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Data Science: The Path To Understand Where We Are And Will Go

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By: Divya Bora

August 19, 2021

data science header ## WHAT IS DATA SCIENCE? According to Wikipedia, Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains. It relates to data mining, machine learning, and big data.


The main phases of the it's lifecycle are: data science lifecycle

Phase 1: Discovery Before starting the project, it is essential to understand the various requirements, specifications, priorities, and required budgets. The data scientist at work must ask real questions that will help assess the given project resources in terms of people, technology, time, and data. They are responsible for framing the business problem and formulate initial hypotheses to test in this phase.

Phase 2: Data Preparation This phase requires analytical sandboxing for performing analytics during the project. Professionals should explore, preprocess and condition the provided data before they model it. The ETLT(extract, transform, load, transform) process gets the data into the sandbox. A language like R could clean, transform, and visualize the data. It also helps to establish a relationship between the variables. After the data has been cleaned and prepared, it is ready for exploratory analytics. data preparation analytics

Phase 3: Model Planning This phase requires them to determine the methods and techniques to establish relationships between variables. These relationships are essential as they set the base for the algorithms implemented in the next phase. Exploratory Data Analytics(EDA) will be applied using various statistical formulae and visualization tools. Some familiar tools used for model planning are:

1. R: It provides a suitable environment for building interpretive models and has complete modeling capabilities.

2. SQL Analysis services: It can perform g in-database analytics using typical data mining functions and basic predictive models.

3. SAS/ACCESS: It creates repeatable and reusable model flow diagrams and can also access data from Hadoop.

Phase 4: Model Building Datasets for training and testing purposes are the result of this phase. Here the scientist needs to decide whether the existing tools are sufficient for running the model or require a faster environment with parallel processing. The given business problem could adopt Various learning techniques. Shared model building tools are SAS Enterprise Miner, WEKA, SPCS Modeler, Matlab Alpine Miner, and Statistica.

Phase 5: Operationalize This phase consists of the delivery of briefings, final reports, code, and technical documents. Some pilot projects need to be implemented in a real-time production environment to test the model. This experience provides valuable information about the performance and other project-related constraints on a small scale before full deployment.

Phase 6: Communicate Results Here the data scientist must identify all the key findings, determine if the results are successful or not, and communicate the impact to the stakeholders.


Some pros are:

1. Is in Demand. It is one of the careers that has increased demand, and prospective job seekers will have ample opportunities. It is a highly employable job sector, and the prediction estimates the creation of11.5 million jobs by 2026. It is a promising career for the future as the rate of data generation is high, and many companies and organizations will require a data scientist to handle its data.

2. The Abundance of positions. It is less saturated than other IT sectors as very few people have the required skills to become data scientists. Data science is a field that is high in demand but low in supply.

3. Highly-paid career. The career is one of the most highly paid jobs and a highly lucrative career option. According to Glassdoor, a Data scientist makes on average $116,000 per year, making it a well-paid job.

4. Is versatile. Data Science has numerous applications like healthcare, consultancy services, banking, transport, and e-commerce industries. It will offer the person the opportunity to work in various fields.

5. Makes data better. Professionals work for companies to process and analyze their data. They don't just study the data but also refine the quality of the data by breaking down the information and examining it to find out the nature of the information. Data science enriches the data and improves it.

6. Data scientists are highly prestigious. They hold a vital position in a company as they enable their company to make smarter decisions. They are relied upon to use their expertise and skills to bring forth better results to their clients.

7. No boring tasks. Historical data trains the machines to perform repetitive tasks by companies, which has led to the simplification of arduous tasks done by humans before.

8. It makes products smarter. It involves machine learning which has enabled industries to create better products tailored specifically for customer experience. Recommendation Systems used by e-commerce websites provide personalized insights to users based on their previous purchases.

9. Save lives. Data science has immensely improved the Healthcare sector as machine learning has made it easier to detect early-stage tumors. Many healthcare facilities have been using data science to help their clients. For example, To fight diseases like cancer, data is essential in the discovery of a cure.

10. Transform a person into a better person. It is responsible for a great career and helps personal growth as the person develops a problem-solving attitude. Data scientists have a secure future due to growing technology, and also they have the best of both the IT and Management worlds.

Some cons of Data Science are as follows:

1. Data Security & Privacy. In most industries, data is their core component. Data scientists make data-driven decisions that may lead to a rise in their company's productivity and revenue. Since an individual's data is accessible to the company, competitors can leak and misuse it.

2. Complexity. Data science techniques and tools cost a lot to an organization and are complex tools. They require expert knowledge to make use of them for work. Selection of the right tool based on specific requirements is also a difficult task. It requires a proper understanding of tools and accuracy in analyzing the data and extracting information.

3. Term is misleading It is a more general term and doesn't have a specific definition for it. Data science is more of a business than science and includes data preparation, analysis, and management.

4. It Does not allow expertise. The professionals must have various skills like machine learning, programming, statistics, and business strategies. Still, they won't be able to deep dive in any particular field as it is a mixture of various areas, so it is difficult to master each area and be equally proficient in all of them. Data science is a dynamic field that requires the person to keep learning various skills for it.

5. Arbitrary data may yield unexpected results. Data scientist performs data analysis and then makes cautious predictions to facilitate the decision-making process. When the data provided is arbitrary, it does not yield the expected results, and this is due to weak management or poor resource utilization done by the data scientist.


The scope of data science has been tremendously growing with every passing year as people across the globe have stepped into the digitalization age. Industries with the future field in it are:

  • Healthcare

Healthcare has one of the most prominent scopes for Data Science as a large number of patient datasets can be used to build a specific approach to identify any disease at its early stages. Professionals can provide immediate help to patients by combining their medical expertise with data science.

  • IT

The IT sector has shown enormous growth in the world's GDP, and Data Science has become a necessary aspect of any successful data-driven company. It has assisted companies in identifying the impact of the new changes that they make.

  • Automobile industry

The Automobile industry can act as the new powerhouse to opportunities in it. Future developments like autopilot flying cars, fixed destination cabs, automatic public transport, and more will require passionate people to code and consider the added advantages.

  • Banking & Finance

It manages money investment in the banking and finance sector based on data predictions for best results. It identifies and groups similar groups of people. It also provides security from fraudulent activities taking place online.

  • Power & Energy

Data Science predicts the effects of Nuclear energy sources and indicates the maximum safest potential to be utilized. It builds AI bots that can handle enormous power sources.

Intro to Data Science is a course specifically designed to help with the basics of Data Science. For hands-on training, Data Analysis will be a perfect start for beginners, and intermediate learners can go ahead with Data Analysis with Python.


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