Why a Data Science Career Is Worth Pursuing

Definitely !!!!! Yes. If anything is expanding today it is the data science field and the career opportunities it offers. The appeal for the best data scientists is high and the supply is inadequate. The industry needs data scientists that would define a new picture for business development, so if you are a computer science student or an aspiring data engineer, choosing data science as a career option can only prove beneficial.

Upgrade your knowledge with the amazing data-driven facts:

• Every day, we create roughly 2.5 Quintilian bytes of data. With the growing popularity of IoT (Internet of Things), this data creation rate will only increase.
• How Much Data Is on the Internet + More Stats
• By 2025, the amount of data generated each day is expected to reach 463 exabytes globally.
• Google, Facebook, Microsoft, and Amazon store at least 1,200 petabytes of information.
• The world spends almost $1 million per minute on commodities on the Internet.
• Electronic Arts process roughly 50 terabytes of data every day.
• By 2025, there would be 75 billion Internet-of-Things (IoT) devices in the world
• By 2030, nine out of every ten people aged six and above would be digitally active.
• In this data-driven world of opportunities, every company is looking for a data specialist who can handle big data and come up with reliable and profitable solutions.

Data science expertise like data mining, artificial intelligence, data analytics, big data, machine learning, and deep learning is only going to grow in the coming time. The demand for data science expertise is high and valuable, for at least a few decades now. So choosing a data science career will be the right decision for a shining career ahead.
Let us explore the endless career opportunities that data science has to offer and understand why should data science career is worth pursuing.

What is Data Science?

Data science is an integrative field that combines techniques from statistics, computer science, and machine learning to make sense of data. It is the process of withdrawing specific vision from data. It involves using scientific methods, algorithms, and techniques to analyze data and examine it for hidden patterns, and trends. The extraction from data science can then be used for decision-making, driving business value, and solving significant problems. 
Understand the Fundamental Components of Data Science
The fundamental components of data science are:

Big Data: A technology that manages, creates, and store a large volume of data. It recognizes the huge explosion of data and manages it into a useful set of information. It is widely used in various fields like agriculture, medicine, statistical structures, geographical protection, etc.

Data Mining: Data mining is the process of discovering hidden patterns in organized data and disclosing extracted information from it. It is focused on evoking useful information from large data sets.

Data Analytics: Data analytics is a procedure that follows data mining. Data analytics studies the raw data and withdraws useful information from it to derive meaningful business solutions. It examines the data trends and extracts useful information from them.

Data Analysis:  Data Analysis is a method of squeezing out the insights from raw data by formulating hypotheses and demonstrating them with statistical tests.

Data Science: It defines the process of comprehending a business challenge to provide a solution.

Machine Learning: It is a data analytics tool that is used to predict or find a hidden piece of information in data. It is a computer science or artificial intelligence-related technology that helps to use algorithms or data to perform the required functions with accuracy.

Predictive Modeling: Predictive Modeling is a statistical technique that is used to build models that can make predictions about future events.

Why Choose Data Science as a Career?

This is an interesting question that has an equally interesting answer. Look around and think, about any other career you choose for instance a lawyer, doctor, security officer, technical engineer, etc all these career options allow you to operate one or two variations in the job. There are limitations in the field, it has defined job tasks that you perform. For example, if you are into the technical stream you may know a few software or hardware peculiarities but you may not know the sales or production department details. Which defines limited variations for you to grow in your career.
Whereas if you choose data science as your career option it will offer you multiple career variations with different tasks to perform. Data science is becoming a very important aspect of business be it any field like business, development, commerce, healthcare, sports, entertainment, logistics, technology, security, innovation, etc. Data science is the solution for every business and development sector.
It is believed that data science is only prominent in technology or innovative field, but that's not true data science has huge scope in other sectors as well. Data science, artificial intelligence, and machine learning have sophisticated overtaken many tedious work methods in various fields and lines of work.
The advanced solutions of data science have therefore increased its demand and highlighted the focus of smart performances in various professions by seizing its advancement. 
If you have a good interest in computer science, mathematics, engineering, and statistics and you are passionate about innovative smart business solutions, data science is just the right field for you. It will give you multiple career options. 
Know more about the data science career.

What are Data Science Career tips?  

For a successful data science career, know these important career tips. It will help you shape your career with a spark of confidence. A strong educational background in deep data science learning is the most essential skill required for a good data scientist. High qualifications like the degree in master's or Ph.Ds are the looked-up options in the data science field. Your excellent parallel high-end management and performance skills in statistics, mathematics, and programming will pull you toward the brightest data science career opportunities.
Data scientists should have sharp programming skills with a strong command of technical and analytical skills.
Well, it is observed that due to wide variations in the data science career options. To have specialization in one subject is not viable, you must be prominent in managing and analyzing raw data into meaningful data with disciplinary business solutions.
Following are fields in which you must be master and very proficient.

Mathematics & Statistics: 

To create high-end statistical data reports, you must be very comfortable with statistics, probability, mathematics, and algorithms.

Programming

You must be well-versed and efficient in programming languages like python, R, and SQL. These are some of the most common data science programming languages. It is the basic and very important aspect of data science, you must be very professional in bringing out the best solutions through these programming languages, no matter what stream you choose in data science.

Communication skills

When you are handling an enormous amount of data and converting it into meaningful information it is very important to pass on this information to a related source with an easy and understandable methodology. If you are unable to process the information on which you have worked so hard it becomes pointless if the information is not rightly passed on ahead in the related department. Hence smooth communication skills are a must for a professional data scientist.
The above-mentioned tips will help you take the right step toward achieving your career goals. There is wide scope in the data science career option. Data is the new crude in the field of the fine career extraction process. This field requires highly qualified professionals and options are immense, you have to choose which area you want to process with. The world is flooded with artificial intelligence and advanced technologies. Pick up your interesting data science stream and you are good to go, make a mark for yourself.
Now understand and check out a quick guideline on how to start a career in data science.

How to start a career in data science? 

Understand which is your area of interest. Which data science job do you want to choose?
What does the data team do? Well, the purpose of the data team is to provide insightful data solutions for the company's growth.
Below we will specify how you can achieve the goal of choosing a career in data science and how you can implement the steps.
Create reports and dashboards that provide easy vision to the management to understand the complex project details. The pictorial representation will make it simple for the higher management to make well-informed business decisions.
Reduce the processing time with accurate information that will help the client choose the company product or services.
Rectify the business patterns and client requirements by studying the business market.
Make important data-driven decisions that will enhance the company's development and add value to the business.

1. Practice programming

In today's fast-paced world code less modeling is taking over and many assume that programming is no more required for data processing. The fact remains that coding helps you in every way to define the program and make it accessible and programming language like python is sure a most basic and solid step to go far in the data science field.
Creating new classes, frameworks, and detail coding all these programming aspects sharpens your development skills and makes it easy to understand and resolve the complexities of the data structures, it also helps in program automation which adds value to your product or service design and application.
So it is advisable to say yes, to programming and learn the languages before going ahead in the deep data science world of possibilities.

2. Explore the data science possibilities

Just choose what career in data science you want to pursue. A career option is a data scientist, a data analyst, or a data engineer.

3. Create an impressive portfolio

Make a mark, and stand out in the crowd. So to begin with make an interesting portfolio that sets you apart from the rest. Do your research pick up live projects with complexities, and resolve the issues. Involve in the real case scenario and put down all the solutions and experiences with detailed descriptions. Create study groups and do combine project solving with a competitive approach, this will add more value to your experience and will add up more confidence to solve practical data science issues. 

What are the career opportunities in data science? 

The U.S. Bureau of Labor Statistics predicts employment in data science will grow by 31 percent in the coming decade, which means employers will create more than 10,000 new jobs for data scientists. They predict data science will see more growth than almost any other field between now and 2029. 
Data Science is ranked as the fastest growing globally by LinkedIn's Emerging Jobs Report. The market is predicted to grow to $230. 80 billion by 2026 from $37.9 billion in 2019.

Data scientist

Sharp programming knowledge of Python, R, SQL, and SAS is a must. Also, data visualization expertise like creating charts and graphs with tools like Tableau, Power BI, and Excel are essential skill sets. Knowledge of machine learning and big data is also essential. Good understanding of machine learning methods like Neighbors, Naive Bayes, SVM, and Decision Forests.

Full Stack Developer

The job role of Full stack developer: 
A full-stack developer has a great understanding of the front-end and the back-end programming of web applications. 
Let’s understand exactly the back-end and front-end terms in the web application.

Big Data Engineer

A big data engineer is a professional who is assigned to handle the development, analyzation, and maintainance of data for companies.
Education required: bachelor’s degree in Computer Science, maths, or engineering.
Skills required for the Big Data Engineer
 Expertise in coding, programming, algorithm, and logic development.  Knowledge of scrapping, APIs, and proficiency with Hadoop v2, and HDFS.
SQL, dimensional modeling. 
Efficient in structured RDBMS, spreadsheets, semi-structured XML, JSON, etc, and unstructured text, audio, and video data sources. 

The Data Architect

Well, with rising of big data, the role of the data architect is becoming increasingly important. They draw up plans for data management systems that will integrate, centralize, safeguard, and preserve data sources. The data architect is an expert in technologies such as Hive, Pig, and Spark and must keep up with every new development in the field.

The Data Statistician

Using statistical theories and procedures, the statistician collects, analyzes, and comprehends qualitative and quantitative data.