Articles, blogs, whitepapers, webinars, and other resources to Learn In-demand Data Science Skills

A place to improve knowledge and learn new and In-demand Data Science skills for career launch, promotion, higher pay scale, and career switch.

Tom Robertson

Tom Robertson
Tom Robertson
Data Science Enthusiast

Tom Robertson is a Python-based data scientist with domain knowledge in behavioral neuroscience research and non-profit development. He is currently employeed as Data Science Instructor at QuickStart Technologies. Tom is inspired by the belief that data science can improve quality of life by providing our researchers, doctors and policy-makers with new, data-driven insights.

Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.

  1. Why and Data Science Certification Can Help Improve Your KPIs

    Why and Data Science Certification Can Help Improve Your KPIs

    Categories: Data Science
    In this era, data science experts or managers, whether technical roots or managerial, all strive to give their team visibility and align teamwork with business value. It’s hard to lead a team and communicate with a company. It is usually too late to find out that a project has come to the wrong conclusion or that a company has already decided because the project has not been completed quickly enough. However, the amount of data that companies process today is quite large. Though, only about 0.5% of the respective data is analyzed.
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  2. How Data Upskilling Is Going to Help?

    How Data Upskilling Is Going to Help?

    Categories: Data Science , Data analysis and visualization
    As data and analytics progress, companies can use the data to identify the skills and methods needed to train and hire employees and provide them with new opportunities for the future. Skills include the development and diversification of skills and knowledge needed for further success and employment. And as a data expert, it’s important to be competitive and stand out - and help companies grow in the future that could continue to use the new data architecture and external infrastructure.
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  3. How to Create a Healthy Data Culture Within An Organization

    How to Create a Healthy Data Culture Within An Organization

    Categories: Data Science , Data analysis and visualization
    In this era, for companies - the ability to make informed decisions is more crucial and critical than ever. The data no longer relate to observations but encourage organizations to ask critical questions. On the other hand, it has the power to reshape companies, create new sources of revenue, and create a business model that protects the future. Today, the culture is data-driven communities is set by data-driven individuals who obtained Big data certification through all departments and levels. In these cultures, everyone has the right to critically use data, make decisions, and initiate conversations, instead of blocking them.
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  4. Why Python Is the Perfection Language for ML

    Why Python Is the Perfection Language for ML

    Categories: Python
    In this world of technology, it is considered that Machine Learning (ML) is one of the fastest-growing areas. It is an artificial intelligence app, where systems can automatically learn and improve their skills, understanding them without special programming. However, it more than analyzing data patterns, if people want to work in the respective field, they have to learn special programming languages and skills. Creating an intelligent algorithm requires an expert to process, practice, define, organize, and understand the data.
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  5. How Data Is Driving Big Organizations and What to Learn From It

    How Data Is Driving Big Organizations and What to Learn From It

    Categories: Data Science , Data analysis and visualization
    Well-managed and reliable data leads to reliable analyzes and reliable decisions. To stay competitive, companies need to take full advantage of big data and act on data - making big data decisions, not intuition. The benefits of using data-based are obvious. Data organizations operate better, more predictably, and more efficiently. It is collected quickly and in large quantities to identify trends and patterns that drive customers, industry, and life itself.
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  6. Machine Learning Cheat Sheet

    Machine Learning Cheat Sheet

    Categories: Data Science , Cheat Sheet
    Machine learning is the method of algorithms understanding processes without programming. As part of artificial intelligence (AI), machine learning accesses data and learns by itself. It’s a fascinating field of study that can even be used to predict future events based on past data. Predictive analytics, deep learning, algorithms, and supervised and unsupervised learning are all part of machine learning. Many advancements in AI are due to machine learning algorithms. This could range from recommendations you see on YouTube, Google and other major sites that track data, such as clicks, likes and frequented websites. In this way, the algorithm “learns” what you like and provides recommendations.
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  7. What Is Data Governance? A Best Practices Framework for Managing Data Assets

    What Is Data Governance? A Best Practices Framework for Managing Data Assets

    Categories: Data Science
    In this era, you can learn a lot from other people who have worked on different processes and models by researching best practices in data - governance. However, every single business is varying from another and one has to adopt the customs of data - governance customs to the process. Though, it is not required to wholly build the wheel. Use agile evolutionary thinking in data governance, start with the least efficiency, then repeat and expand from there. For most companies, data is often collected for action purposes.
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  8. What Is A Data Architect? It’s a Data Framework Visionary

    What Is a Data Architect? It's a Data Framework Visionary

    Categories: Data Science
    In this interconnected world, it is believed that data - architects are the ones who are known as experienced visionaries - that can interpret the demands of the industry into a practical condition. On the other hand, also outline data principles as well as standards for the industry. The data architect also provides a standard unified terminology, sets strategic requirements, describes a high-level integrated design to meet those requirements, and is consistent with company policies and related business architectures. All the same, the data - architect framework describes the processes used to organize, identify, activate, create, manage, distribute, monitor, and clean data.
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