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According to Google, “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”
It is the most crucial step in a life cycle of a data scientist where one has to build various models using machine learning algorithms and should be able to predict and come with the most optimum solution to solve any problem.
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For becoming a Data Scientist, having knowledge of statistics and probability is as essential as having salt in food. Knowing them will help the data scientists interpret large data sets, get insights from them, and analyze them better.
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Structured Query Language (SQL) is used for extracting and communicating with large databases. One should focus on understanding the different types of normalization, writing nested queries, using co-related questions, group-by, performing join operations, etc., on the data and extract in raw for...
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When a Data Scientist is given a project, the majority of the time goes into cleaning the data set, removing unwanted values, handling missing values. It can be achieved by using some inbuilt python libraries like Pandas and Numpy.
One should also know how to manipulate data using Microsoft...
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Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work.
To guide your journey, the skills outlined here are the first you must acquire to ...
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Python is the most preferred coding language and is adopted by most Data Scientists. It is easy to understand, versatile, and supports various in-built libraries such as Numpy, Pandas, MatplotLib, Seaborn, Scipy, and many more.
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Exploratory data analysis is the essential part when talking about data science. The data scientist has many tasks, including finding data patterns, analyzing data, finding the appropriate trends in the data and obtaining valuable insights, etc., from them with the help of various graphical and s...
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After acquiring the basics of Data Science, now it’s time to get hands-on experience in its part. There are many online platforms, like Kaggle and Analytics Vidhya, that can provide you with hands-on experience with both beginner and advanced level data sets. They can help you to understand vario...
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“Talent wins games, but teamwork and intelligence win championships.”, Michael Jordan
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Some of the most effective machine learning algorithms beyond deep learning include:
Companies are leveraging data and artificial intelligence to create scalable solutions — but they’re also scaling their reputational, regulatory, and legal risks. For instance, Los Angeles...
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