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Data Science

  1. Artificial Intelligence vs. Machine Learning vs. Data Science

    Artificial Intelligence vs. Machine Learning vs. Data Science

    AI, ML, and data science are all core disciplines in the IT world, which are involved in the manipulation of data at various levels to solve some real-world problems. While AI seems to be an all-encompassing discipline that cuts across both data science and ML, there is still much to uncover about this amazing technology. More, so, AI is hoped to go a long way in helping humans solve various analytical problems with the use of machines and computers.
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  2. Why Data Scientist is The Most Promising Job of 2019 and beyond

    Why Data Scientist is The Most Promising Job of 2019 and beyond

    With the advancements being made in the field of technology, the data science is progressing from datasets cleaning to using statistical techniques to incorporate data analysis, data mining, predictive analytics, machine learning, occupational intelligence, and many other things. Many people have this thought that the tech industry is making a hype about data science, and it will eventually go away. But unfortunately, this is not the truth. The reality is data science is just expanding and stretching from one field to another. The reason behind this growing popularity is its diversity as it is being used in almost every industry we can think of, such as logistics and transportation, retail and e-commerce, finance, real estate, health-care and off-course technology, and cyber industry.

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  3. 5 data scientists share how they got in the industry — and how you can, too

    5 data scientists share how they got in the industry — and how you can, too

    You have a bulk of data on the internet, and someone who interprets that data into meaningful content for running up a business is known as a data scientist. Good know-how of statistics is a life of data scientist. They are the one nerdy employee who spends day and night on collecting, merging, and cleaning data. And as we all are well aware that data is never clean, data scientists should mean to find a diamond from the garbage.

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  4. How to Become A Data Engineer?

    How to Become A Data Engineer?

    Nowadays, modern businesses heavily depend on Big Data to meet its goals and objectives; that's why everybody wants to be a Data Scientist. But very few of them ever think about becoming a Data Engineer, a hybrid between a data analyst and a data scientist.

    Attractive salary and high demand are negligible aspects of what makes this job to be most charming. Data Engineer's role needs essential technical skills, including in-depth knowledge of SQL databases, different programming languages and data science tools.

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  5. How to Become a Data Architect

    How to Become a Data Architect

    Data architects design and manage huge data infrastructures and databases to handle and organize Big data. A state-of-the-art data architecture smoothens the way for both the infrastructure and its features to align with business objectives. Data architects examine the facilities' existing data infrastructure and propose a plan to combine an existing system with a future model that possesses the resources within, optimized data structures, and the finest methodologies for handling big data. That's why, best data architects, just like their other data science counterparts, must have rich technical knowledge and skillset. 

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  6. How to Become a Data Analyst

    How to Become a Data Analyst

    Data analyst takes raw data, perform statistical analysis on it, and interpret it into essential knowledge or information. This technique helps the business to grow. A data analyst must have a pro grip on subjects of mathematics and statistics. Data analysis is used in taxes, finance, census, weather, and another crucial government department.

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  7. HOW TO BECOME A BUSINESS ANALYST

    HOW TO BECOME A BUSINESS ANALYST

    A business analyst is responsible for identifying a business’s needs and challenges and provide technological solutions to improve the involved processes and systems.

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  8. How to become a Big Data Engineer

    How to become a Big Data Engineer

    Big Data is referred to as a huge data set with massive volume.

    Big Data includes both data type, structured and non-structured. The main issue with Big Data is that huge data is difficult to process using software techniques and traditional databases. It comes from several different sources such as text, audio, images, and videos. Data is always available online, and it is gathered and created by digital technologies such as apps on phone, social media interaction or e-shopping, etc. All these data are mixed with other sources of data and tend to become Big Data.

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