have defined your purpose, you can then decide what data sources you will use to populate your data warehouse. Data can come from various sources such as CRM systems, ERP systems, social media, website analytics,

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