What is Data Science - A Complete Guide
What is Data Science - A
Complete Guide
What is Data Science?
Data science is the technique of unearthing patterns and trends from a range of data. It encompasses several fields like statistics, mathematics, computation, and more.
The term data science came into existence around 2008 and was coined by businesses. They felt the need for
experts who could organize and analyze data to solve a plethora of business
problems.
In a glimpse, data science
is able to:
● Zero in on the relevant questions
● Collect structured and unstructured data from
countless sources
● Sort or organize the data
● Analyze the data and come up with results
Data science has gone through a significant transformation in recent years. The invention of groundbreaking technologies like artificial intelligence (AI) and machine learning (ML) has taken data science to the ultimate level.
Now, we can use data science to make sense of highly complex data. In addition, machine learning is able to process vast volumes of data in minutes.
These changes have made
data science a need of the hour. But why?
The Importance of Data
Science
We create data continuously as we engage with modern technology. Therefore, we are creating data when ordering food from a mobile app or booking an appointment with the doctor on a website.
As a result, businesses and governments possess a ton of data today. As per Oracle, 90% of the data on the planet was created in the past two years.
However, most of the time, this data sits in storage units without doing anything.
Data science comes in handy during such times to interpret data and bring transformative changes in society.
Businesses can use data science to understand customers better. Or, governments can rely on data science to identify populations at risk of certain diseases.
In short, data science can open up new possibilities for organizations. It can also learn from the data being fed to it and perform the role of a data analyst.
Best of all, most of the
process is performed by digital solutions. As a result, we can reserve human
judgment for activities where data science falls short or needs a helping hand
from our brains.
How Does Data Science Work?
Data science generally
comprises several steps to deliver the final result. Let's take a look at the
stages of the data science lifecycle and the associated workflows:
Data Capture
Data capture is all about collecting data from a plethora of sources. It may involve processes like data acquisition and data extraction.
Of course, you will first need to decide the sources from which you want to collect data. For example, a business may collect user data from its apps, websites, and social media pages.
In addition, data capture may involve data entry. You may need to enter the data into a system or database for analysis.
Data entry can be manual or automated. It is even possible for the software to extract data from receipts and match them with the proper transaction. This is an example of data extraction at work.
So, the first stage of
data science is to collect the data to make sense.
Data Maintenance
The data at hand may not be outright useful. We may need to clean the data or sort the data before we can process it.
Data maintenance takes
care of such needs. It may include workflows such as:
● Data warehousing or storing the data securely
● Data cleansing to reduce or remove noise from
data
● Data staging to store or process data temporarily
● Data processing
● Data architecture to guide the storing or
processing of data
Data maintenance is all
about preparing your data for the next stage.
Data Processing
In this step, we use digital tools to process data to generate specific outcomes. It may include workflows such as data mining to identify patterns or anomalies. We can use a wide range of data mining techniques based on the specified needs.
Data processing might also include data classification or clustering. It allows the sorting of data into categories or groups to facilitate analysis.
Additionally, you may
conduct data modeling to make your data simple or more readable. Moreover, data
summarization helps to make a concise summary of a large set of data.
Data Analysis
Data analysis is perhaps the most important step of data science. This is where we perform predictive analysis on data to forecast trends or patterns.
For example, a business may accurately forecast its cost five years down the line. We can run different types of analyses to answer different questions.
Technologies like AI and ML also facilitate data analyses and can even perform at par with humans or even better.
Data analysis may also
involve qualitative analysis.
Data Communication
The last step is about presenting the findings and making them more understandable. It may involve data reporting to report the outcomes to the C-Suite or the President.
You may also want to create data visualization like graphs or charts to make information easy to digest.
Most importantly, this step may include business intelligence and decision-making. It is where the findings of data science provide intelligence or insight to businesses.
As a result, they can
provide better products and services. They can also take on the competition and
fare better than rivals.
Uses of Data Science
Data science has ample
use cases for a wide range of industries and governments. Let's check out some
instances where you may call upon data science:
Autonomous Cars
Major brands like Tesla and Ford are experimenting with self-driven cars. These automobiles rely on sensors, cameras, predictive analysis, reporting, and more to navigate safely.
The system is governed
by data science workflows, without which autonomous cars would have been a
disaster.
Businesses
Enterprises are the
leading adopters of data science. They use the process for several purposes,
such as:
● Boosting sales by recommending products based on
past purchases
● Improve customer retention by finding out why
they leave
● Identify areas of cost savings or opportunities
to improve revenues
● Make financial decisions and forecasts
Entertainment
YouTube always seems to recommend the songs you are more likely to love. How is that possible?
The secret behind the act is data science. It analyzes the songs you have listened to on YouTube to recommend the ones you might like.
OTT platforms like
Netflix use similar technology to recommend TV series or movies. The whole
personalization trend is driven by data science.
Healthcare
Data science, coupled with modern technology like wearables, has transformed healthcare. Providers can now assess a range of patient data to improve diagnosis and treatment outcomes.
In addition, data
science may facilitate medical research. It might even track the progression of
novel diseases that we haven't encountered before.
Delivery & Logistics
Data science goes a long way to making logistics businesses profitable. It can scan existing routes, traffic conditions, weather, and more to identify the best routes.
In addition, data
science allows the logistics industry to save fuel and distance. Therefore, it
plays a role in improving profitability.
Finance
The finance industry can rely on data science to track fraud and anomalies. It can also go through large sets of data to make predictions and drive decisions.
In addition, data science helps businesses in finance to save costs and labor.
No wonder the data science platform market will amount to 68.02 billion in 2026. Additionally, it will keep growing at a CAGR of
24.73%.
Strengths and Weaknesses of
Data Science
Like all other
verticals, data science has a few strengths and weaknesses. Let's go over them
in brief to give you a complete picture.
Strengths of Data Science
Here are the strengths
of data science:
Better Services
Whether it's the
government or a business, data science can improve its services. The technique
can generate vital insights that serve the needs of customers or citizens.
Better Decisions
Data science can eliminate guesswork to enable improved decisions. It can learn from past data and use advanced predictive technology to make forecasts.
You can implement
data-driven decision-making in all sectors, be it businesses or defense.
Cost Savings
Data science is enabling businesses and governments to save millions. It can zero in on untapped or hidden value in supply chains to lower costs.
You may even detect
fraud or anomalies to reduce losses.
Better Opportunities
Data science can discover opportunities for all adopters. It may assess a course and students' performance to recommend a better instruction delivery approach.
In the same way, businesses may find new customers or markets to expand sales.
However, not everything
is well about data science. Let's take a look at its drawbacks.
Weaknesses of Data Science
Data science shares a
few concerns, such as:
Data Privacy
Individuals generate most of the data used by businesses or governments in data science. Organizations may collect or use the data in ways that could affect the privacy of a person.
For example, some websites track other websites you visit and collect that data. However, you may not want anyone to know what you do online.
In addition, data may
contain personally identifiable information that could be revealed to
unauthorized parties.
Expensive
Data science is an expensive field. Users have to pay a large sum to take advantage of data science platforms to generate insights.
As a result, small businesses and nations may not be able to leverage the technique.
Moreover, you need data
scientists to make the most of data science. These professionals are in high
demand and charge high salaries.
Therefore, data science
might be a cost-prohibitive option for some people.
It's Ever-Evolving
Data science has evolved significantly from its inception. Therefore, adopters have to invest in continuous learning and growing to stay updated.
Additionally, they may
need to increase investment to enjoy the latest technology and trends.
Final Thoughts
Data is the gold of the modern age. Every business is looking to make use of its data to drive more sales or improve services.
Data science implies working with a huge volume of data to make its sense. It relies on several fields and technologies to make the results possible.
Current technological advances like AI and ML are making data science more accurate and reliable. Therefore, its adopters can stay ahead in the race and identify unique opportunities to win the competition.
Data science consists of
several stages like data collection, data processing, and data analysis. It is
used in different industries like healthcare, cybersecurity, logistics, and
more. Even governments rely on data science to attend to the citizens' best interests.
In the future, data
science may become more common and cost-effective.
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