What is Data Science? The Ultimate Beginner's Guide

What to expect?

1. What is Data Science?
2. How does Data Science work?
3. Data Science v/s Data Analysis
4. Application of Data Science
5. Difference between Data Science and Data Engineer
6. Should you pursue a career in Data Science?
7. How to pursue a career in Data Science?
8. Where to learn these Data Science skills?
9. Conclusion

What is Data Science?

Data science is a wide field. The primary focus of this is to gain as much understanding from data as possible, often through analytical and statistical methods to yield results that can be used in formulating new hypotheses. For a career in data science, you need the right data science skills, knowledge, and the right tools.

Other areas where data science jobs may contribute are coming up with questions to answer or proposing ways to improve databases, both of which require the excellent application of data science, analytical skills, and a sense of how to find connections or trends in the data. The role by which a person contributes in this career can be through data science engineering, learning from data science Bootcamp/data science courses, or other data science internships and jobs.

People who perform Data Science jobs are valued by their employers for their ability to use their broad expertise in domain-specific knowledge, computational tools, and statistical inferences to solve complex business problems, typically relying on big datasets that contain large volumes of raw and unstructured data that has not been easily deducible by manual approaches or queries generated using traditional software tools or data science skills.

How does Data Science work?

Machine learning is a type of artificial intelligence that helps computers learn how to do things without being explicitly programmed. It is used in every industry today and data science is a type of machine learning that uses data to solve problems. Data science skills use machine learning methods, algorithms, and models to find the best solution to a problem.

For instance, R-squared is an advanced statistic method used to measure how well a model fits the data. R-squared ranges from 0 to 1. The closer to 1, the better the model fits the data. The best way to understand R-squared is to think about it as a way to measure how well the model fits the data.

For effective analysis, Data Science skills include using matrix operations, Artificial Intelligence, linear algebra, and probability distribution functions such as Gaussian elimination, solving rank deficient systems of equations, eigenvalue problems and discrete Fourier transforms.

Data Science v/s Data Analysis

The popularity of Big Data has led to the development of new technologies, in particular, Data Science and Data Analytics. The former first appeared after the introduction of Big Data as a concept. In essence, these technologies modernize our ways of processing large amounts of data – the above-mentioned "Big Data" buzzword.

This leads to confusion between Data Scientists and Data Analysts since both works with Big Data. Even so, there are still differences between them both: firstly, each has its own purposes that they seek to achieve when working with data and secondly it is worth noting that there are some existing disputes as to which career path is stronger or more proficient than the other.

Data Science builds critical foundations and analyzes large data sets to generate early observations, trends for the future, and potential insights that could prove to be significant. Whereas data analytics is focused mostly on understanding data sets and extracting insights that can be turned into actions, data science is focused on creating, cleaning, and organizing data sets.

Data scientists help organize many of the steps leading up to data analytics, along with various analytics techniques used to meet business goals, and data analysts frequently depend on the work of data scientists for data analysis. In other words, data scientists work with data to discover novel questions and surprising insights, whereas data analysts seek answers to questions that an organization knows it has.

Regardless of the differences between data science and data analytics, many companies are looking to hire both data analysts and data scientists to fill key data professionals roles in the business.

Application of Data Science

Until now, you've come to understand that Data Science can be applied in various industries, from the non-profit sector to big business. At times, it may have felt like this high-paying job is more of a niche field but it's important for you to not only realize the application of data science engineering in different kinds of businesses but also put yourself out there and expose others to the value you could bring them with your skillsets.

E-Commerce

The application of data science in E-commerce websites like Amazon, Flipkart, and ebay.com is to help predict their customers' wants and preferences regarding their product offerings based on a customer's online behavior patterns.

Finance

While Data Science engineering saves the financial industry millions of dollars, it consistently comes down to being able to effectively comb through and utilize large amounts of data. Fintech companies like Stripe and Paypal are investing heavily in data science so they can create machine learning tools capable of quickly detecting and preventing fraudulent activities.

Health

One of the most current and relevant Data Science job examples in healthcare is how big data analytics supported the rapid development of COVID-19 vaccines. The healthcare industry has to grapple with the complexities of handling copious amounts of data, particularly in medical research.

Having a proper infrastructure in place that allows even more robust data collection means it's then possible to make better use of this information, which significantly improves its usefulness to patients globally!

Entertainment

The application of data science in Entertainment companies is very vital as these industries never had more access to data on their customers than they do now. This has made the entertainment industry thrive by providing unprecedented insights into their customers and completely revolutionizing entertainment for audiences.

Data Science allows companies in the media and entertainment industry to gain a better idea of the type of information they need and the insight they can use to learn more about themselves, which helps them make more informed decisions that save money and achieve results.

Logistics

Data Analysis helps companies increase supply chain efficiency by reducing freight costs, identifying new opportunities for optimization, and streamlining operations. Data science engineering of course has a big role to play in reducing the supply chain's carbon footprint and this includes optimizing routing, forecasting demand accurately, and reducing emissions.

Cybersecurity

The application of data science in Cybersecurity is a relatively new method of implementing data science to detect, prevent and reduce cybersecurity threats. The main purpose of this data science job is to keep digital devices, services, systems, and software safe from cyber-attacks by detecting security vulnerabilities, preventing their exploitation, and reducing business impact. Large amounts of data can be difficult to manage without the help of data scientists.

Difference between Data Science and Data Engineer

A Data Engineer develops, tests, and supports the data pipelines and architectures used by the data scientist to perform analytics while a Data Scientist uses the data collected by a data engineer to conduct analysis and computations that deliver valuable insights back to an organization, helping build and grow a business.

While the Data Engineer is involved with building infrastructure and architecture to generate the data, the Data Scientist is mostly involved with performing advanced mathematical and statistical analyses of the collected data. In short, Data Engineers set up and maintain a company's data infrastructure, setting it up to be analyzed by Data Analysts and Scientists.

Data science is responsible for developing models and procedures for mining data to gain actionable insights about the business whereas the end result of a data engineering process is to create databases, queries for database interaction, and convert data so it is sent in the correct format to the endpoint.

Should you pursue a career in Data Science?

The job of a Data Scientist has been ranked No.1 in terms of satisfaction and income for the three past years by Glassdoor, a career site for jobs. The main responsibility is to analyze large sets of quantitative data and understand the patterns to pursue a career in Data Science. It is also significantly popular among people who want to pursue a data science internship.

Data scientists should be able to create complex models to predict outcomes based on information gathered from raw numbers. They are also required to have knowledge of statistical tools while they apply them in their profession, which involves analyzing algorithms and codes created using statistical programming language and making predictions using scientific methods drawing data from these numbers.

People who have a degree in computer science and statistics benefit the most from data science internships and jobs. In addition, one must be proficient in machine learning techniques like Statistical Data Science, clustering, Classification, and Regression analysis. A data scientist may also be known as a machine learning architect. Based on Glassdoor's released data for 2018, these professionals generated an average salary of $128,800 based on the skills and job descriptions noted above.

How to pursue a career in Data Science?

The field of data science has many faces and can have its appeal in a host of ways depending on your individual interests and approach. Take some time to consider which avenue you would like to tackle first. Consider the roles of data scientists, analysts, and engineers as it relates to your ideas about both the scope and execution of your goals.

You will need at least a bachelor's degree in Data Science or related IT fields to get your foot in the door as an entry-level data scientist or land yourself a data science internship, though most data science careers will require a Masters's Degree as it helps students build specialized skillsets in machine learning, software development, data mining, applied statistics, risk analytics, analytics, and data management.

If you are looking to learn about data science jobs for the first time, it is recommended that you acquire valuable analytics skills that you can apply for a career in Data Science across a range of industries, or pursue an advanced degree.

While data analyst, data scientist, data engineer, and other data science jobs all broadly describe the various roles that data experts may perform in a business, there are all sorts of other job titles you will see either that are tied directly into those roles, or that involve using data science skills in some other way.

Data Analyst

Whether it is a data science internship or job, Data analysts must have proficiency with data storage, warehousing, and the use of tools like Tableau. Some consider data analysts to be data scientists, or they meld their duties with data engineers which is not the case.

For a career in data science, the role of a Data Analyst is to gather, process, and conduct statistical data analysis, with the aim of helping companies to make better business decisions. Data analysts' jobs majorly deal with analyzing historic data, creating and running A/B tests on products, or even designing systems.

Skills Requisites: Mathematical skills, programming skills (Python, R & SAS), experience with data wrangling and data visualization, plus an interest in statistics.

Data Scientist

Like many roles discussed, data scientists should possess a mix of analytical, machine-learning, data-mining, and statistical skills, as well as expertise with algorithms and coding even if it is a data science internship. Data scientists need to possess skills in areas including computer science, analytics, statistics, modeling, and mathematics.

Skills Requisites: Programming skills (SAS, R, Python), storytelling and data visualization chops, statistical and mathematical expertise with a few twists, holistic understanding of Hadoop, SQL, and Machine Learning.

Data Engineer

Data engineers are innately talented at extracting data from even the most expurgated and fragmented system -- for example, a database. They also have a knack for transforming data from one format to another so that it can be used for analysis by data scientists to achieve insights about the product.

Data Engineering jobs involve much less statistical analysis and much more skills in software engineering and programming. The main purpose of this data science job is to build bridges of communication between data scientists so that they are better able to understand the users' needs and to help them prepare effective solutions accordingly.

Skills Requisites: NoSQL databases (e.g., MongoDB, Cassandra), programming languages such as Java and Scala, and frameworks like Apache Hadoop.

Where to learn these Data Science skills?

You can learn these skills on your own with online Data Science courses or even YouTube videos as there is no dearth of learning materials on the Internet if you're working towards a career in this field, but self-learning through resources like youtube lacks structure.

These free resources might give you exposure to interesting content and that's a plus, but if you're dead set on learning data science skills from either a beginner or intermediate level, we recommend going through one of the countless Best Data Science courses available on platforms such as Udemy, Coursera, and LinkedIn Learning.

These platforms offer an added benefit: they provide in-depth coverage of this topic while allowing their students to receive a certificate of completion upon finishing the Data Science courses! With that said, remember that the key difference between these online learning tools and something like Youtube is having the additional expertise at hand for increased chances of success in your data science jobs whenever you need it.

For your convenience, we have handpicked several courses from which you can choose which one is most suitable for starting your Data Science career.

• Free Data Science Courses
• Best Data Science Courses (Free or Paid)
• Best Data Science Bootcamps
• The Data Science Course 2022: Complete Data Science Bootcamp - Top Data Science course with over 100 thousand ratings.

Conclusion

The world of data science is a highly competitive one, and it is important that you do your best to stand out from the crowd. Again, we would like to stress that if you want to make a career in data science online, it can be a great idea to start off by taking Data Science Bootcamp and courses.

To boost your Data Science skills, enroll in a course or a Data Science Bootcamp. These can be easily found online and will help you in Data Science analytics. Training for Data Science is a great way of learning the fundamentals of Data Science, such as Statistics, Modeling, Data Mining, and Machine Learning. These are the basic skills that will help you in your Data Science career.