Data science is one of the fastest-growing jobs in the USA.
According to the Bureau of Labor Statistics, computer and information research jobs will increase by 22% from 2020 to 2030, which is higher than any other job field.
With businesses trying to take advantage of big data, a significant amount of information has to be sorted, analyzed, and applied in a wide variety of industries.
How can businesses handle so much data at a time? How can the government use patterns to create meaningful analysis from random data?
They need data scientists.
Data scientists are highly skilled in gathering, organizing, and analyzing data and identifying meaningful patterns to help businesses create data-driven decisions.
Interested in how to become a data scientist?
In this blog, we will cover:
- Who is a data scientist?
- What does a data scientist do?
- Difference between data analysis and data science?
- Salary and Job outlook of data science?
- Step by Step process to become a data scientist?
- What are the pros and cons of becoming a data scientist?
- Can you become a data scientist without a degree?
- What are some data science career opportunities you can look for?
- A list of Frequently Asked Questions
Who is a Data Scientist?
A data scientist collects data and analyzes them to identify patterns to understand the trend as part of reaching a conclusion.
They are also proficient in data visualization to present the data in a visually appealing way so everyone can understand it.
Data scientists often take advantage of complex algorithms to identify patterns and assist businesses in making data-driven decisions.
Data scientists primarily work in big data and artificial intelligence companies as these companies work with the maximum amount of data. However, you don’t need to be an AI or big data expert to work in data science.
Data scientists usually work adjacent with data analysts, data engineers, business analysts, and business architects to fulfill the organizational goals.
Also Read: What is Data Scientist in 2022?
Real World Application of Data Science
Here’s how data science works in the real world. Let’s say a food service company wants to see how many customers are most likely to switch to their services. Then the company could ask the data scientist to sort data from different sources and create an algorithm that may predict the number of customers that may switch to their services.
The company then can change its offerings or bandwidth to improve customer experience and increase business.
Unicorn companies like Netflix, Google uses data science all the time to track users’ actions and give you the perfect suggestions according to your taste and requirements. Amazon also uses it to give you similar recommendations based on your search history.
What Does a Data Scientist Do?
Here are a few responsibilities of a data scientist :
- Identifying trends by running complex algorithms to uncover hidden insights
- Perform data analysis to predict future trends and outcomes for assisting with data-driven decision making
- Use data visualization techniques to present the data in an easily digestible way
- Deploy tools such as python, R, SAS, and SQL to perform data analysis
- Stay updated on the latest development in data science
Data Scientist Requirements
Here are some data scientist job requirements:
- Past experience as a Data Scientist or Data Analyst
- Proven knowledge in data mining
- Experience with Machine learning, Python, R, C++, and Java
- Skilled in using business intelligence tools such as Tableau and Hadoop
- Knowledge in statistics and algebra
What is the Difference Between a Data Scientist and a Data Analyst?
Data science and data analysis may look like similar jobs, but data scientists and data analysts work with data differently.
Data analysts scrutinize large data sets to identify patterns. After that is done, they use data visualization methods to present the data in an easy way so that people from both technical and non-technical backgrounds can understand it.
Whereas, data scientists use statistics, mathematics, and machine learning techniques to create complex algorithms for interpreting data. They are more into developing advanced data modeling processes using ML, predictive models, and custom analysis.
We can see, both the profiles work with collecting, sorting, and analyzing data. For that reason, their responsibilities often tend to overlap.
What is the Difference Between Data Science and Statistics?
Data science is a comparatively new field, and it is mainly revolved around coding and technical knowledge. It collects data from various sources and uses data analysis and data visualization techniques to extract insights.
Statistics generally used mathematics and probability to derive hypotheses and theories. There is less technology involvement, and It’s much older than data science.
Responsibilities of Data Scientists
- Process, clean, and ensure the integrity of data
- Perform data mining
- Perform analysis by deploying complex algorithms such as Logical Regression, Decision Trees, etc.
- Write scripts to automate the data validation process
- Identify insights from the data to assist in data-driven decision making
Responsibilities of Data Analysts
- Collect and process a large amount of data
- Identify patterns from data sets
- Deploy SQL to perform data query
- Deploy different analytics tools such as predictive analytics, prescriptive analytics, and diagnostic analytics to identify trends
- Use data visualization such as Tableau to present data in an easily understandable way.
Salary and Job Outlook of Data Science?
According to indeed.com, the average salary of a data scientist in the United States is $115,884 per year.
And it’s $113,396, according to Glassdoor in the USA.
The good news is that the demand for data scientists is higher than ever, and it’s still increasing. According to the USA Bureau of Labor Statistics(BLS), the data scientist and statistics jobs are expected to grow by 35% from 2019 to 2029, which is much faster than the average job growth rate in the USA.
Also Read: How much a data scientist makes in 2022?
Step by Step Process to Become a Data Scientist?
There are roughly 5-6 steps you need to follow to become a data scientist.
Get Proper Data Science Education
The minimum required qualification for a data scientist is a bachelor’s degree in any field. However, recruiters prefer to see a data science degree. It helps the recruiter know that you have data science know-how and can tackle the job efficiently. However, it’s not mandatory.
You can get any bachelor’s degree, and along with that, start learning about programming languages, database architectures, SQL/MySQL, etc. These skills will help you build a foundation in data science.
If you’ve already finished your degree, you can get a master’s degree in data science. With a master’s degree course, you can get a detailed understanding of statistics, machine learning, algorithms, and data prediction.
Acquire Relevant Data Science Skills
It’s better to learn about data science skills before getting into the job hunt for a data scientist job. There are multiple online courses you can enroll in to learn about data science-specific skills.
Here are some skills required for a data scientist:
Data scientists should learn about specific programming languages, such as:
One of the essential data visualization skills is presenting data in a visual format so that it is easier to understand.t. Here are some tools you need to learn.
Machine learning is an integral part of a data scientist's job. It helps in continuously improving the quality of pattern recognition and improving prediction accuracy. A course in machine learning will be an introductory step towards the field.
Since data scientists often work with Big Data, employers want candidates to have some knowledge about it. You need to have a basic understanding of big data frameworks such as Hadoop and Apache Spark.
In high-stakes jobs like data science, the ability to share ideas effectively is an essential skill.
Get an Entry-level Job
The easiest way to become a data scientist is to start working as an entry-level data scientist or a Junior Data Scientist, Junior Data Analyst, Business Analyst, etc. As you begin to get exposure, you can work your way up to a senior data scientist role.
Brush up Your Interview Skills
Scoring an interview is only half of the game. You have to prepare for the interview to ace it. A Data scientist interview can be highly technical. That’s why you need to be well-versed with both technical and non-technical questions. The best way to prepare for interviews is to give mock interviews and answer questions aloud.
Here are some common questions candidates are asked in a data science interview:
- What is a linear model, and what are its advantages?
- What do you know about the random forest?
- What tool do you use to find duplicates in data sets?
- Give us an example of a challenge you faced and how did you overcome it?
What are the Pros and Cons of Becoming a Data Scientist?
Becoming a data scientist is great. It is one of the most demanding careers out there. However, like everything in life, data scientist also has some pros and cons.
Here's a list of advantages and disadvantages you want to know:
Advantages of Becoming a Data Scientist
- Data science is interesting and challenging. You will never get bored by solving a complex problem.
- You will get to work on various tasks in different verticles of the organization
- You will see your work bringing significant results in the company's growth
Disadvantages of Becoming a Data Scientist
- You will get to work on the top-level problems- and it will restrict you to go into deeper concepts of a specific subject
- Data science technology is constantly changing. And you need to constantly be on your toes to become a data scientist.
- It is not easy to learn data science. That's why there are not many highly skilled people working in the field.
Data Science Roadmap
Here is a data science roadmap from GeeksforGeeks.
How to Become a Data Scientist Without a Degree?
It is not mandatory to have a data science degree to become a data scientist. But you need to have a bachelor’s degree to get a job as a data scientist.
According to a study conducted by Indeed, maximum data scientists come from totally different educational backgrounds.
Since the requirement for data scientists is much higher than the supply, many professionals have transitioned into data science jobs from other industries without any relevant degree.
The most important thing you need to have to become a data scientist is relevant skills. If you can acquire those skills, you can become a data scientist.
Careers in Data Science
Here is some career you can explore after gaining data science skills apart from data scientist. Also, you can start preparing for data scientist jobs with the data science interview questions
Data engineers work in organizing and assembling large data sets. They also develop internal processes and workflows to optimize the data extraction and loading process. They ate also actively involved in developing data analytics tools by catering to specific data sets.
The average salary of a data engineer in the USA is: $116,781 per year.
Data architects analyze and interpret structural requirements for performing data analysis and developing database solutions.
They also install and configure new systems and help in migrating data from legacy systems to new software.
The average salary of a data architect in the USA is $141,174 per year.
Data analysts collect data from various sources and organize them into the database. They also create complex models to analyze data and identify meaningful trends that help businesses prioritize their growth roadmaps and requirements.
The average salary of a data analyst in the USA is $65,074 per year.
Data administrators assist with designing databases and updating existing databases for improving data analysis efficiency. Apart from that, they also set up and test new databases and data analysis workflows to ensure maximum security and integrity of the database.
The average salary of a database administrator in the USA is $86,630 per year.
A business analyst is responsible for planning and monitoring a company’s business processes and organizing a company’s requirements. They also verify resource requirements and develop cost-estimation models for stakeholders to make data-driven decisions.
The average salary of a business analyst in the USA is $74,642 per year.
Frequently Asked Questions
Here are some questions people ask all the time about data scientists:
Q. Is data science worth pursuing?
A. According to BLS, the data science field will see job growth of 22% from 2019- 2029. IBM projects that by 2020, the number of data scientists will increase by 364000. And the job growth trend is here to stay.
Q. What are the skills you need to become a data scientist?
A. The primary skills you need are :
- Machine learning
Apart from that, you need to have strong interpersonal skills.
Q. How much time does it take to become a data scientist?
A. Data science is an ever-changing field. You need to learn constantly to stay updated. However, to start in that field, you don’t need to spend your time getting a degree in a university for three years. You can start with any job-ready course, which is generally geared towards employability, within one year.
Q. Do you need to learn code to become a data scientist?
A. Yes, you need to learn about Python and R basics to become a data scientist because you will be implementing machine learning algorithms to analyze data.
Q. Is data science hard?
A. Initially, data science may feel challenging. But with proper guidance, it can become an easy skill to learn. However, if you are not from a technical background or don't have a grasp on coding, you may feel lost and lose sight.
Becoming a data scientist is not difficult. You need to learn about relevant skills and follow a structured roadmap to becoming successful at it.
- Here are some key points to keep in mind:
- Get a bachelor’s degree in data science or computer science. Although there is no special degree requirement to become a data scientist, a relevant degree will help your data science resume stand out in front of the recruiters.
- Learn relevant skills such as Python, R, Java, Machine Learning, Tableau, Hadoop, etc.
- Get an entry-level data scientist job to gain experience. It will help you understand the inner workings of the data science field and learn from other professionals.
- Lastly, you need to be consistent in your pursuit. If you stay consistent in your goal to become a data scientist long enough, it’s a matter of time until you reach your goal.
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