The evolution of the internet has brought the world closer, and a massive volume of data is being generated every second across the globe. In today’s digital era, most industries have a huge demand for data analysts to analyze their huge volume of data that’ll help them in business development and revenue generation. They are also looking out for solutions that can help them administer data on digital platforms.
Data science gives an intuitive understanding regarding structured and unstructured data devising and data-driven solutions using algorithms, scientific methods, etc. On the other hand, Big Data is a warehouse for all kinds of data. It helps in optimizing performance, business processes and improves the overall research and development.
Since big data and data science fall under the category of data analytics field, it’s a Herculean task for students to choose a career in either field. This article will help you know- data science or big data, which is better and help in selecting a career among the two.
What is the difference between DataScience and Big Data?
In simple words, data science is the process of using data to find solutions for a problem statement. Its main objective is to extract meaningful insights from data using mathematical skills, scientific methods, algorithms, structures, etc.
Whereas big data is a domain that extracts information meticulously from large data sets that are too complex to be dealt with by traditional data processing software.
For aspiring data scientists, these are some of the following skills that need to be mastered:
- Probability and Statistical methods
- Visualization of data
- Programming languages
- Modelling and analytics
- Intellectual curiosity.
For those who want to make a career in the Big Data domain, these skills need to be mastered mainly:
- Data Visualisation
- Analytical Skills
- Fundamentals of Data Structures, Algorithms and Object-Oriented Languages
- Skills of data mining
- Structured Query Language
Which is better? Data science or Big data?
You must understand that data science and big data are specific fields, with the major parameter being the scope. Both these fields use data extensively. However, their objectives differ. Data science mainly focuses on asking queries than finding solutions. At the same time, the prime objective of big data is to glean insights such as customer preferences, hidden patterns and is also used to prevent fraudulent activities.
Various industries like e-commerce, education, healthcare, etc., use data science. Hence there are ample opportunities for data scientists. The major fields where big data analytics is extensively used are banking, manufacturing, government, transportation, etc.
Since both these domains provide equal career opportunities, you need not worry about the scope. Yet, if you are asked to choose one among these two, data science is always a big draw as it is a multidisciplinary field. It offers more flexibility and tools to collect the data and produce something useful.
What is SAS in Data Science?
SAS is an abbreviation for Statistical Analytical System in Data science. It is a tool used to aspiring data scientists. Generally, professionals and large-scale organizations use this tool due to its high functionality and reliability. It performs complex data modelling.
However, these people should avoid using it in the short run:
- Independent data science enthusiasts
- It is catered to meet industrial demands.
- Exorbitant pricing.
How is Python used in Data Science?
Data scientists usually prefer Python in data science applications because it is highly functional in dealing with math, statistics, and scientific function. It is a high-level, open-source programming language that provides an approach for object-oriented programming.
A person from a non-engineering background finds it easier to adapt to this language because of its simple syntax and ease of use.
Usage of Python in each stage of data science:
- Understanding the types of forms that data can take- Parallel processing is done by using the libraries of Python, such as NumPy and Pandas.
- Collection of necessary data– Scraping of data from the web using BeautifulSoup and Python Scrapy.
- Graphical representation and visualization of data– Using graphs, histograms, pie charts, etc.
- Machine learning- Using various mathematical tools like Matrix, Calculus, and so on.
- Most large-scale organizations prefer SAS as their data analytics tool.
- Startups prefer Python due to its ease of use and functionality.
In terms of speed, SAS edges out; in terms of output, Python edges SAS out. So these are some of the tools used in the field of Data Science. Let us now have a look at the tools that are used in Big Data.
Tools used for Big Data:
- Hadoop– For faster data processing and flexibility
- Cassandra– For effective management of data
- MongoDB– Document-oriented database for storing large volumes of data.
- Storm– For real-time data processing and has massive scalability.
- OpenRefine-For cleaning the data, and the user can extend his data to numerous web services.
What are the benefits of Big Data?
- Generates new products and services
- Cuts your costs
- Not cumbersome
- Increased efficiency
How to present a Data Science/Big Data Project?
Building a fancy project alone isn’t sufficient to impress your interviewers. It is not about the outcome, but sometimes it is the mechanism of finding a highly valued solution. If your ideas must reach a larger audience, you must remember that effective presentation skills are vital.
Consider these factors to make your data science presentation a memorable one:
- Effective slides/graphics reinforce your presentation.
- Try to explain the technical aspects of your project as it is the ultimate goal of any research.
- Confidence is one of the important features of a good presentation. Try to present your project with a smile on your face and make sure to be audible.
- Try to simplify your way of explaining your high-level project as most of them would refrain from deep- technicalities. But make sure to focus your audience on your solution for the problem you are addressing.
How is linear algebra used in Data Science?
One of the branches of fortune in data science and machine learning is Linear Algebra. It is used in model evaluation, data pre-processing, data transformation, etc. You need to have sound skills in the following topics:
- Vectors and Matrices (all possible operations)
- Dot Product (To quantify the multicollinearity)
- Eigenvalues and Eigenvectors.
- Unitary transformation for data visualization.
How to Build a Data Science/Big Data portfolio?
In the earlier days, a candidate’s eligibility for a job would be assessed by the employers by looking at their CV. Gone are those days now. Having relevant skills in your domain alone will not guarantee your dream job. If you want to showcase your accomplishments to impress your recruiter, having a portfolio is of utmost importance.
The following tips might help you build an impressive portfolio:
- LinkedIn Profile: It is regarded as an extended resume that enables you to connect people worldwide and network with various organizations and professionals.
- An active GitHub profile: Creating a profile alone won’t be sufficient. The daily contributions are tracked daily, and you need to work on them regularly.
- Participate in hackathons.
- You need to update yourself about the recent developments in the field. This can be done by reading blogs.
- Build a working prototype.
Which country has more Data science jobs?
If you want to make a massive fortune in data science, it is essential to know the global opportunities for data scientists. Here is a list of a few countries that provide lucrative offers for a data professional.
- United Kingdom
Which Country has better opportunities for Big Data Jobs?
- New Zealand
You must remember that data is a common entity in Big data and Data Science. Big data concentrates on providing analytics focused mainly on customer satisfaction, whereas Data Science simplifies the raw data for the general public to convey the basic concepts. As the world is going digital, an enormous amount of data is being generated that is voluminous and needs to be dealt with carefully. You must have an iron will to make the best out of these booming technologies.
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