The importance of data Science brings together the domain expertise from programming, mathematics, and statistics to create insights and make sense of data. When we think about why data science is increasingly becoming important, the answer lies in the fact that the value of data is soaring heights. Did you know that Southwest Airlines, at one point, was able to save $100 million by leveraging data? They could reduce their planes’ idle time that waited at the tarmac and drive a change in utilizing their resources. In short, today, it is not possible for any business to imagine a world without data.
Data science is high in demand and explains how digital data is transforming businesses and helping them make sharper and critical decisions. So data that is digital is ubiquitous for people who are looking to work as data scientists.
Who is a Data Scientist?
Data scientists are in constant demand because it is a data-heavy world!
Data scientists are a new growing breed of professionals, highly in demand today. This term was introduced some years back by data leads to companies in LinkedIn and Facebook. And today, we have a huge influx of data scientist nerds working across verticals. This demand happened due to the sudden need to find brains who could wrangle with data and help make discoveries and ultimately empower organizations to make data-driven decisions. This also marked the dawn of digital transformation. From organizations trying to meddle with petabytes of data, a data scientist’s role was to help them utilize this opportunity to find insights from this data pool. They will use their computer science, statistics, and mathematical skills to analyze, process, interpret and store data. It is not just about analytical skills, but a data scientist’s scope combines the best social skills to discover trends.
Role of data scientist
In today’s emerging data-driven businesses, data scientist plays business-critical roles.
Typically, a data scientist’s role comprises handling humongous amounts of data and then analyzing it using data-driven methodologies. Once they can make sense of the data, they bridge the business gaps by communicating it to the information technology leadership teams and understanding the patterns and trends through visualizations. Data scientists also leverage Machine Learning and AI, use their programming knowledge around Java, Python, SQL, Big data Hadoop, and data mining. They require to have great communication skills to translate to the business their data discovery insights effectively.
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Why data science is important
The simple answer to this billion-dollar question
Why data science? It is simple. Making sense of data will reduce the horrors of uncertainty for organizations. Data science is a rapidly growing function, but industry experts say it is still in its infancy. In 2003, iTunes took 100 months to reach 100 million users, while for Pokemon in 2016, it took days to reach the million mark. In the graph below, you will see how from 1878, user outreach timelines kept changing by changing away from the old models of marketing and promotions. This was posted on by Sequoia Capital that shows how from two decades back, businesses moved from legacy techniques to social media. The evolution happened due to the massive digitization of promotion platforms that run on data insights.
Data mining for excavating insights has marked the demand to be able to use data for business strategies. There are a few important stages for housing data science within businesses. From doing business health checks, evaluating data to maintain data through data cleansing, warehousing, procession, and then analyzing and finally visualizing and communicating.
Look at this data science life cycle explained in the image below by Berkely.
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Benefits of data science
Why data science is important as a foundation for taking businesses to the next level
Data is valuable, and so is the science in decoding it. Zillions of bytes of data are being generated, and now its value has surpassed oil as well. The role of a data scientist is and will be of paramount importance for organizations across many verticals.
- Data without science is nothing.
Data needs to be read and analyzed. This calls out for the requirement of having a quality of data and understanding how to read it and make data-driven discoveries.
- Data will help to create better customer experiences.
For goods and products, data science will be leveraging the power of machine learning to enable companies to create and produce products that customers will adore. For example, for an eCommerce company, a great recommendation system can help them discover their customer personas by looking at their purchase history.
- Data will be used across verticals.
Data science is not limited to only consumer goods or tech or healthcare. There will be a high demand to optimize business processes using data science from banking and transport to manufacturing. So anyone who wants to be a data scientist will have a whole new world of opportunities open out there. The future is data.
Use of data science
Industry verticals leveraging the power of data
Data science is important for businesses because it has been unveiling amazing solutions and intelligent decisions across many industry verticals. The epic way of using intelligent machines to churn huge amounts of data to understand and explore behavior and patterns is simply mind-boggling. This is why data science has been getting all the spotlight.
Deloitte Access Economics report suggests that 76% of businesses will be pumping up their data analytics spending. For example, big data helps them understand their customer personas and improve their experiences by learning from historical purchase data. For example, the medicine vertical could use data science to compile the patient’s history and help make sense of their well-being status and prescribe correct remedies from time to time. In the banking sector, for example, Bank of America leverages NLP (Natural Language Processing). It uses predictive analytics to have a virtual assistant, routing customers to important tasks that need their attention, like upcoming bills, etc.
Data science as a term was first coined in 2001. It has been an incredible journey since the last few years to see its importance for business verticals trying to make intelligent decisions and build future roadmaps. Data is a force to reckon with, and organizations worldwide capitalize on this valuable asset to develop smarter solutions and capabilities.
Why do you want to learn data science
5 valuable reasons to pursue data science as a career
In 2019, Salesforce acquired Tableau and Google acquired Looker, a startup in the data analytics space. These two stories showed how businesses across the globe are shifting their focus to data-driven goals. Some more stories highlighting worth here are
- Lionbridge acquired Gengo
- DataRobot acquired three companies – ParallelM, Cursor, and Paxata
- HPE acquired MapR
Thinking about getting started with a career in Data Science? There cannot be a better time than now!
Did you know that Glassdoor discovered that a data scientist’s role is one of the top-scoring jobs in 2020? It did not just rank in terms of its demand but also on job satisfaction metrics. Learning data science today is not tough anymore. You could take up professional courses or even resort to an array of online courses to kick start your journey as a data scientist. If you are an undergraduate with basic knowledge of programming and great analytical skills, you can move along the data science curve.
In business, the use of Data Science is in varied domains. This gives you ample scope to learn and grow in the role of a data scientist. Here are the 5 reasons why you must learn data science.
- Great career trajectory with data science – Yes, you will have rewarding career growth in this field. Data scientists bring tons of value to organizations and are the most sought after roles in today’s scenario and will be in the future.
- Great potential to branch out with different options – You can choose to branch out as a data engineer, an analyst, or an ML engineer, or even a data science manager.
- Highest salary takeaway quotient – As a Data scientist, you can expect to take away a great salary package. Usual data scientists are paid great salaries, sometimes much above the normal market standards due to the critical roles and responsibilities.
- Become a decision-maker – Not every job opportunity will give you the power to make informed business decisions. For a data scientist, that is the core responsibility. That is how you kick start. The credibility will always be rewarded because of the lack of talent pool in the ecosystem.
- Less competitive because it is a highly analytical role – Competition is less, but demand is not. With a limited talent pool, there is always a challenge for businesses to hire in these roles. Once you join in, you become a decision-maker and face less competition from your organization’s peers for you having a unique skill set.
Eligibility for Becoming a Data scientist
You can pursue Data science if you come from mathematics or computer science academics. If you have a science background or come from quantitative backgrounds like finance or business, you can easily opt for this career option.
Career option for non-technical folks
For students who hail from non-technical backgrounds, good prior knowledge of analytic tools such as SQL, Tableau, or Excel can help them kick-start a data science career. If you lack programming skills but still have a good understanding of concepts such as logical programming, functions, and loops, dive in with your career journey in data science.
Now that we have already demystified some of the myths around who can pursue a data science career, here are a few more frequently asked questions that we’ll answer for you.
Frequently Asked Questions – Answered for You
1. What is the need for Data Science?
The reason why we need data science is the ability to process and interpret data. This enables companies to make informed decisions around growth, optimization, and performance. Demand for skilled data scientists is on the rise now and in the next decade. For example, machine learning is now being used to make sense of every kind of data – big or small. Data metrics are driving every business decision. The job market scenario for data scientists will grow to almost 11.5M by 2026 [U.S Bureau of Labor Statistics]. Companies are busy ramping up their data science workforce to enable higher efficiency and planning.
2. Why is Data Science interesting?
Did you know in the 1900s, German inventor Dr. Herman Hollerith created a mechanical system to record data with a punched card for data processing for the US census? Since then, we have seen an evolution in how data is being used to measure, scale, and optimize. As a data scientist, the journey through discovering insights leads to innovation.
3. What is Data Science useful for?
Data science is a process that empowers better business decision-making through interpreting, modeling, and deployment. This helps in visualizing data that is understandable for business stakeholders to build future roadmaps and trajectories. Implementing Data Science for businesses is now a mandate for any business looking to grow.
I hope in this article I have answered all your questions. This is where your journey to becoming a successful data scientist begins. Visit AnalytixLabs to get started with online and on-campus courses on Data Science. All the best to you.
You may also like to read:
1. How to Become A Data Scientist – Step By Step Guide
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