Data Science Fundamentals For Python And MongoDB ^NEW^
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.
Data Science Fundamentals for Python and MongoDB
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.
In order to build a successful career in the field of data science, one must learn MongoDB as well thoroughly to create, manipulate, delete and update data and perform different operations. Prefer to take a comprehensive bootcamp program that fits all your needs to level up your career? Well, you are in the right place as Simplilearn offers the best in the industry courses to take a leap in your data science career through its PG in Data Science. Do check it out!
The growth of data science requires a deeper set of skills and capabilities from data science practitioners. While the fundamentals of data science have been around for decades, only recently have the tools and techniques matured to provide the capabilities necessary to accomplish more advanced data analytics, AI and machine learning (ML) goals.
The cornerstones of deriving insights from data are the mathematical areas of statistics and probability. Advanced levels of statistics are the mainstay of data science and are applied throughout the profession with data visualization, data modeling, identification of correlations, regression, feature transformation, data imputation and dimensionality reduction, among others.
Linear algebra and multivariable calculus are widely applied by organizations in data science to manipulate and transform data and derive insights. Linear algebra is applied in areas such as data processing and transformation, dimensionality reduction and model evaluation. Core linear algebra topics data scientists need to be familiar with include vectors, norms, matrices, matrix transpositions and manipulations, dot products, eigenvalues and eigenvectors.
While being able to derive insights from data is the core of data science, the ability to present that information in ways that can provide value to an organization is equally important. Likewise, analysis of data requires access to sufficient volumes of structured data on which to base those insights. As a result, data scientists also need to have key skills around data visualization, data manipulation, data preparation and data wrangling.
Taking the numerical or classification insights from data and presenting them in a way that can be understood by decision-makers is a vital skill for data scientists. Data visualization, which embodies the concept of creating charts, graphs, diagrams and other illustrations of data, is helpful for people who are better with visual information than numerical or quantified data. In many ways, data visualization is a creative aspect of data science, and appeals to those with design-thinking or UX priorities. The most important outcome of data visualization is successfully building a story from the data using visualizations that people can easily understand.
Much of the technology around data science has evolved over the past few decades. Open source as well as commercial offerings provide a plethora of tools, libraries, frameworks and support functionality across the full lifecycle of the data scientist's job responsibilities.
Data scientists need proficiency in a range of languages including Python, R, Julia and Java-based languages. Python in particular has been the star of the data science world. In 2018, 66% of data scientists reported using Python every day, overtaking R as the most popular language for data science. Julia and other languages are helpful for high-speed and big data processing, and even the use of commercial offerings from SAS and MATLAB are helpful to accomplish a range of data science and analytics tasks, particularly in enterprise settings where the ability to scale up projects is important.
Increasingly, much of the work of data science and ML engineering is done in the cloud. Data scientists should have some experience with cloud-based MLaaS environments from Amazon, Microsoft, Google and IBM, among others, with specific expertise in whichever of those environments are utilized by their organization. Many of these platforms have a wide range of tools, pretrained models and additional support for the full lifecycle of model development and data science activities.
No doubt, just reading this list of skills can be overwhelming. The experienced data scientist who possesses all these skills is in extremely high demand, as it's hard to find people who not only have all these skills, but also the experience and knowledge in how to apply them effectively. So, keep all these skills in mind when you're building up your experience but approach data science as a team sport.
The organization needs to make sure that each of these areas of knowledge and expertise are covered. If you can be that one "unicorn" with all these skills and capabilities, you'll find yourself with tremendous job prospects. If you are a member of the hiring organization, be aware that competition will be intense and requested salaries will be high. But if you approach the need for data science skills as a group effort, you'll find greater opportunities to not only meet your team's immediate needs, but also grow your team's capabilities over time. 041b061a72