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Beginner’s Guide to NumPy for Data Science

Data science is an evolutionary extension of statistics capable of coping with the huge volumes of data created on a regular basis today. NumPy (Numerical Python) is a Python linear algebra package. It expands the range of statistics by using methods from computer science. It is a critical library on which practically every data science or machine learning Python module, including SciPy (Scientific Python), Matplotlib (plotting library), Scikit-learn, and others, is built. Data scientists that need to deal with data for analysis, modelling, or forecasting should become acquainted with NumPy's capabilities and usage, as it will allow them to swiftly prototype and test their ideas. NumPy is a Python library that can conduct mathematical and logical operations on arrays. This is where the data science certification course stepped in where everything would be explained. What is a NumPy array? NumPy, an abbreviation for Numerical Python, is an efficient interface for storing and proc...
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Data Science is a dying profession vs Data science is just starting to bloom

So, in 2021, is data science still a promising job path? This is a well-known and famous question that requires a debatable answer. The heart of the industry is data science. The data scientist is dying, and there's little we can do to keep our fat paychecks, celebrity status, and bloated egos and maybe you have heard about it. Is data science a dying profession? Almost every company collects data that is increasing rather than decreasing. Many people believe that a "data science career is risky." However, we cannot make assumptions or plan our careers based on what is popular on the internet. Let's take a look at some of the reasons why data scientists are leaving or losing their employment. ●       This is the most significant challenge that a data scientist faces in the workplace. ●       The corporation did not provide the data scientists with what they expected. ●       Data science is an academically demanding field with a steep learnin...
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Fake News Detection

  Machines are producing an ever-increasing amount of data per second in our world, and there is concern that this data may be false (or fake). How will you be able to tell whether anything is fake? Fortunately, machine learning can help solve this issue. You will be able to tell the difference between real and fake news after practicing this advanced python project on detecting fake news. In Python, we can create a machine learning model that can determine whether or not news is bogus. Another difficulty that has been identified as a machine learning challenge disguised as a natural language processing problem is this one. Before you start working on this machine learning project, familiarize yourself with words like false news, tfidfvectorizer, and PassiveAggressive Classifier. Check out Learnbay's data science courses if you're a newbie who wants to learn more about data science. We'd also like to point out that Learnbay provides a series of data science projects where...
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Difference between ANN CNN & RNN in deep learning

    If you've been reading up on data science and AI, you've probably come across the term " deep learning ." Also you would have come across the term "neural network." Deep learning, also known as neural networks, has aided in the rapid evolution of AI and is pioneering the next stage of AI development. In conjunction with the data science resources sector, AI, machine learning, and deep learning have become an integral part of social media at its core. In this post, we'll take a closer look at the many components of Neural Networks and how they're influencing AI's rapid advancement. Convolutional neural networks (CNN), recurrent neural networks (RNN), artificial neural networks (ANN), and other forms of deep learning neural networks are transforming the way we interact with the world. It's a commonly used machine learning algorithm among data scientists and machine learning professionals, and it's now a standard part of any learning p...
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Data Visualization Vs Data Science

  Why Is Data Visualization Important In Data Science? Data Science refers to the process or art of interpreting data and creating useful information, whereas Data Visualization refers to representation of data. Although both of them are different but are interlinked with each other, as we can say that data visualization is a subset or part of data science. Let’s elaborate on the difference.   Basis Of Difference Data Science Data Visualization 1.Meaning Data Science is the study of data and converting it into useful information. It is the process of translating large data sets into charts, maps, graphs and other visuals. 2.Data Size It works on any size of data. It works on a massive amount of data. 3.Goal Main Goal of data science is to gain knowledge from raw data and analyze it to extract useful information. The main purpose of data visualization is to visualize data by representing it into pictorial form. 4. Professionals who perform it? Data Scientist, Data Analysts...
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