When big data started increasing, it was a huge problem to store all that data. Till 2010, organizations primarily attempted to figure out ways to store data securely and in an organized manner. With technologies such as Hadoop having resolved the problem of storing data, now individuals are concerned about utilizing and analyzing the data. Data Science has an excellent role to play in this case. Many things from science fiction films are real due to Data Science. It is also the future of Artificial Intelligence. That is why it is necessary to know what Data Science is and how it can contribute to your company's growth.
What is Data Science?
Data Science applies tools, formulas, and machine learning to uncover latent patterns in unprocessed data. How is that different from what statisticians have been doing for decades?
Difference Between Data Analyst and Data Scientist
An Analyst is simply looking at past data to tell us what did, in fact, happen. A Data Scientist does that, but also so much more — they find new concepts and apply them with sophisticated computer code to forecast which way things will go next. Data Scientists examine data from infinite perspectives, sometimes ones not yet conceived.
What Data Science Does
Data Science is helping in decision-making and prediction by using certain methods:
Predictive causal analytics tells us if something is going to happen or not, like if a customer will pay a loan within time.
Prescriptive analytics not only predicts what should happen, but also prescribes what to do. For example, autonomous vehicles use it to determine when to turn or brake.
Machine learning employs data to enable computers to make predictions or discover patterns. If your data is labeled (such as fraud or not fraud), this is supervised learning. When there are no labels, the computer clusters or groups the data itself — this is unsupervised learning.
Why Data Science Matters Now
Previously, data was tidy and small, and thus rudimentary tools could manage it. Nowadays, much data is big, messy, and from numerous sources such as videos, text, and sensors. Rudimentary tools cannot manage it anymore today, and that is the reason we require Data Science to interpret it all.
How Data Science Benefits Business and Everyday Life
Data Science enables businesses to understand what customers need by looking at their history of browsing or shopping. Autocars employ Data Science to drive safely on the roads. Weather forecasts and warnings for natural disasters are improved by Data Science models that leverage a significant amount of sensor data.
What is a data scientist?
Data Scientist is a person who applies science, math, and stats in analyzing data and solving problems. They apply special techniques and software to transform raw data into meaningful information.
If you wish to learn more, you can read "Who is a Data Scientist?"
Business Intelligence (BI) vs. Data Science
You may have come across Business Intelligence, or BI. People confuse BI and Data Science, but they are different.
BI examines history to observe what occurred and identify patterns. BI collects statistics within and outside a business, organizes them, and creates reports and dashboards. BI provides responses to such questions as how much revenue the business made for the last quarter or how the issue affected the business.
Data Science Lifecycle
These are the key steps of the Data Science process:
Phase 1 — Discovery:
Before you start, you must be aware of the project goals, requirements, and budget. You must ask the right questions and have sufficient people, tools, and time, and information in hand for the project. You must define the problem clearly and formulate some preliminary hypotheses to experiment with.
Step 2 — Data Preparation:
Here, you set the data to be ready to analyze. You store the data in a special area to work with on the project. You clean the data, fix errors, and arrange it with a process called ETLT (extract, transform, load, transform). With programs like R, you can clean and inspect the data, identify unusual points, and observe how the different parts of the data interact.
Stage 3 — Planning the Model:
Now you determine how to pursue the relationships in the data. This will help you formulate the algorithms in the following step. You use Exploratory Data Analysis (EDA) with numbers and plots to familiarize yourself with the data.
R is a program with many ways to build models and help you understand your data. SQL Analysis Services can perform some analysis of data in the database using regular methods and basic prediction models. SAS/ACCESS enables you to bridge Hadoop data and model diagrams that can be reused. There may be many tools, but R is the most utilized.
Phase 4 — Model Building:
During this stage, you create sets of data to test0 and train your model. You also have to determine whether your existing tools are sufficient to execute the models or if you require more advanced and faster machines. You shall employ various techniques such as classification, association, and clustering to construct your model.
How to obtain Data Science certification?
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Conclusion
Data Science teaches us to comprehend large and complex data in order to address real problems. If we take some basic steps, we can design robust models that give meaningful insights. Studying Data Science opens up the path to exciting careers and new ideas.
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