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Data Science Fundamentals For Python And MongoDB


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In the world of data space, the era of Big Data emerged when organizations are dealing with petabytes and exabytes of data. It became very tough for industries for the storage of data until 2010. Now when the popular frameworks like Hadoop and others solved the problem of storage, the focus is on processing the data. And here Data Science plays a big role. Nowadays the growth of data science has been increased in various ways and so on should be ready for the future by learning what data science is and how can we add value to it.


One of the reasons for the acceleration of data science in recent years is the enormous volume of data currently available and being generated. Not only are huge amounts of data being collected about many aspects of the world and our lives, but we concurrently have the rise of inexpensive computing. This has formed the perfect storm in which we have rich data and the tools to analyze it. Advancing computer memory capacities, more enhanced software, more competent processors, and now, more numerous data scientists with the skills to put this to use and solve questions using the data!


Speaking of demand, there is an immense need for individuals with data science skills. According to LinkedIn U.S. Emerging Jobs Report, 2020 Data Scientist ranked #3 with 37% annual growth. This field has topped the Emerging Jobs list for three years running.


One famous example of data science in action is from 2009, in which some researchers at Google analyzed 50 million commonly searched words over a five year period and compared them against CDC(Centers for Disease Control and Prevention) data on flu outbreaks. Their aim was to understand if some particular searches harmonized with outbreaks of the flu.


One of the advantages of data science and working with big data is that it can distinguish correlations; in this case, they distinguished 45 words that had a strong correlation with the CDC flu outbreak data. And using this data, they were able to predict flu outbreaks based only on usual Google searches! Without this mass amount of data, these 45 words could not have been predicted beforehand.


The following are some cool data science projects. In each project, the author had a question, and they wanted to solve the question. And they utilized data to solve that question. They analyzed and visualized the data. Then, they wrote blog posts to communicate their results. Have a look to know more about the topics and to see how others work through the data science project and deliver their results!


This section gives you various examples to help you understand Data Science. It explains how you decide on a place for the vacation, how the weather is predicted, and sales during a particular time in a year using data science.


This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data.


DataCamp has definitely been the key starting point for me in terms of becoming a self-taught data analytics professional because it enables me to work on projects I enjoy and write about all things data science. That said, it has its drawbacks.


Lessons on general programming context and syntax are followed intuitively in the curriculum by the introduction of data analysis and science-specific packages, such as Pandas in Python for data cleaning and manipulation or ggplot in R for data visualization.


While the course assumes you have Node and JavaScript skills, it leaves no stone unturned with a detailed set of classes to cover MongoDB fundamentals, including essential CRUD operations, aggregation, indexes, data modeling, and more.


The Data Science and Analytics Boot Camp equips you with the skills required for a high-growth career in data science and analytics. Over the course of 24-weeks, you'll master the fundamentals of statistics, Python programming, datab