Data Science

You hear about machine learning . But do you know what is true and what is not? People are fascinated about machine learning and artificial intelligence, yet they are confused. Multinational companies like Facebook, Google, and Amazon employed machine learning first. Google utilized it for ad placement, while Facebook used it to show post feeds. However, there are some misunderstandings about machine learning. Let's start with a few.   1.   Anyone Can Build A Machine Learning Platform That Can Be Used Anywhere Many believe you can just Google machine learning and develop any platform. However, machine learning is a specialized skill set. While learning machine learning, it is critical to comprehend the productive system. Hands-on experience with machine learning patterns and algorithms is required to master machine learning. This is a widespread misconception about machine learning. Nobody will spend Rs. 1,000 on a Rs. 200 job. Machine learning is only used with large am

Data cleansing has technically played an important part and vital role in the history of data science and data analytics, so also it continues to evolve at a rapid pace.   But what is data cleansing, and why is it so necessary? If you want to build a good culture around quality data decision-making and data cleaning, also known as data cleansing as well as data scrubbing, is one of the most crucial tasks for your organization to take. We'll look at the necessity of data cleansing in this post, as well as why individuals and corporations should use good data cleansing strategies. Definition: What is data cleaning? Cleansing data is a type of data management. Individuals and corporations amass a great deal of personal data over time! The process of ensuring that data is particularly correct and so usable is ideally known as data cleansing. Data cleansing is nothing but an act of going through all of the required data in a database. You can clean data by looking for faults or co

    Time Series is a series of data points ordered in time. In mathematics, time series is a sequence taken at successive equally spaced points in time. In simple words, it is a sequence of discrete time data. Time series tracks the movement of the chosen data points over a specified period of time with data points recorded at regular intervals. Definition: According to Mooris Hamburg “A time series is a set of statistical observations arranged in chronological order”. Uses of Time Series: It is used for prediction or to detect the changes in patterns in collected data. Here are few uses of time series mentioned below: ·        Used to predict future values ·        Evaluation of current achievements ·        Identify the changes in economics and business ·        Pattern recognition ·        Weather forecasting ·        Earthquake prediction ·        Signal Processing ·        Astronomy ·        Statistics and Mathematical Finance Benefits of Time Series

  Every day, we use data to draw conclusions. Concepts assist you in comprehending the world around you. They're not just only for data scientists , sp also many of the resources they link to aren't either as well. Data science concepts may be utilized in many areas of life, including finance, healthcare, and job choice, and it is a vital field for understanding how the world works. When we use a high-tech product or high-end technology, we often praise it without acknowledging the role of data science in making it possible.   You'll learn how the data science notion can help you live a better life in this post. Explore-exploit A framework for producing online and interactive learning that is simple to use. The exploration-exploitation trade-off is a fundamental problem when learning about the world by trying things out. It's intended to collect and use customer feedback in an interactive, online format in order to reduce regret. The problem is between choosing wh

Introduction Many data science or algorithm metrics are useful to know, such as MAE and RMSE, but there are other metrics that can be more meaningful to stakeholders and your organization as a whole. It is equally vital to comprehend, practice, and put into action, despite the fact that it is not as well-taught in academic contexts. I'm going to look at three instances in which using business analytics to interact with stakeholders can be advantageous in the future (especially the ones who are not in data science). Bucketed Unit Ranges   Always keep in mind that not all of these metrics will be applicable to all use cases, which is why I'm offering an example of one so you can determine whether it's the correct fit for your situation. Keeping this in mind, let's consider a case in which you're anticipating a continuous aim, such as a range of numbers ranging from one to hundred.   You can use MAE or RMSE, for example, to figure out how well your uniq

  There is a lot to learn in the field of data engineering. The demand for data scientists is continuously expanding, but there is a scarcity of people with data science abilities. As a Data Scientist, the most satisfactory solutions emerge from thinking from the ground up. Exploring, manipulating, analyzing, and developing algorithms to provide answers to questions or provide valuable recommendations is what Data Science is all about. Curiosity is a crucial talent in data science. Other abilities, such as mathematics and programming, can be learned through experience and various online courses. A Data Scientist's curiosity, growth mentality, and self-starter attitude are equally crucial as their technical skillset. Curiosity, on the other hand, should be the essential trait of a data scientist. The breadth and depth of the data science area, on the other hand, can be daunting at times. ·          Curiosity is about deducing meaning from facts and discovering answers to que