Did You Know?
Data Science is not just about algorithms and numbers; but it is the invisible force that is responsible for many big business decisions! Currently, data science is one of the influential fields in the modern digital era as it assists with business decisions, improves customer experience, and fuels major advancements within the company.
Today, most people are familiar with the business models of YouTube, Netflix, eBay, etc., and how they are customizing solutions to meet our requirements. On the contrary, a study published by Forbes shows that data science professionals spend almost 80% of their time cleaning and exploring data. In this blog, we will discuss some interesting facts about data science and what skills you might require to become skilled data scientists in the future.
What do you understand about Data Science?
“There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every two days.”
Eric Schmidt, Executive Chairman at Google
An interdisciplinary field connected to big data and machine learning that is responsible for leveraging scientific processes, algorithms, and methods to extract informative inputs from unstructured and structured datasets. The data science workflow requires a set of complex processes that include data warehousing, data cleansing, data acquisition, data modeling, and other functions.
Interesting Facts About Data Science
The role of data science has expanded from traditional analytics to offering customized business insights across sectors (be it healthcare, finance, operations, etc.). Here are some of the key reasons highlighting the importance of data science in modern business:
- Detecting fraudulent activities – In the finance sector, data analytics approaches are helping to identify different fraudulent activities. According to a statistic published by Statista, the use of AI fraud detection systems has led to a reduction of potential threats and losses by 40% over the last five years.
- Interference of Machine Learning – As a subset of data science, this is crucial to automate data analysis. With algorithms improving over time, machine learning models can perform advanced analytics like detecting outcomes, identifying patterns, and machine decisions without human intervention.
- The Revolution of Personalized Business - Ever thought about how Netflix recommends the perfect show? This is because machine learning algorithms analyze your watch history then compare it with other users and predict what you might love to watch next. Data science is the secret behind hyper-personalization helping companies tailor their services according to your preferences.
- Improving Customer Experience – As established before, data science is a powerful tool to drive business growth and improve industrial expansion. One of the major responsibilities of data scientists includes studying customer behavior and offering them customized solutions.
- The Role of Big Data Technologies -The significant growth of big data technologies like Hadoop, SQL, Spark, etc. have drastically changed the way data is stored and used. These technologies are useful to handle massive datasets especially in real-time to provide effective solutions.
- Rise of Citizen Data Scientists - The rising demand for ‘data scientist skills’ has led to an increase in such roles where individuals do not have to hold the ‘data scientist title’ to work on its tools and techniques. Instead, the tools have become user-friendly making data analysis easily available to a broader audience.
How Does Data Science Work?
Data science follows a structured process that impacts many sectors around the world. Here are a series of steps that are often referred to as the data science lifecycle:
Step 1
Defining the problem – Data scientists require a clear idea of the problem, such as predicting when customers will leave and detecting fraud or suspicious activities.
Step 2
Collecting and cleansing data – After defining the problem, data scientists are required to gather data that can come from different databases, APIs, or other sources to organize it.
Step 3
Feature engineering – Data Science's future lies in feature engineering as it involves selecting and developing the right data attributes, which is one of the pillars for building effective models.
Step 4
Modeling – This step involves applying the right machine learning algorithms such as neural networks, decision trees, etc. to provide an appropriate solution.
Step 5
Model evaluation – After the model is designed, skilled data scientists are required to test the model using necessary metrics such as accuracy and precision to check if the model is effective.
Step 6
Model deployment – Once the model is successfully evaluated, the data science professionals deploy it to generate real-time and batch predictions.
Start Your Data Science Journey By
Learning Programming>> Gaining Strong Foundation in Numbers>> Working on Diverse Projects
Quick Tip:
Pursue Renowned Data Science Certifications – Industry-centric and vendor-neutral certifications from globally renowned institutes like the United States Data Science Institute (global-accepted and well-reputed), ENSAE Paris (France), Tsinghua University (China), etc., increase your credibility and give you a competitive edge in the pursuit of your data science career.
Conclusion
Data Science helps businesses discover competitive insights, create personalized solutions, and automate tasks to boost organizational efficiency and reduce budget constraints. Pursuing a data science career comprises technical expertise, ethical responsibilities, and problem-solving skills. Undoubtedly, this field plays an important role in shaping the future; hence, with the rising demand for data science technologies, the demand for skilled professionals is at an all-time high!
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