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What is Data Science: Applications, Prerequisites and Tools


An Overview of Data Science: Applications, Prerequisites, and Tools:



In an era full of demand for work, the data science job sector can be a good opportunity for job seekers. This fact alone makes data science one of the most important elements of the modern age. Data science is used in virtually every aspect of business operations and strategies. Data science initiatives can improve supply chains, product inventories, distribution networks, and customer service from an operational aspect. On a more fundamental level, they point the way to increasing efficiency and cost reduction. Data science also enables companies to create more effective marketing plans. Without it, businesses may miss opportunities and make flawed decisions.




Applications of Data Science


Almost every industry has found applications for data science.

1. In Search Engines


Search engines are the most useful application of data science. We all know that when we need to find something on the internet, we use search engines like Google, Yahoo, Safari, Firefox, and others. As a result, data science is used to speed up searches.



2. In Transport


Data science has also made significant advancements in the realm of transportation, such as with self-driving cars. It is simple to lower the number of accidents with the use of driverless cars.


3. In Finance


In the financial industry, data science is essential. The financial industry has long had a problem with fraud and the possibility of losing revenue. As a result, in order to make strategic decisions for the organization, the financial industry must automate loss risk analysis. Financial industries employ Data Science Analytics technologies to forecast the future. It enables businesses to forecast client lifetime value and stock market movements.


4. In E-Commerce


Data science is used in E-Commerce websites like Amazon, Flipkart to improve the user experience.


5. In Health Care


Data science plays an important role in the healthcare industry. Healthcare companies are using data science to develop advanced medical tools that can identify and cure ailments.


6. Targeting Recommendation


The most important application of data science is targeting recommendations. With the help of target recommendations, activities are automatically displayed in terms of products, services, or content that might interest the visitors based on their previous activities, preferences, or any other information.




Prerequisites for Data Science


1. Machine Learning: Machine learning is an essential part of data science. In addition to basic knowledge of statistics, data scientists need a solid understanding of machine learning.


2. SQL: The study and analysis of data are referred to as data science. We must first extract data from the database before we can analyze it. This is where SQL comes in. SQL is the basis for several database platforms. This is due to the fact that it has become the industry standard for many database systems. Modern big data systems like Hadoop and Spark, for example, employ SQL to manage relational database systems and handle structured data.


3. Tableau: Tableau improves how analytics teams understand and present data, which strengthens their collective data scientist skill sets. It's also great for swiftly putting together reports when they're needed—without having to create visualizations manually.

4. Programming: A successful data science project necessitates some level of programming. Python and R are the most widely used programming languages. Python is particularly popular since it is simple to learn and supports a variety of data science and machine learning libraries.


5. Databases: A data scientist should have a good grasp of how databases work, how to manage them, and how to extract information from them.




Data Science Tools

Even though the data science profession is challenging, there are a number of tools available to help data scientists succeed.

  • Statistics: Python (SciPy, Numpy, Pandas), Excel

  • Data Warehousing: MS-SQL

  • Data Visualization: Tableau, Python (Matplotlib, Seaborn), Excel

  • Data Analysis & Analytics: Python, Excel, Tableau

Machine Learning: Scikit-Learn

Deep Learning & Neural Network-Tensorflow, Keras

  • Data science as a profession and its growth

There are many opportunities for advancement in the field of data science in the future. There is already high demand, competitive salaries, and plenty of perks – which is why data scientists have been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor.



About the Author


SkillEnable is India’s top data science training and placement specialist. We aim to increase access to quality education in India. We bring to you specially designed employability-focused courses and make them accessible to all deserving candidates to bridge the gap between people who are looking for a job and companies who are willing to hire candidates with the right skills.





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