INTRODUCTION TO DATA SCIENCE
Data science is a culmination of various fields of knowledge, Mathematics, statistics, and programming. It deals with a vast amount of data both structured and unstructured, and encompasses the transformation of data into a form that makes it readable by humans, this also includes the data visualization aspect. Combining all of these derives the true essence of data science.
While the answers to all are easily available everywhere, we’ve made this blog as a complete and exclusive guide that will help you navigate through this with ease. Let us begin.
Data has been accumulating for some while now. But recently, various individuals have started noticing the potential it has to offer. Computer scientists and companies alike have started understanding what data has to say and simplified it for the rest of us.
They have mined, cleaned, analyzed, and interpreted the data and now have deep and comprehensible insights to share. These people are called Data Scientists and they are the artists whose expertise lies in Data Science.
Now that you know what data science is. The next question you might be wondering would be how does data science even work?
HOW DOES DATA SCIENCE WORK?
Data science as mentioned earlier is a boiling pot for various disciplines and areas such as statistics, programming, mathematics, etc. This also means that an effective data scientist has to have enough knowledge and be skilled in these aspects.
The aspects could be as varying as data engineering, data visualization, analytics, and the fields mentioned above.
Data scientists also are proficient in other disciplines like Artificial Intelligence. Mainly the parts of Machine Learning and Deep Learning. The technical synergy between these fields are used for making future predictions by creating models using techniques and algorithms to train and test data for accuracy and reliability.
For more information on Machine learning, you could check out these articles and videos
- Importance of Machine learning
- Understanding Machine Learning Algorithms
- What is Artificial intelligence
- How Artificial intelligence is changing the world
Data science applications are immensely popular, but before you dive deeper into that, let me explain how the lifecycle of data science works.
It all begins with:
- Capturing data: The extraction of data and data entry is the first stage in this five-stage process.
- Data Maintaining: The stage involves the storage of data and cleaning hence data warehousing and data cleaning as well as processing are naturally the next step post data acquisition
- Data processing: This stage involved mining classification and modeling of data ending in summarization
- Communication of data: once the data has been processed data reporting is done, to ensure the data is in human-readable format data visualization of data takes place. This stage is done for decision making.
- Analysis of data: In the final stage, the qualitative analysis, predictive analysis, and regression are performed according to the business problem.
Each step of this lifecycle requires a unique set of skills and knowledge, hence the complex nature of this subject. You now know the answer to what is data science, and the lifecycle, it’s time to answer your next burning question, “Where is data science applied?”
DATA SCIENCE APPLICATIONS
From the time you shopped online and got a recommendation for purchases like in Amazon, when you check for weather patterns in your phone and also when you were glued to your phone to follow the financial market patterns for your investments, and sometimes when you get emails that get automatically chucked into junk so that you don’t have to worry about it.
What about those background check the companies and other organization performs and the creditworthiness of people who have applied for financial services.
All of these are applications of data science!
Let us now look at some businesses innovating using data science to increase efficiency.
- Anomaly detection for diseases in Healthcare
- Artificial intelligence in Self-driving cars
- Recommendations in the Entertainment section
The healthcare industry has set an example of efficient ways of application of data science. Due to which they have saved time in diagnosis of diseases, as well as understand and recognize diseases as well as navigate through better modes of treatment.
The fascination towards driverless cars has been around the corner for a few years now, and seeing it become a reality has sparked interest in the amazing ways companies are using Data science for. The application of machine learning including the model of predictive analysis has found ways to take quicker and less dangerous routes saving time.
Isn’t it so cool that Netflix understands your taste of viewership or that Spotify knows what you’ll like and recommend songs that make you tuned in on loop?
Data science is the reason behind all this magic, Streaming services, based on your preferences, recognize what you generally like, and create a list that you would prefer.
I hope such applications have made you aware of the potential of data science. There are many such cool applications and future developments that data scientists are working towards.
It was symbolized so beautifully by this quote from Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics who said “The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
Data science is our present and our future. This is the right time for you to learn more about this and get ahead in your career. There are various ways to enter into this realm, one of the most popular and easier ones being online certification courses.
You’ll receive mentoring from industry experts and the course is curated to ensure you get all the real-time knowledge in mind. The projects are developed keeping the current industry’s trends in mind. To expedite your career prospect in Machine learning join more than 50,000+ students who have already upskilled themselves and are industry-ready in Machine Learning.