Is Data Science a Good Career ?

11 mins

Data science is one of the most spectacular career options these days. It has become really popular in the past decade and is expected to account for about 6% of all jobs by 2023.

Also, Data science is a career that is likely to grow by 171% in 5 years as per the Global Industry Analysts. Companies like Uber, Amazon, Microsoft, Apple, and IBM hire data scientists rapidly. Other significant employers include insurance agencies, government agencies, and financial firms, apart from tech companies.

But you're here because you are amongst the smarter bunch, right?

So you don't want to jump into something that might be unsuitable or unfruitful for you.

Don't worry we've got you covered. This blog post will talk about all the pros, cons, and suitability of a career in data science.


Why is Data Science such a Growing field?

 

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A Data Scientist is defined as someone who can "think like a computer scientist, but act like a statistician." In other words, they've mastered the art of extracting meaning from data and can solve real-world problems in an industry or business setting.

The key to success as a Data Scientist is converting unstructured data into structured & meaningful data that can then be analyzed and used to solve practical problems.


Today's world is highly data-oriented.

We are living in the Information Age, and with the massive amounts of data available today, there is a huge demand for professionals who have the skills to analyze this data and extract meaningful information.

That's where a career in Data Science comes into play. But ironic to this, Data science is a relatively new discipline at the intersection of many different fields. The term "data science" was first used in a report issued by the White House in September 2011, and it has been gaining popularity ever since.

The term data scientist was coined around the same time as "data science," but it wasn't until recently that the idea of data scientist as a job title became common in the mainstream press.


So if you add up everything, I think it becomes pretty clear why data science is prospering so rapidly.

And I can assure you that this growth is going to continue for a long time because data will only increase with time, and so will the competition between the companies who analyze that data faster and more accurately than the other.


And don't worry if you're not sure where to get started, many universities offer programs in Data Science and Big Data Analysis, making it easier than ever before to become part of this growing field!


Skills required to be a Data Scientist


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According to a global poll, data science's expanding popularity would result in 11.6 million employment openings by 2026.


But how does one go about becoming a data scientist?
What abilities do you require?
And how do you get those abilities?


Here are the core skills required to be a successful data scientist:

    • Data analysis:

      You've got data, now what do you do with it? Analyzing large sets of information can be difficult, especially when many different variables are at play.

    • Modeling:

      Once you've analyzed your data, you'll probably want to create models based on those findings. This involves making mathematical representations of real-world phenomena to be used for forecasting or predictive analysis.

 

    • Machine learning:

      Data scientists use machine learning algorithms to predict outcomes based on previous data sets. They also use these algorithms when building models and classifications to analyze unstructured data such as text or images.

      A background in computer science or engineering will help your understanding of these concepts but isn't necessary if you have some programming experience under your belt already.

 

    • Statistics:

      Data scientists need to be able to conduct statistical analysis on large datasets to identify trends and patterns.

      This involves setting up experiments and analyzing results. Some basic knowledge of statistics or calculus would be required.

 

    • Data mining:


      Data mining is used to discover patterns in large datasets using analytical tools such as clustering or classification models like decision trees or neural networks. It's applied in many fields, including science, medicine, and business analytics.

 

    • SQL and database design:


      data scientist coding the sql codes
      You need to know how to query databases effectively, organize and structure your data, and design efficient database architecture. SQL (Structured Query Language) is one of the most important skills for any aspiring data scientist.

      It's a declarative language for managing data stored in relational database management systems (RDBMS). It's also one of the easiest programming languages to learn — many online tutorials provide step-by-step instructions on how to write basic queries using SQL Server or Oracle as examples.

 

    • Data Wrangling and Munging:

      These are two skills that allow you to manipulate your data into the desired format.

      This is important because data is rarely in the correct format for analysis, so you have to clean it up before you can analyze it.

      For example, if you wanted to find out what size shoes each person at an event wears, you would have to enter all of their shoe sizes into a spreadsheet one by one.

      This would be very time-consuming if there were many people at this event!

      So instead, data scientists use data wrangling tools like Pandas(Python Library) which allows them to use functions like 'filling()' and 'drop' 'duplicates()' in their dataset so that all of the inputs are automatically done for them by these computer programs.
    • Business knowledge:

      A good data analyst should understand business concepts and be able to apply them on the job. This will help him/her understand how data can be used to improve various aspects of the business, including marketing, sales, finance, etc.
    • Communication Skills:

      A good data analyst must have excellent communication skills to explain his findings clearly to his clients or colleagues without losing interest or confusing them with technical jargon.

      He should also be able to communicate with people from other departments within the organization who may need his help in better understanding certain aspects of their jobs or improving their performance at work.
    • A Master's Degree:

      A master's degree in data science can give you a significant edge over your competitors in the field. A degree from any reputed educational institute has a high value in the data science market.

      Apart from this, a master's degree in the domain is also going to provide you with extensive knowledge in the field and make you better versed in understanding big data.

 

Top 6 Job Profiles in Data Science


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1. Data Scientist:

 

A data scientist is a person who works with data, analyzing it and interpreting it to make decisions based on it. The role involves collecting, cleaning, and preparing data so that it can be analyzed by computer algorithms.

A data scientist also uses statistical techniques like regression analysis or machine learning techniques to come up with solutions for problems faced by businesses or organizations.

Data scientists work closely with managers and business analysts to understand what kind of information they need and how they want it analyzed so that they can make better decisions based on them.

2. Data Engineer:


The role of a data engineer is similar to that of a software engineer who builds software programs using programming languages like Java or Ruby on Rails. Their main task is to ensure that all data flows smoothly through the system without any errors or gaps. This requires them to have sound knowledge about IT systems and tools used for storing large amounts of data like cloud storage systems, big data frameworks, etc

3. Data architects:



Data Architect is the person who finds out the ways to analyze the data and gather information from it.

The job of a Data Architect is to make sure that the company can use its data effectively, promoting leaner processes and better decision-making, among others.

As a Data Architect, you will be under a little pressure in this job role because of your commitment to delivering quality results. You need to have good programming skills and knowledge about Software Development Life Cycle (SDLC).

Your ability to work with different teams like Business Intelligence, Information Technology, or Engineering is another necessary skill for you because these people will help you in achieving your goals.

Data Architects are in high demand right now, with a global shortage of talent.

The money being poured into the industry is immense. And the thing that sets data architects with a good master's degree apart from everyone else is that they have experience working with large data sets.

Data Architecture jobs are going to continue to increase over the next decade, and those who can offer their services with excellence will be able to make very good money doing it.

 

4. Business intelligence developers:


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Financial institutions engage business intelligence (BI) engineers to provide analytical tools. They provide new business intelligence tools and apps to assist customers in learning how to use firm goods.

They also devise data-savvy ways to boost the company's analytical procedures and methodologies.
You have to be creative while working as a business intelligence developer as it requires coming up with innovative ideas and solutions regularly.

 

5. Machine Learning Engineer:


Machine learning engineers work on building algorithms that can learn from experience and apply this knowledge to new situations. These algorithms have many applications but they're mostly used in areas like fraud detection or targeted advertising where they need to identify patterns in large amounts of data without being explicitly programmed with rules about how these patterns should look beforehand.

 

Salary in Data Science


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Data science is a vast field, and there are many different types of careers that you can pursue. The salary in the field of data science varies widely depending on your role and experience.


Data scientist salaries range from $30K to $150K, with an average salary of $111K per year. This is a relatively new field, so the pay is still not as high as some other career paths.

However, it is expected to grow significantly in the next few years as more companies develop data science teams and more people enter the field.


Data Scientists earn an average salary of $111K per year.


The highest paying industries for Data Scientists are Finance and Banking at an average annual salary of $148K and Consulting at $140K. The lowest paying industry for Data Scientists is Computer Software at an average annual salary of $93K.


Salaries in Data Science vary in many different aspects. Let's take a look at what those aspects are:

1. Education:


The greatest pay does not go to individuals with PhDs or master's degrees, however, it does provide a good start for entry-level data scientists. In the longer run, your skill-set and experience are going t help you get higher pay but for an entry-level data scientist, having a master's degree or a Ph.D. will surely help in giving a head start to your career.

 

2. Niche:


Your salary in the data science field is also going to depend on what niche you pick to master. Every position has a different set of responsibilities and hence their salary is also different. Although no niche is inferior to the others still they can't have equal pay.

One of the main reasons for this is the demand for each role in the global market, some of them are frighteningly in demand while others are not that popular.

Some of the most popular niches are:- data Scientist, Data Analyst, Data Engineer, Data Architect, Machine Learning Engineer, etc.

 

3. Industry:



The organization that is hiring you belongs to which industry also lays a huge impact on your pay. The best-paying industry sectors for a data scientist are finance, banking, and E-commerce.

 

4. Experience:

 

The most trusted aspect when hiring, in any industry, is the experience a candidate holds in a certain field. Having experience as a data scientist is a very strong factor in determining your income.

The median salary increases with experience — it jumps from $80K at five years' experience to $110K at 10 years of experience, according to Payscale.com.

 

Challenges faced in Data Science Industry

 

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    • Meeting high expectations is one of the key challenges faced by data scientists today. There is an enormous amount of data present in today's world and every industry is hiring a data scientist to make sense of that data. But it is not at all easy to do that, especially if you are new to working as a data scientist. But you can't lack results when working in the data field due to the fierce competition of the modern world.

 

    • It's difficult to develop precision in such a big field, and it takes a lot of effort and dedication. The job of a data scientist is determined by the type of organization, procedure, and industry. Some have referred to it as the fourth paradigm of science, while others have referred to it as a basic 2.0 version of Statistics.

 

    • Data is the key source of income for many enterprises and corporations. Data scientists will assist companies in making data-driven choices. The process's specifics may also intrude on the client's privacy. Client information can be leaked and made available to anybody at any moment. The issue of data protection is a key source of concern for businesses. As a result, data privacy appears to be a key issue that makes data scientists nervous and hesitant.


Conclusion


I hope you now have a better understanding of whether Data Science is a smart career choice or not. And this blog will assist you in making a well-informed decision that will protect your future. Be sure to hit the comment section for any further queries.

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