Today, data are widespread around the world. Organizations of all sizes are carefully collecting customer data. The customer data is used to deliver a better decision, resulting in high profits and business gains. Undoubtedly, data rule the world now.

The interest in data has led to an increasing demand for data scientists globally. 

With the rapidly growing business and technology world, data have become the center of all decision making. The use of a large amount of valuable data has become mainstream, industries; eager to win big companies in their niche, are investing more and more in the collection and effective management of data by hiring data scientists. 

Consequently, data science has become the hottest job and promising career to get into.

However, if you are new to this and want to know more about the hyped “data science” then here’s a helpful beginners guide to data science. This guide will help you take part in this fastest-growing field.

What is Data Science?

Data science combines various learning tools, algorithms, and rules to filter out, prepare and analyze data. We analyze big data using data science. 

In other words, data science is the area of study involving science to carefully extract an array of data and key insights from the data to resolve complex problems and provide solutions.

The data science expert, that is data scientist, uses mathematical terms, statistics, applications, technology and a variety of other data processing tools to complete data-operations. 

Here’s What Data Scientist Do –

The data scientist's job roles required extracting insights from the data evaluate it and are expected to be very curious to carefully formulate the significant questions that will assist them to derive the better answer for business progress. 

Data scientist with the collaboration of data engineers processes the freshly collected raw information-data from multiple resources; refine it using mathematical concepts, programming, statistics, and various other modern scientific tools to derive the final meaningful result focusing on the present and promising future of the business.

Data scientist follows a data science pipeline to extract efficient data:

Data Science Pipeline

The data science pipeline describes the flow of the entire process from receiving data to make accurate calculations and interpretation of data.

Having a strong grasp of the typical workflow of the data science pipeline is a step towards a better understanding of the business and resolving business issues. Here what is included in the pipeline:

• Obtaining Data:  To start the data science work, we start with collecting data. To gather all the necessary data, a data scientist must know the data requirements. The “what”, “why”, “where” and “how” of the data required are important to proceed to the collection of relevant data.

Data science experts know where to get the data from and where to find all the data elements. Data scientist use both structured and unstructured data sources like online repositories, logs, cloud and more to obtain the data in a usable format that CSV, XML, JSON, etc.

• Scrubbing or cleaning Data: The scrubbing and cleaning of data take most of the time and it should as its important step to get the only the required data and dump away garbage data. Cleaning data process flushes out the errors; fill in empty spaces or missing values while examining the data.
  
  
• Exploring or Visualizing Data: After the data is cleansed, the data analysis process takes the data science workflow to another level. This step is crucial as it helps figure out different data patterns, values and hidden meanings behind the collected data by utilizing visualization and statistical modeling and graphs.
• Modeling Data: Modeling phase helps figure out the behavior of the data which further leads to predicting the use of data for future business gains. Different algorithms are available to evaluate the data. This stage helps to measure the accuracy and relevance of data.
  
  
• Interpreting Data:  The most important stage of the data science life cycle is presenting the explaining the data collected. Communication is the key here. You want to be able to explain to your team of non-technical and technical members the meaning behind the data, the idea behind the data, how to use it for better business gains and to overcome future challenges.

Now that you have a basic know-how of the data scientist workflow, there are certain skill set required to perform the work.

Must have “Data Scientist” Skills

Data scientists must be curious with the necessary knowledge of different technologies and excellent communication skills that will enable them to adequately explain their findings and result with great technical expertise. They have a stronghold on the following technical tools and practical skills: