What is Data Science – The Ultimate Guide for 2023

With the sudden growth and development of technology, it’s very thrilling to see where the world is headed. Numerous inventions have spiked productivity in businesses and even saved the lives of many humans worldwide.

As technology gets more intelligent and increases in complexity, we’ve started using them to automate big chunks of our lives. This makes us more efficient in our day-to-day tasks. And Data Science plays an important role here. But you might be asking yourself, “What exactly is Data Scientist, and how do I become one?”. Well, you’re at the right spot; let’s take a closer look at this broad term.

What Is Data Science?

Data Science isn’t just one thing by itself. Instead, it’s a combination of more scientific fields—like statistics, AI, and data analysis—to see the real value in collected data. And there’s a lot of data floating up in the air right now, even though you may overlook that.

Think of how many people use Instagram in one day. And then, think about how much Facebook(Instagram’s parent company) collects and stores information from those same people. This is an insane amount of data that the companies have in their databases (Facebook alone has 350 million photo uploads every day), and they just let it sit there.

That’s where Data Scientists come in, and in the past year, they have been a crucial part of any company. These people have advanced skills that help them in data collection, analysis, and building complex algorithms. Data Scientists need to be result-oriented and have data-related skills that will help them make sense of big datasets.

Having a Data Scientist in a company means utilizing every aspect of the data collected from users. Which, if done right, could bring tremendous value to the company.

Profit isn’t the only reason why Data Science is actual, though. The Health care system uses Data Science for the prevention and diagnosis of certain diseases. The government uses Data Science to prevent crimes and traffic problems, etc.

How To Become A Data Scientist

Tools and Skills

Want to become a Data Scientist? Then you should learn about the required skills you’ll need before you start. While it’s doable to go at it alone, Data Science is an extensive and, at times, very dense subject.

That said, let’s take a look at what you’re in for pursuing a career in Data Science.

Programming and Analytical Tools

Programming has a massive part in Data Science. This is where you’ll create your algorithms and manipulate the data to your liking and need. Most people involved in Data Science use the programming language Python (as of 2018, 66% of Data Scientists reported using it) because it offers the programmer a multi-use and object-oriented environment used for data science purposes and integrating with apps and apps websites.

Python

Python has a lot of Data Science-related libraries that you should know about and take advantage of, like Pandas, NumPy, SciPy, TensorFlow, Selenium, and more. These libraries offer their own special functions already prebuilt for you, which you can use in your code to make your life easier. For example, you could use the Pandas Python library to easily create tables full of thousands and thousands of data entries. And on the other hand, you can use Matplotlib to visualize that data in a static, animated, or interactive environment.

R

R is an open-source language for data analysis, and it was the most popular until it got dethroned by Python a few years ago. It’s still valuable to learn R if you’re doing academic research or something similar, though, as it has all of the features of the Data Science Python libraries built in. It also has its own integrated environment for data entry, manipulation, and visualization.

SAS

There’s also SAS, which has built-in features through a graphical user interface, which would prove more productive for a starter Data Scientist. But, the big drawback here is that it’s an expensive enterprise program, which still makes Python and R (both free) the smart route to go, especially when starting out.

Other Tools

Alongside programming knowledge, analytical tools provide a practical approach to big data processing and give you valuable insights. These tools include SQL, Spark, Hadoop, which let the user store data and optimize structured data processing.

SQL is a standardized language that works with databases and perform whatever function you need on the data in them. As for Spark and Hadoop, they serve together for working with large unstructured sets of data.

Data Visualization

We touched on visualizing data with Python and R, but we can’t stress enough how vital these things are for big decisions and predictions. If you’ve been looking into enrolling in data science courses, we bet you’ve already seen every one of them cover the basics of data visualization.

Many big corporations ask for Data Scientists to represent what they’ve collected and analyzed in the form of charts, bars, or pies to reach a mutual understanding of the presented information. As a Data Scientist, your job will often include telling people who have no idea how programming and statistics work what they should do with their company or product. And, it’s one thing if you know what the data means, but you’ll still need to communicate that through natural language to the other, less-knowledgeable people you’re working for.

Tools for Data Visualization include Tableau, Microsoft Power BI, Looker, Sisense, Zoho Analytics, and others.

Math

Math may be the scariest topic to most people, but if you’re considering being a Data Scientist, you know you’re going to be surrounded by millions of numbers every day. Therefore, it’s crucial to know crucial mathematical concepts in the fields of Statistics, Linear Algebra, Calculus, and Probability.

Statistics offer insights such as a set’s mean, median, standard deviation, and numerous distributions. Therefore, Data Scientists need to have a firm grasp of all aspects of statistics to test and prove their hypotheses and avoid bias when undergoing an experiment. Then, when all is done, descriptive and inferential statistics are going to help you visualize the data in the forms of bars and graphs and make assumptions and predictions based on that data.

Linear Algebra is the foundation of Data Science and Machine Learning. With it, you learn basic principles, such as working with vectors and matrices, which are used to represent data sets and Machine Learning models.

Probability involves topics like distribution functions, central limits, random variables, standard errors, the Bayes theorem, etc. These actions are to perform tests and see the trends in your data.

If you’re thinking about pursuing data science courses, it’s worth considering taking some form of math course as well. Khan Academy or Brilliant are excellent resources where you can find various math courses. This will definitely help you in the long run and make all of these complex Data Science topics much easier to grasp and understand.

Machine Learning

If you want to utilize the technology given to us, then learning core Machine Learning techniques as a Data Scientist will help you feed big data into algorithms and produce important information that you need to know. These algorithms include neural networks, linear regression, Naive Bayes, K-Nearest Neighbors, random forests, ensemble methods, and many others.

Web Scraping

We’ve been talking about collecting data a lot, but how do Data Scientists do it? If you don’t have your dataset collected yet, do you go through websites, one by one, and enter everything manually? Well, technically, you could do that, but that would take days, even weeks. The other, more efficient way is a technique called Web Scraping.

Scraping is used by Data Analysts all the time to automate the data-extracting process from the Web. It involves two entities. First is an AI algorithm, often called a “crawler” or a “spider,” used for crawling (analyzing) a website URL so that you can pass it on for scraping (extracting) data. Second, the tool you pass the data from the Web to is called the “scraper,” which is specialized for extracting the data from HTML and XML files.

For example, say you want to analyze all of the albums charting on Billboard right now. You wouldn’t want to manually enter every number, artist, and album name in a datasheet, right? So, with a scraper, you could do that in a few lines of code and watch all 200 albums get extracted in an instant, right before you.

Python has a special library made just for scraping—BeautifulSoup—which is really easy to use. Suppose you want to learn more in-depth about Web Scraping and any other topic we talked about above. In that case, you can check out the data science courses offered online, which are going to help you tremendously in studying this complicated but beautiful craft.

The Data Science Process

The Data Science process, lifecycle, pipeline, and any other term you might hear being thrown around, includes more than one process that is being handled at the same time. Of course, everyone has their own definition of what works best for them and their team, but here are some processes that most Data Scientists have reached a mutual consensus on.

Gather

This is the first and most logical step when getting into data analysis—you can’t really analyze data without having the data in the first place. Gathering data, be it from search engines, social media sites, businesses, or your own database, can be done through a variety of ways, including web scraping, real-time recording, manual entry, and others.

Maintain

This step involves putting the data you’ve acquired in your databases and refining it for analyzing purposes or machine learning algorithms. The “refinement” process includes cleaning up the data, putting it in a consistent format, doing additional processing work, figuring out the data architecture, and more.

Process

The processing part of Data Science includes data mining, classification, modeling, etc. Here, Data Scientists examine various things, such as patterns, biases, and distributions in the data to determine which method is best to use in a particular scenario.

Analyze

This is where the fun part begins, where Data Scientists finally perform something on the data. One can do this through statistical analysis, regression, prediction, machine learning algorithms, etc. In the end, you have valuable insights and information about the data you fed to the algorithms.

Communicate

Finally, the last stage involves taking all the analyzed data and making sense of it. This process includes visualization (creating bars, graphs, pies, and charts) and explaining what each section means. You might think that this is useless because you may already know what your results mean—but think about all the people that need to know that information but don’t know how to make sense of numbers and countless data entries. By visualizing the results, you’re making it easier for the higher-ups to make the needed decisions.

Keep in mind more sub-steps need to take place in specific situations, and they can all overlap with each other—making Data Science a very unpredictable and, at times, messy job. But once you get into it, it isn’t as scary as it seems right now.

Types of Data Scientists

Data Science is a broad spectrum of tasks that need to take place in a specific order to be efficient. And, if you’ve ever listened to data science courses, we’re sure the Instructors have already brushed over the different tasks that need to be done. That’s why we have more types of Data Scientists out there that work together to create one harmonious dynamic.

Data Engineer

There’s a lot of confusion regarding data scientists and data engineers. No wonder you’ll often hear the terms in the same context. But the reality here is that they do vastly different jobs, and we should not use them as synonyms for each other.

Data engineers have the job to build and manage the data extracted by companies. They need to control the storing of data and create the infrastructures where that data is going to be in accordance with the company’s needs. Data engineers also work closely with Data Scientists and all people of all sectors to make sure that data is being transformed, analyzed, and visually presented in a correct and understandable way.

They work with programming languages, such as Java, NoSQL databases, and frameworks, such as Hadoop and Spark.

Mathematician

This comes as a natural addition to any Data Science team because it is very much about creating complex algorithms and understanding fundamental mathematical concepts in order to increase their efficiency. They often do research and can work with predictions and forecasting, pricing, supplies, defect control, etc.

Statistician

Being a Statistician working in Data Science is maybe the thing that most people think about when they hear the term “Data Scientist.” And, they’d be right. Data Science, at its core, is about using various statistical methods to create models for data collecting and analyzing, experimentation, clustering, and everything in between.

Data Analyst

You may find the terms confusing by now because all of them, more or less, sound the same. Everyone does something with data!

Well, let’s put it into practice. After the Data Scientists and Data Engineers have done their job (collecting and storing the data and putting it through an algorithm), now it’s the Data Analysts’ very important job to make sense of it all. Then, they gather the results they’ve received from the analysis and try to identify trends that help the business in a meaningful way—be it sales, brand recognition, ad campaign trends, etc.

Before starting the work on the data, the Data Analysts typically overlook all of the steps in accordance with the company goals they’ve set up before starting. They work with Python, R, and SAS the most and need sophisticated mathematical skills (namely statistics) to do their job.

Enrolling In Data Science Courses

We know you may be wondering how to become a Data Analyst or just how to get started with Data Science in general. Luckily enough, we live in the digital era where technology and people are thriving together, so there are a plethora of data science courses that you can choose from.

These courses cater to your needs the most. You can go through them in your time, do the homework when you want. Meaning you can adjust the learning rate to your comfort. This is especially good for people who are not able to pay hundreds of thousands of dollars for tech college. But also for people who are already studying something else and want to expand their career opportunities in the future. The opportunity is also great for anyone looking to pick up a skill.

Most of them go over the basic concepts of Data Science and teach you the most fundamental skills you need, as we mentioned above, for starting your new journey. Some of the topics covered are: explaining the entire Data Science process, databases, visualization, data mining, machine learning, artificial intelligence, cloud concepts, statistical learning, and most importantly—you’re guaranteed to learn Python by the end!

How Data Science Guides Businesses

Data Science has great benefits for small and huge companies. As technology evolves, more and more businesses have started implementing and hiring Data Scientists to extend the lifespan of their companies.

First of all, Data Science is great for decision-making. You’ve probably figured it out by now, but the biggest reason why businesses hire whole departments of Data Scientists, Engineers, and Analysts is because they don’t want to make wrong choices that could kill the company later on. With the right implementations of certain algorithms, Data Scientists could easily forecast what would happen, and then the business executives have the power to make a choice according to the results.

A second popular reason why businesses like to incorporate Data Science is to make better products that buyers actually want. With the collection of user experience and analyzing that data, a business could cater more towards the needs of the people, attracting more customers and keeping them coming back at the same time.

Another reason may be to identify opportunities that you can’t detect without analyzing the extracted data. Many businesses have tons and tons of information stored in their data pools that just sit there and do nothing. But, as companies are hiring more and more Data Scientists, they’re utilizing what they already have in order to do what’s best for the company.

The Goals of Data Science

We’ve spent nearly all this time describing what Data Scientists do, but what about why they do it? There must be a certain end goal, right? Of course, there is, and it’s surprising how many familiar things you’re going to find in the goal list of Data Scientists because some of them are things we take for granted every day of our waking lives.

Some of the goals of a Data Science process are:

• Recommendations—e.g., the YouTube homepage, Spotify Discover and Netflix
• Email spam recognition
• Fraud recognition
• Automated decision making—e.g., account approval based on personal ID
• Text recognition—e.g., live markup from photos
• Voice recognition—e.g., virtual assistants on smartphones, like Siri
• Photo recognition—e.g., apps like Google Lens, which recognize objects and give you relevant search results
• Facial recognition—e.g., Face ID unlock on iPhone
• Audio recognition—e.g., apps like Shazam for identifying music

How Much Do Data Scientists Make?

If you’ve ever talked with anyone working in IT, you’ve noticed how much they talk about a Data Scientist’s salary. And we must admit, it’s with good reasoning. According to the 2020 Burtch-Works study, it’s reported that entry-level Data Scientists have a median starting salary of $95,000 per year! Mid-level Data Scientists go up to $130,000, and experienced Data Scientists are getting paid as high as $165,000 to $250,000!

It’s clear that companies value Data Scientists and what they do. They get the idea that without them, their sales and all-around reputation could plummet to the ground.