What is Data Science? Requirements, Scope, and Implementation

In today’s world which is dominated by technology, data is considered the new gold. Now imagine the benefits a data scientist can get! You might wonder what Data Science is and what is actually studied under this branch of technology?

As the name suggests, Data Science refers to the field of study that is closely related to the collection and analysis of data in order to make fruitful business decisions.

Data Science has managed to create a prominent space in many leading industries in the world. Every day more and more companies are switching to Data Science tools and techniques in order to flourish in their business and reach new heights of success.

What is Data Science?

Data Science is a branch of technology that majorly deals with analyzing vast amounts of data by making use of complex tools and techniques to unearth hidden patterns and make informed decisions that yield maximum success. Modern machine learning algorithms and technology are used in this field to develop predictive patterns that help in making sane decisions. The data can be gathered through various platforms and mediums for analysis.

5 stages of Data Science

Now that you have a clear understanding of what is Data Science, let’s move on to discussing the steps involved in Data Science, along with its significance.

  1. Data Collection
  2. Data Maintenance
  3. Data Processing
  4. Data Analysis
  5. Data Implementation 

Data Collection

Data Science is basically a 5-stage process and the first step is data collection. This stage of Data Science deals with acquiring raw and unrefined data which is set to be analysed.

Data Maintenance

This stage of Data Science involves taking raw data and refining it into a form that can be used easily. Data Warehousing, Data Cleansing, and Data Staging are all a major part of this stage.

Data Processing

Data Mining, Clustering/Classification, Data Modelling and Data Summarization are the four main aspects of data processing. During this stage, a data scientist examines a structured dataset. This examination takes place in order for them to determine how useful it will be when predicting future outcomes.

Data Analysis

This is the fundamental stage of Data Science. Here, a data scientist works to analyse every segment of data collected through various platforms and mediums.

Data Implementation 

The very last step involved in Data Science is data communication and implementation. Here data scientists work to transform useful feedback from refined forms of data into actual measures. Moreover, data is converted into easily readable graphs and charts that can come in handy while making decisions.

What does a Data Scientist do?

The primary duty of a data scientist is to extract valuable insight by analyzing business data. In simpler words, a data scientist finds solutions for business-related problems through a series of steps that are as below:

  1. Prior to data collection and analysis, the data scientist first determines what problem they want to solve by asking accurate questions.
  2. The data scientist determines the correct variables and sets of data.
  3. The data scientist gathers structured and unstructured data from many different sources which include enterprise data, public data sets, etc.
  4. After sufficient valuable data is gathered, the data scientist refines the raw data into a form that is fit for analysis.
  5. The refined data is then fed into machine learning algorithms and innovative technology after which the data scientist identifies data patterns.
  6. Lastly, the data scientist makes use of his skills and expertise to make interpretations of data through various data sets for gaining meaningful insights, helping in decision making.

What are the prerequisites for becoming a Data Scientist?

You can not simply dive into Data Science. Before you step into this field there is a certain criteria you need to meet. We have listed down the prerequisites for becoming a data scientist.

Machine Learning

Machine learning is that essential skill on which the entire structure of Data Science stands. Hence, in order to become a professional data scientist you need to have a solid understanding of machine learning fundamentals.


Knowledge of programming to a certain extent is required as well when you want to become a data scientist. Python and R.python are the most common and widely used programming languages as they are comparatively easier to learn and the least complex.


Statistics, meanwhile, is considered the core of Data Science. One who lacks understanding of statistics can never excel in this field of Data Science. A tight grasp on concepts of statistics is necessary for interpreting data in order to extract meaningful insight.

Why choose a Data Science career path?

Now that you are well aware of what Data Science is and what your role as a data scientist is, you might wonder why you should choose a Data Science career path and what scope it holds in the future? We will answer all your queries in detail, but first, let’s start by telling you that the job market for Data Science is expected to reach 178 billion US dollars by 2025.

Choosing Data Science as a career path is absolutely worth it because there is a continuous rise in demand for data scientists in leading industries across the world. In addition to that, Data Science is among the top 15 in-demand jobs, according to LinkedIn.

Apart from that, Data Science stands on the third position in the list of best jobs with a median salary range of $120,000/year in America, as released by Glassdoor. On the other hand, you can expect to start your career as a Data Scientist in South Africa with approx 545,547 (ZAR)/year which is equal to 36,000 (USD)

Hence, if you are passionate about your career growth and want to build a massive career in a tremendously growing field of IT, think no more and dive right into Data Science after acquiring the necessary skill set.

How to become a Data scientist?

No amount of words can describe the sky-rocketing demand for data scientists in nearly every industry right now. Moreover, post-pandemic, a sharp rise in the demand for data scientists has been observed. In case you look forward to changing your fate by becoming a data scientist, you can always take up free online Data Science courses or Nanodegree Programmes. Apart from this, you can look up various scholarship programs that offer Data Science courses, according to certain criteria.


Data Science has the potential to become the backbone of all the leading industries of the world. This is why now is the best time to choose the field as your career. Everyone is well aware of the fact that data is invaluable and the wonders one can do once able to extract useful insights from a data set, whether raw or refined, that is collected through various channels.

Through Data Science companies can easily foresee their future growth prospects and how their business will look like if they carry out certain approaches. This makes it the best possible time for you to kickstart your career in Data Science with the Udacity Data Science Nanodegree Programme!


Manno Notermans

Related Post