The Prerequisites of Data Science – Unique skills every data scientist needs to have

Data Science

Data Science

Data science’s primary purpose is to find the patterns in data using different statistical techniques and get insights from the analyzed data. New technologies and smart products are derived from a massive data explosion in the present era of Artificial Intelligence and big data. 

With these latest developments, the demand and need for data are growing day by day. Many businesses and companies made data a center of focus, and data also created new sectors in the IT industry. 

Earlier data analytics was based on surveys and statistics. The invent of computers solved the complexity and simplified the process of decision making. Many companies realized the value of data science and making use of it to enhance customer experiences. 

Industry experts are making use of data to make the right business decisions and 

are maximizing their profit by understanding their consumer’s behavior and act accordingly based on the purchase patterns. 

Why do we need data science?

With data-driven science, we can take better decisions and make predictive analysis or pattern discovery.

With data science, we can 

  • Ask the right questions and find the cause of the problem.
  • Use various algorithms to model the data.
  • Perform exploratory study on the data. 
  • Visualize and analyze the results through graph and dashboards.
  • With data science, airlines can also optimize operations in many ways.
  • Data science helps in planning routes, scheduling connected and direct flights.
  • Helps in the prediction of flight delays.
  • Based on customers booking patterns, it provides promotional offers.

Prerequisites of Data science 

Some of the essential technical concepts or prerequisites of data science are:

1. Statistics– Data science is mostly all about statistics. The core part of data science is statistics which helps you obtain meaningful results and extract intelligence. Statistics can further be classified into two groups 

  • Descriptive statistics
  • Inferential statistics

2. Machine Learning– It is the backbone of data science. Data scientists need to have a solid knowledge of ML. The depth if mathematics helps you to use the right approach by using a practical implementation of mathematics. There are two areas of mathematics to be mastered in data science. They are:

  • Linear algebra 
  • Calculus 

3. Programming– To execute a data science project successfully, some level of programming is required. Knowledge about programming languages and tools are required for this field. While fundamentals are necessary, programming languages are essential. The most significant programming languages used in data science are:

  • Python
  • R
  • Excel
  • SQL Database

4. Modeling– Modeling helps to identify algorithms that are most suitable for a given problem. It is a process of training the Machine Learning algorithms to predict the labels from features and tune and validate them for business needs without holding out the data.

Applications of Data Science 

Data makes an impact almost on every industry. The data application areas are clustered, such as business logistics and business analytics, and include supply chain optimization finance. 

Many businesses intelligently use data science to find out consumer behavior and interests and offer services according to their customer preferences. 

Some of the major application areas of data science include:

  • Healthcare– data science is used by healthcare companies to detect and cure diseases, monitor health problems, improve diagnostic accuracy and efficiency, and optimize clinical performances. 
  • Banking– The banking sector can use data science for customer segmentation, lifetime value prediction, personalized marketing, risk modelling for investment banks, and customer support.
  • Internet search– search engines like Google, Yahoo, and Ask etc., use data science algorithms to deliver the searched query’s best results instantly. Google processes more than 20 PB (Petabytes) of data for this purpose. 
  • Targeted Digital Advertisements – from displaying advertising banners on websites to digital billboards at airports, almost all the digital advertisements use data science algorithms. 
  • Speech and image recognition– Social networking sites like Facebook and Twitter use image algorithms, whereas speech recognition products like Siri and Cortana use speech recognition algorithms. 
  • Gaming- Games are being designed using machine learning algorithms to upgrade them to a higher level. Data science plays a vital role in keeping the player interested and engaged in the game.
  • Fraud and risk detection– the finance sector is one of the first areas to use the application of data science. Data science is essential to Businesses, especially in financial matters, to keep an account of customer purchases and understand consumer behavior. 

Data science is dominating almost all the industries in the world today. It is considered the fuel of industries and is used in almost every field, including manufacturing, transport, and E-commerce. 

Conclusion 

Data science is a crucial part of businesses, primarily in the coming decade. It influences various areas, and its effect can be seen in multiple sectors of the retail industry. Data science can be incorporated and integrated into businesses to provide the best possible solutions for all business problems and manage the future with increasing productivity.

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