Are you good at programming, math, and analyzing large amounts of data and are wondering whether data science is a good field for you?
Or are you someone who doesn’t have a formal degree in computer science, but wants to pursue data science as a career?
Don’t worry, we have you covered!
In this step-by-step guide for newbies, we’ll tell you how to become a data scientist in the easiest way possible.
Modern organizations need data scientists to help complete objectives such as:
- Analyzing large amounts of data to build better policies
- Developing new strategies
- Improving customer service
Tableau, Accenture, Fractal Analytics, Mu Sigma, and iTechAr are among the top ten data science companies that hire data analysts in 2022.
Here are some quick facts about careers in Data Science:
Data science was identified as the skill with the largest skill gap, according to a 2021 report by the World Economic Forum.
According to a report by the Bureau of Labor Statistics (BLS) professions in data science are projected to increase by a whopping 31% from 2020 to 2030 which is 8% higher than the growth rate for other jobs in the same period of time.
Pursuing a career in data science can not only help you earn an average pay of almost $122,000 a year but will also open up a variety of niche fields that you can work in, such as data engineering, machine learning, and data architecture. Since handling and understanding data is key to multiple sectors of the economy, you can find jobs for yourself in several industries including entertainment, software development, and e-commerce.
A data scientist is someone who helps process and analyzes large volumes of structured and unstructured data and determines trends and patterns in the information that they collect. Their insights help companies understand their customers better.
With a good data science and analytics department, companies are able to succeed in the market and expand their businesses.
Data scientists are responsible for handling specific tasks including research, statistical analysis, organizing data sets, creating data visualizations, employing machine learning techniques, and building predictive models, all of which improve the quality of data and help understand trends and patterns.
Data science involves dealing with a lot of data and being able to organize and understand it. So, anyone interested in becoming a data scientist must have excellent analytical skills and a strong command over programming, mathematics – especially statistics – and the handling of big data.
If you are wondering what to study to be a data scientist, you must know that a lot of companies require a Bachelor’s degree in computer science, data science, statistics, or another closely related field to become eligible for the position.
Candidates who have completed a Master’s degree and specialized in the field are given higher credit and are considered for senior posts. In fact, around 75 percent of data scientists have a Master’s degree or a Doctorate.
However, having a degree is not always an absolute requirement.
Allow us to explain:
See, even if you don’t have a formal degree but have hands-on experience with Python programming, R programming, Machine learning, Deep learning, and Data visualization tools, you should have no trouble landing a high-paying role.
If you’re interested in pursuing a career in data science, here are some ways that you can learn how to become a data scientist from scratch:
Step 1: Develop Your Data Science Skills
To further develop your skills and stay up to date with advancements in the field, you can choose affordable and time-saving options such as doing an internship, getting certifications, and attending bootcamps. This will enable you to get hands-on experience with projects related to data science and will expose you to the field when you are applying for a job.
Here are some important skills you will need on your path to becoming a data scientist:
- Proficiency in programming languages like Python and SQL
- Knowledge of data visualizations in the forms of graphs, diagrams, and charts using software like Tableau and Excel
- Knowledge of machine learning techniques through algorithms such as Regressions and Naive Bayes
- Proficiency in software like Hadoop, Apache Spark, and Talend to handle big data
- Strong analytical and mathematical skills
- Ability to communicate well with the other members of your team
- Build Your Own Projects
- Sell yourself!
Step 2: Build a portfolio
Building a portfolio is a great practical way to earn experience and boost your chances of landing a job in data science.
One of the ways that you can find projects to work on are by joining internships where you will be given a case to help analyze. You can also take up pro-bono cases with NGOs and help them structure their data or come up with project ideas of your own.
If you are looking to do a personal project, one way to come up with a topic to conduct your research on can be to identify real-world problems around you – such as climate change, for example – and then gather data on it. You can also find project ideas on the internet on websites like Data.gov.
Step 3: Sharing Your Projects
After you build your portfolio, your next aim should be to share your projects with your peers and potential employers. Make sure you upload your work on platforms like GitHub and LinkedIn so that it is visible to recruiters and they can see what your skill level is. You can also write blogs on Medium to talk about your experiences with your projects and what caught your interest so that your recruiters are well aware of your passion beforehand and consider you for data scientist positions.
Step 4: Finding an Entry-Level Job in Data Science
The best place to start your journey as a data scientist is to start small. While your ultimate goal can be to work in a large-scale organization as a senior data scientist, starting off from entry-level jobs can help you slowly increase your exposure, build your skill, and gain experience.
Taking all of this into consideration, what does an entry-level data analyst do on a typical day? Here are some entry-level data analytics tasks you may be expected to perform:
- Data collection and storage from various sources.
- Conducting exploratory data analysis (EDA).
- Recognizing patterns and trends in a dataset (or datasets).
- Cleaning and quality assurance of data using Microsoft Excel formulas.
- Using the Python programming language, create basic scripts and refactor code.
- Using Structured Query Language to query relational databases (SQL).
- Reporting and presenting your findings in reports and presentations.
While having a Bachelor’s, Master’s, and Doctorate degree is preferred by many organizations looking to hire data scientists, it is not always a requirement. If you’re looking to save the time and money that would be spent in full-time degree programs in college, there are ways for you to learn how to become a data scientist on your own such as self-teaching, bootcamps, and joining companies as entry-level data analysts.
Many people try to get into data science without prior experience. One of the ways to do so is by attending online short courses, getting certifications, and self-teaching programming and analytical skills. You can further hone your data science skills by doing internships and working on personal projects.
You can also attend data science bootcamps which will expose you to 15-20 hours of daily training for about six months, enable you to work on hands-on projects and keep you up to date with newer advancements in the field that you will need to be aware of.
Data science is a very technical field that requires extremely high levels of proficiency in programming, math, and analytics. It requires rigorous practice and a heavy investment of time, and energy. That being said, becoming a data scientist is not at all impossible, especially if you have the passion and skill for what the job requires of you.
Generally speaking, if you do a four-year Bachelor’s and then a two-year Master’s degree, you can land a decent job as a data scientist in a matter of six years. If you are learning how to become a data scientist on your own, graduating from a bootcamp should take you around six months with 15-20 hours of rigorous work every day.
Data science is a popular career choice in 2022 and has vast prospects even in the future with an estimated growth rate of 31% by 2030. The insights provided by data scientists have high value in the eyes of companies that are constantly seeking to perform better. As a result, many multi-billion dollar companies like Google, Pinterest, and Meta are always in search of talented data scientists and are willing to pay them high salaries.
If you are interested in programming and want to learn how to become a data scientist, you must understand all this field encompasses. Data scientists structure, process and analyze data for organizations in order to help them develop better strategies, policies, and customer service.
The preferred education for a data scientist is a Bachelor’s degree followed by a Master’s and a Doctorate but is not always a requirement. With the right amount of practice, you can even enter the field without a college degree.
There are some steps to becoming a data scientist that can help you land well-paying jobs where you can make an average of $122,000 annually. These include developing necessary skills like programming, communication, machine learning, and data visualization through internships, certifications, and bootcamps, building your projects, and sharing them on various platforms.
Even though becoming a data scientist often requires a lot of practice, time, and energy, it is an increasingly popular profession and can be achieved if you have a passion for it.