Data science is a rapidly growing field that combines statistical analysis, machine learning, and programming skills to extract insights and value from data.
While a technical background is often associated with data science, it is entirely possible to transition into this field from a non-technical background.
Big data and business analytics are expected to generate $210 billion in global revenue by 2020.
Naturally, there has been a lack of data science specialists due to rising demand, and businesses are welcoming people looking to embrace the sector and join the workforce.
The supply and demand mismatch has undoubtedly been aggravated by the large difference between the skill sets that are currently in demand and those that are currently available.
Nevertheless, it is a rewarding career opportunity in a field that is expected to expand and permeate into other industries, no matter how far away they may have originally been.
Do not worry if you want to bridge the knowledge gap and move from a non-technical background to data science and related subjects. These guidelines will show you how to enter a competitive field and remain at the top of recruiters’ minds as new job opportunities arise.
This article aims to provide a roadmap for individuals who aspire to pursue a career in data science without a strong technical foundation.
Steps in Getting Into Data Science From a Non-Technical Background
By following these steps and leveraging your existing skills, you can embark on a successful journey into the world of data science.
Choose your dream job
Data science is a very diverse profession, so narrowing down your job role and working towards it can help you create goals and get rid of any skills you may not need right now.
The majority of roles in technical jobs require these skills.
- Business knowledge
- Statistics
- Mathematics
- Programming
This is because data scientists and analysts play a crucial role in presenting insights to stakeholders and subject-matter experts from other disciplines.
The long-term purpose of data science in the business world is to produce insights that may be used to inform future plans for the company, including those for boosting revenue, boosting sales, and hiring top talent.
However, you can only choose the technical skills that will help you in your dream profession when you know which path you want to take.
Evaluating existing knowledge that, despite coming from another subject, is applicable in this one is a key next step.
This is especially true for graduates with backgrounds in economics, mathematics, statistics, or business studies; data science features from these fields are well-integrated into them.
Knowing what you need to learn is the first step in learning.
Understand the Field by taking a data science course
Start by gaining a solid understanding of what data science entails. Research the core concepts, techniques, and tools used in data science, such as statistical analysis, machine learning algorithms, and programming languages like Python and R.
Familiarize yourself with real-world applications of data science and the industries where it is in high demand.
Candidates must upskill by enlisting in a well-outlined course with reputable educational institutions or course providers if they want to be able to obtain a head start in the profession as a newbie with little to no experience.
The following subjects should be included in the ideal curriculum:
- Basics of programming (Java, R, Python)
- Big data handling
- Deep learning
- Statistics and probability
- Data visualization
Since it would take a lot of effort to gather pertinent resources and figure out where to start if one were to start their research journey alone, this is the simplest and most organised way to go about learning about the area.
Furthermore, it is essentially difficult to set a goal for oneself to study every skill set within the data science umbrella. Data science courses with certificates are the greatest way to gain a head start because some talents are also reliant on experience and people skills.
These programs frequently include career guidance, job placements, and mentorship programs with professionals in the field and are designed by veterans in the field.
Business Problems and Decision Automation Needs
Before enrolling in any paid classes, be sure to ask the questions listed above. Sometimes, if you’re fortunate, you’ll come upon a free course or open-source programme that provides you the boost you need. You can then choose whatever premium certification you wish to pursue after going through it and determining what you hope to get from the course.
Today, almost all industries have improved their organisational skills, adopted best practises, and begun automating inefficient procedures. Data science can be used to uncover common implementable procedures that can be automated. You can also create a business problem and work towards a business objective to start a Proof Of Concept [POC].
Example: Decision Automation
In the area of human resources, what if a business could automate the collecting of all employees’ KPIs?
NLP-based deep learning automation can process the employee’s self-assessment exam during a performance review while machine learning automation can process the numbers to provide a rating. To give the employee a final rating, you can mix both of these factors.
This will cut down on managerial work and shorten the evaluation period. Every review cycle can start this automation. Additionally, bias and disagreements can be eliminated using machine-based evaluation, saving HR time when handling employee review complaints.
Develop a Strong Foundation in Statistics and Big data
Data science heavily relies on statistical analysis and mathematical modeling. Strengthen your knowledge of statistics, probability, and linear algebra by taking online courses, watching tutorials, or attending workshops. Understanding these concepts will help you grasp the fundamentals of data science.
Huge amounts of data and statistics are necessary for data science and its hybrids.
STATISTICS: Statistics is essential to every one of the important events that led to the current explosive development in the usage of analytical decision-making.
Learn More: A Useful Guide On Statistical Package For The Social Sciences [SPSS]
BIG DATA: A collection of data that cannot be efficiently managed, processed, or analysed using traditional software or algorithms.
From the above, we can infer that you must be comfortable with numbers, large datasets, and using statistical methods to draw conclusions from them.
Learn Excel
Since it is primarily used to gather and store data, Excel is a fairly well-known piece of business software. As a result, Excel has the majority of the data that you can use.
Understanding how to edit and analyse data in Excel is an easier starting point for someone with a non-technical background and probably no programming knowledge, and it will serve as a very valuable foundation for your career as a data scientist.
Learning fundamental Excel skills like cell referencing, creating simple formulas, and utilising aggregation operations like SUM, AVERAGE, MAX, MIN, as well as logical functions like IF, AND, OR, is a good place to start.
Learn More: Differences Between Microsoft Excel and SPSS
Once you have a firm grasp of the fundamentals, you may go on to learning how to extract information from data using search functions like INDEX/MATCH, VLOOKUP, NESTED IFS, and conditional aggregation functions like COUNTIF(S), SUMIF(S), and AVERAGEIF(S).
The next step will be to learn how to use pivot tables, which will make it easier and more structured for you to aggregate and summarise data. You can learn how to make visuals and dashboards utilising Pivot Charts, Slicers, Filters, and other tools in addition to Pivot Tables.
Learning these concepts will provide you with a basic understanding of how to extract meaning from raw data and convey your results through visualization.
Acquire Data Handling Skill
Data preprocessing and cleaning are essential steps in data science. Gain proficiency in data manipulation techniques, data cleaning, and feature engineering. Learn how to work with different data formats, handle missing values, and normalize data. Online courses and practical projects can help you develop these skills.
Learn Power BI
The next step you can take is to recreate principles learnt in Power BI once you have a basic understanding of working with data in Excel.
Power BI includes more sophisticated functionality for working with data, even though it functions similarly to Excel in terms of cleaning, analysing, and visualising data. Beginners should concentrate on using Power Query to connect to data, clean it, and carry out simple transformations.
Power BI’s data modelling function teaches you how to model relationships between data tables. Additionally, you can learn how to use DAX to model your data and conduct calculations. Following that, it will be good to look at how to develop visualisations and dashboards in Power BI utilising both in-built and custom visualisations, as well as tools such as filters, slicers, bookmarks, page navigation, and so on.
To share and collaborate on Power BI reports, it would also be beneficial to have a basic understanding of how the Power BI Service operates.
Learn Python
Although Power BI and Excel are excellent tools for data analysis and visualization, data scientists frequently employ the extensive range of packages in the Python programming language to carry out more complex descriptive, prescriptive, and predictive data analysis, including machine learning.
You must first comprehend some python language fundamentals, such as general syntax, dealing with variables and expressions, control flow (loops and conditional statements), functions, and data types and data structures, before you can begin utilizing python for these data analysis functions.
With a thorough grasp of these ideas, you should be able to use Python to build straightforward logic-based programs and lay the groundwork for learning the language for data science.
Learn Machine Learning
Exploratory Data Analysis is more of a descriptive analysis, so if you are comfortable with it, you can go on to the predictive side of data science by learning machine learning.
You can begin by studying the typical supervised machine learning techniques for classification (logistic regression) and regression (linear regression).
To apply these algorithms without writing any code from scratch, you’ll need to be familiar with the scikit-learn package for Python.
You can learn more categorization algorithms like Decision Trees, Random Forest, Naive Bayes, and Support Vector Machines after acquiring a general understanding of these two. Decision Tree Regression and LASSO Regression are two other regression algorithms.
Learning SQL software
You might want to consider learning SQL to work with relational databases once you’re more accustomed to working with data in Excel and other common data sources.
As a data scientist, you will frequently interact with vast amounts of SQL data because it is frequently used by businesses to store structured data.
PostgreSQL and MySQL are the most widely used SQL software, and I strongly recommend learning both. You can begin with one of them as a beginner.
Find industry mentors
There are lots of certified mentors out there, including those employed by universities and education technology firms. Before selecting a mentor, you should consider the course content and methods that will be used.
It is never easy to make progress, no matter what industry you decide to enter. The same is true for people with non-technical backgrounds who want to enter the field of data science, however finding a mentor can be advantageous.
The benefits of hiring a mentor with at least five years of practical expertise in data science are as follows:
Networking: Mentors can connect new hires with top-tier recruiters and industry veterans, potentially securing the candidate’s career or at the very least lending a helpful hand.
Industry insider tips: Mentors are a treasure of professional expertise since they have spent years applying theory to real-world situations. They can also teach valuable lessons on how to improve soft skills like managing people, coping with deadlines, and working with other teams to achieve shared objectives.
Question-and-answer session: Mentors could be able to provide all the information you need regarding the field, open positions, and future career advancement. They are also very helpful for aspiring entrepreneurs and ideators to propose their concepts again and receive helpful criticism in return.
Long-term relationships: Developing a relationship with a mentor can be helpful at any point in a person’s career. It’s common to turn to mentors for support and assistance. They can be quite helpful in times of uncertainty and uneasiness because they have probably experienced comparable highs and lows and have learned from them.
Build a Strong Portfolio
Practical experience is invaluable in data science. Work on personal projects that showcase your skills and understanding of data science concepts.
Utilize publicly available datasets or find datasets related to your interests. Implement data analysis, visualization, and machine learning techniques to derive insights from the data.
Share your projects on platforms like GitHub or Kaggle to showcase your abilities to potential employers.
Networking and Continuous Learning
Engage with the data science community by participating in online forums, attending meetups, and connecting with professionals in the field.
Networking can lead to valuable insights, job opportunities, and mentorship.
Stay updated with the latest trends and advancements in data science by reading blogs, following industry experts, and participating in online courses or webinars.
Roadmap to Get Into Data Science From a Non-Technical Background
Here’s a table summarizing the key steps to get into data science from a non-technical background:
| Steps | Description |
|---|---|
| Understand the Field | Gain a solid understanding of data science concepts, techniques, and real-world applications. |
| Develop a Strong Foundation in Statistics/Mathematics | Strengthen your knowledge of statistics, probability, and linear algebra through online courses or tutorials. |
| Learn Programming Languages and Tools | Learn programming languages like Python or R and familiarize yourself with data manipulation libraries and visualization tools. |
| Acquire Data Handling Skills | Learn data preprocessing, cleaning, and feature engineering techniques to work with different data formats. |
| Learn Excel, Power BI, Machine Learning Techniques | Understand the basics of Excel, Power BI, machine learning algorithms and their applications in data science. |
| Build a Strong Portfolio | Work on personal data science projects to showcase your skills and understanding of data analysis and machine learning. |
| Networking and Continuous Learning | Engage with the data science community, network with professionals, and stay updated with the latest trends and advancements. |
| Pursue Further Education | Consider additional education options such as online certificates, bootcamps, or a master’s degree in data science. |
Conclusion
Transitioning into data science from a non-technical background requires dedication, self-learning, and perseverance.
By acquiring knowledge in statistics, programming languages, and machine learning, building a strong portfolio, networking with professionals, and continuously updating your skills, you can successfully enter the field of data science.
Embrace the challenges, be proactive in seeking opportunities, and leverage your unique perspective to bring valuable insights to the world of data science.
Your non-technical background can be an asset as you approach data problems from a different angle, leading to innovative solutions and diverse perspectives in the field.
Good luck on your data science journey!
