Since the inception of generative AI many redundant jobs have become extinct, and the era of data driven job-role dominance has begun. According to the report from EY, 93% of companies are already invested in utilizing data and producing solutions that drive business growth.
Data science is a major force driving these opportunities. Dubbed as the “Hottest career choice of the 21st century” by Harvard Business School, data science has grown in demand. According to Indeed, data scientist job posts are up by 256% on their platform, reflecting the growing need for skilled professionals in this field.
Wondering how to get into data science from a non-tech background? Keep reading further, and learn the success mantra that can guide you from scratch to becoming a data scientist within a year.
Table of Contents
Learn more about How to Get Into Data Science From Non-tech Background through this YouTube video:
Why Data Science?
Data science has remained one of the most in-demand areas for over a decade and shows no signs of slowing down. According to the US Bureau of Labor Statistics, there will be 11.5 million data science jobs by 2026. Right now, in early 2024, there are just 4 million data scientist professionals available. This figure highlights the big difference between the number of data scientists needed and the number available.
The high demand for data science is closely tied to its crucial role in the success of modern companies. Think about the most powerful businesses in the world: Google, Apple, Amazon, and Facebook are all largely dependent on data-driven decision-making. Data literacy and analysis are no longer optional in today’s competitive environment; they are required for survival and growth.
Not just that, data scientists are offered salaries higher than industry standards. According to Glassdoor, Jr. Data scientists can expect an average pay of ₹13 lakhs in India and US$156,000 in the USA.
High demand, great salaries, and futuristic skills make data science career a great option. Moving further, let’s look into the essential skills required for data scientists.
Skills Required to Become a Data Scientist
Establishing a strong foundation in the field of data science requires a combination of technical and soft abilities, which are necessary for becoming a data scientist. To become a data scientist, an individual has to possess the following skills:
- Solid foundational understanding of mathematical concepts like Statistics and Probability
- Good working knowledge of Python or R
- Proficiency in Database Management, including SQL and NoSQL tools
- Experience with spreadsheets
- Familiarity with data visualization tools like Power BI or Tableau
- Sound knowledge of Machine Learning and Deep Learning concepts
- Knowledge of Cloud Computing platforms like Azure, AWS, or GCP
- Specialist in Version control systems (i.e., Git and GitHub)
- Strong communication skills and storytelling skills
As we move forward, if you have any doubts, such as, “Will it be difficult to transition from a non-tech background to data science?” Let us address and resolve them.
Is It Hard to Transition into Data Science from Non-Tech Background?
We started this article by saying that even if you’re not tech-savvy, you can still become a data scientist; all you need is interest in numbers and the ability to learn things by doing them. As a suggestion, revise mathematical concepts that you learned at your college level, such as descriptive statistics, calculus, linear algebra, normal distribution, correlation, probability, etc.
You don’t need to solve numerical problems for these concepts; all you have to do is understand how they work. After that, you will be able to understand the ML concepts and deep learning concepts and find relevance in how they drive the predictions. This will help you build problem-solving intuition.
This may sound like a lot, but remember that there are multiple learning resources available, starting with documents, blogs, YouTube videos, communities, and professional certifications.
Opting for professional certification is more recommended for you, as it provides comprehensive training from fundamental concepts to advanced skills. Most professional certifications provide dedicated learning managers who can help you sharpen your skills. Not only that, the pre-planned and well-structured curriculum would help you comprehend skills in a systematic manner.
That being said, let us look into the step-by-step roadmap that, if followed, will definitely guide you towards becoming a data scientist.
How to Get Into Data Science From Non-tech Background – Step-by-Step RoadMap
Step 1: Statistics and Probability
Most recruiters, who hire data scientists, expect strong analytical skills. To develop these skills, you need to develop mathematical intuition. Familiarize yourself with critical statistics concepts, such as normal distribution and descriptive statistics. Go through calculus formulas and linear algebra concepts that will later help you understand the formulation of machine learning algorithms.
Step 2: Proficiency in MS Excel
Next, start learning Excel. It is a great starting point for data exploration and analysis. Its user-friendly interface makes it simple to learn and use, especially for individuals unfamiliar with data science concepts. Excel has strong data cleansing, organizing, and manipulation tools, including filtering, sorting, and eliminating duplicates.
Focus on learning important functions and formulas such as logical functions (IF, AND, OR), lookup functions (VLOOKUP, INDEX MATCH), statistical functions (AVERAGE, MEDIAN, STANDARD DEVIATION), and text functions (LEFT, RIGHT, CONCATENATE). In addition, learn how to manage missing values, delete duplicates, format data uniformly, and spot trends using conditional formatting.
Step 3: Working on Data with Power BI
Power BI provides data scientists with a variety of robust data visualization features. These features include seamless interaction with other Microsoft products, such as Excel and Azure, making it simple to share and collaborate on dashboards across teams. Whether you’re uncovering hidden trends, identifying correlations, or conveying findings to stakeholders, Power BI enables you to build compelling data stories.
Next, Explore DAX, Power BI’s formula language, which enables computations and data manipulations directly in your visualizations. You may create reliable data models that link and control relationships between many data sources with DAX. In order to customize your findings for particular audiences, get even more creative with the wide range of visualization options available.
Step 4: Start with Python Programming
Python has become the lingua franca of data science, and this is no coincidence. Its adaptability, power, and ease of use make it ideal for discovering hidden truths inside data. A diverse ecosystem of data science libraries, including Pandas, NumPy, and scikit-learn, offers pre-built tools for data manipulation, analysis, and machine learning. Learning Python programming makes it easy to enter the field of data science and opens up numerous opportunities for career advancement.
Python also interfaces smoothly with other popular data science tools, such as Jupyter Notebooks and SQL databases, resulting in a more efficient workflow.
To get started, learn basic Python concepts such as lists, tuples, and dictionaries. The foundation for increasingly complicated tasks is made up of these data structures. Explore Pandas next, which is an effective framework for filtering, aggregating, cleaning, and selecting data.
NumPy provides powerful tools for numerical computations and operations on huge datasets, including matrix multiplication and linear system solving. Finally, experiment with visualization frameworks such as Matplotlib and Seaborn to create interesting data narratives.
Step 5: Familiarity with Machine Learning Concepts
Understanding machine learning concepts is essential to build your toolbox because it helps you understand how algorithms use data to learn and create predictions. With this expertise, you can extract useful insights from unprocessed datasets, use them to automate activities and make precise forecasts.
Begin by learning about supervised learning principles like classification and regression, as well as unsupervised learning ideas like clustering and dimensionality reduction.
Learn about principles related to model evaluation such as cross-validation, confusion matrices, and correctness. Go on to more complex ideas like ensemble methods, reinforcement learning, and deep learning once you have a firm basis.
Step 6: Solidify your Data Management Skills
Next, establish your database management and SQL (Structured Query Language) skills, which will assist you in retrieving, manipulating, and analyzing data contained in relational databases. SQL can be used to filter, manipulate, and clean data prior to analysis.
Begin by reviewing basic database management and SQL fundamentals. Simultaneously, practice often on platforms such as HackerRank and LeetCode. You get a solid foundation and progress toward proficiency with this dual approach.
Data scientists recognize the ongoing problems of handling ever-expanding information, computationally intensive jobs, and the necessity for highly scalable resources. Fortunately, cloud computing can help to address these difficulties by offering a flexible and powerful alternative.
Cloud systems enable seamless scalability of resources based on your project’s requirements, eliminating the limits of on-premise hardware. It is more affordable to use the pay-as-you-go model because it does not require any upfront infrastructure costs.
To get started, consider gaining familiarity with prominent cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Your practical experience will give you invaluable understanding for handling data sources and completing necessary activities in cloud environments.
Step 8: Experience with Version Control Systems
As a data scientist, you must effectively manage your code, scripts, and data. Version control systems, like GitHub and Git, are essential for tracking changes, working with others, and rolling back to earlier versions as needed. Keeping a thorough record of all the changes you’ve made to your scripts, data files, and code is crucial. This approach promotes effective collaboration, allowing you to work fluidly with coworkers while merging modifications and avoiding disagreements.
To get started, familiarize yourself with fundamental Git commands such as add, commit, push, pull, and merge. Explore other GitHub features, including branches, pull requests, repositories, and version control visualization. These tools will improve your capacity for efficient project management and teamwork.
Step 9: Start Working on Real-World Projects
It’s time to demonstrate your abilities now! Look for datasets in any area that interests you; the options are unlimited, whether it be sports, music, finance, or another subject. Engage in projects that address real-world challenges, as this is where the magic happens: learning via hands-on experience.
To begin real-world projects, it is critical to identify and gather the necessary data for exploration. Several online platforms, like Kaggle, the UCI Machine Learning Repository, and FiveThirtyEight, can be useful for this purpose. These platforms give you access to a variety of datasets, so you can work on relevant projects that advance your knowledge and abilities.
Step 10: Networking and Collaboration
The data science community is full of amazing people. Connect with them online and offline through seminars, workshops, and numerous online forums. To improve your talents, consider working with others on projects, sharing your experiences, and learning from them.
Take the initiative to create and update your profile on popular job platforms such as LinkedIn and Naukri. Make sure you update your profiles on a regular basis with your relevant achievements and skills. Engage on these platforms by contributing your successes, development, and industry knowledge. By doing this, you make yourself more noticeable to recruiters and raise your chances of getting interviews.
Data Scientist Salary (India & USA)
The salary of a data scientist varies based on factors like experience, location, and industry. However, as of 2024:
Data Scientist Salary in India
Source: Data Scientist Salary in India Glassdoor
According to Glassdoor, the average salary for data scientists in India ranges from ₹9 lakhs to ₹15 lakhs per annum.
However, with experience, the salary could go as high as ₹22 lakhs per annum.
Data Scientist Salary in USA
Source: Data Scientist Salary in USA Glassdoor
According to Glassdoor, the average salary for data scientists in the USA ranges from $100,000 to $130,000 per annum.
However, with experience, the salary could go as high as $200,000 per annum.
FAQs
Can I get into data science with no experience?
Yes, someone with no prior expertise can still work in the field of data science. All that is necessary is a thorough understanding of the tools and technology used by data scientists to construct predictive models or assist organizations in effectively using their data for future forecasts.
Can I do data science without a science background?
Of course! Science degrees are mentioned in certain data science professions, but they are not strictly necessary. A wide range of backgrounds is represented among successful data scientists. Focus on developing fundamental skills such as statistics, Python, R, and data analysis tools. Remember that this particular field values new talent and problem-solving skills.
Does data science have a future?
The future of data science is bright! Data is increasing in volume, demanding the analysis of experienced individuals. Anticipate specialization in the industry, since there is a need for data-savvy professionals in many different sectors. The need for data-driven insights will only grow, even though the term “data scientist” may change over time.
Is 3 months enough to learn data science?
Gaining foundational skills may take 3 to 6 months for some people, but mastering advanced skills may take 1 to 2 years or even longer. The amount of time needed to learn data science varies depending on your learning objectives, past knowledge, preferred learning style, and level of proficiency.