Data Science: Overview, Benefits, and Challenges

Data Science takes up to be one of the topmost promising and in-demand career paths for skilled professionals and data science enthusiasts. Today, Data Science has become an essential part of the majority of IT companies and many industries due to the given amount of data produced every day. As the popularity of Data Science has grown in recent years, companies have started implementing Data Science techniques to grow their business, meet customers’ expectations, and understand future business. Data Science is the future, so it is important to understand what Data Science is its benefits, and its challenges.

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What is Data Science?

In simple words, Data Science is a skill of analyzing huge data, programming skills, data mining, and data analysis. It is a domain of studying a vast amount of raw data using modern tools and techniques to identify a hidden pattern, derive meaningful information, and make business decisions for business growth.

Now that you know what Data Science is, let’s see the benefits of Data Science.

Benefits of Data Science

In today’s world, data is being generated at a speed that we never expected. Be it from social media platforms, companies, industries, or even our mobile phones. Every day, we generate 2.5 quintillion bytes of data, and because of the rapid growth of the data, the demand for Data Science has seen a growth which has led to multiple benefits of Data Science, to name a few:

  • Increases Business Benefits – With the help of Data Scientists, companies know how, when, and which products are best selling. Hence, the stakeholders can imply faster and more efficient decisions that will positively impact the future of business.
  • Real-time Intelligence – With the help of RPA developers, Data Scientists can identify the different sources of data available for the business; hence they can create automated dashboards that will enable to identify the source quickly.
  • Data Security – Data Science is widely used in the banking and financial sectors for fraud detection.
  • Better Data Stats – Data Science helps professionals to analyze the vast amount of statistical, raw, geospatial, temporal data to draw meaningful information.
  • Decision-making Process – In reality, Data Science has improved the decision-making process due to the tools and dashboards created to view the data in real-time. As a result, it has provided more agility to the stakeholders in making their decision.

In this digital age, Data Science with Python is the solution for your company to become more efficient by understanding user behavior and implying decisions for your business.

In simple words, Data Science can be used for various purposes, including:

  • Targeting the right customers
  • Improving the quality of products
  • Understanding customer behavior
  • Making the team more effective

Now, let’s look at some of the challenges data science professionals face.

Challenges of Data Science

As Data Science is helpful in many ways, there are challenges faced by Data Science professionals. These challenges may vary from individual expertise, company, or even industry. So, let’s have a look at some of the common challenges that you may face in your career as a Data Science professional.

  • Problem Statement Undefined – This may appear when one or more Data Scientists are at fault. One should study the business problem for which you want to implement the strategy. Opting for an approach, identifying datasets, and performing analysis may lead to a wrong decision.
  • Data Security – Data security remains one of the topmost issues in Data Science. It is a concern of all the businesses across the world. A few of the data security breach involves:
    • Ransomware
    • Thefts
    • Attack on systems
    • Confidentiality
  • Overfitting – It occurs when a Data Science model is built, which learns the demo or training data well, absorbs detailed information about training data but has a problem in learning the new, real raw data. The goal of a model is to perform well on unseen raw data.
  • Multiple Data Sources – As we know, Data Science deals with Big Data which are usually heterogeneous in nature, i.e., these data are a mix of structured, unstructured, semi-structured formats. Nowadays, companies use various software and tools to gather data from different sources, making it difficult for Data Scientists to understand and derive meaningful information.
  • Getting Value out of Data Science – Data Science professionals believe that the data analytics process needs to be more agile with the business during the decision-making process to support a business. It is important to take the right approach and strategy to derive a proper value from any given set of raw uncultured data.

Data Science Career and Salary Outlook

We already know the fact that a Data Science career is on the rise, promising, and in demand. Hence, Data Science professionals are rewarded for their skills with competitive salaries and better job opportunities in big and small companies across all industries.

Data Scientist is a dream job for many IT professionals, and the number of jobs requiring Data Science skills is rapidly growing and is expected to grow by 27.9% by 2026.

Talking about the salary, it varies based on several factors like location, company, industry, and experience. Below are some of the salary stats of a Data Science professional:

  • Data Analyst – $62,541
  • Data Scientist – $97,004
  • Senior Data Scientist – $127,525
  • Data Engineer – $92,999

The salary stats are from Payscale, based on 1000 salaries mentioned by individuals. I believe that the salary depends upon how skillful you are, and the experience & expertise you bring to the table.

In the digital era and rapid increase of data every day, it is necessary to move forward with the changing market needs and adapt to Data Science. Data Science has its benefits and challenges, and it is not wrong to say that the future belongs to Data Scientists. More and more data will provide opportunities to drive key business decisions, and Data Science professionals should be highly skilled to solve the most complex problems.


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