“How to make a career transition from Data Analyst to Data Scientist ?” This is one of the most common questions our admission counsellors come across. Our career counsellors come across several talented data analysts who are keen to make a career transition to become a data scientist but are not sure where and how to begin.
“I am a data analyst but keen on further advancing my career as a data scientist. I am bored of interrogating data to produce reports and recommendations, it does not seem exciting to me anymore. I am inclined to know more about a data scientist career path where I can use my skills to build robust machine learning models to solve business problems at scale. I am very excited about becoming a data scientist. How can I make the career transition from Data Analyst to Data Scientist?.”
Are you thinking the same? Have you reached a crossroad in your professional life where you feel the need to upgrade your analytical skills or take up an exciting job role like that of a data scientist? Everybody has such moments in life. While a few get the right guidance and opportunity to accomplish it, others fail miserably. If you are one among those aspiring professionals looking to make a career transition from data analyst to a data scientist, this blog will be the best companion for you along your transition career path.
3-Step Guide For Career Transition from Data Analyst to Data Scientist
Let’s put first things first before diving into the career transition path: What is a data analyst? What is a data scientist? And, how do the two job roles differ?
What is a Data Analyst?
A data analyst –
- collects data from various sources
- organizes it effective to identify underlying relationships
- transforms it into a form for easy analysis
- performs statistical analysis,
- and visualizes it using various graphs, charts, and other visualizations to derive meaningful insights helpful for making profitable business decisions.
What is a Data Scientist?
A data scientist job role is an amalgamation of diverse traditional job roles, including computer professionals, mathematicians, statisticians, and a research scientist. A data scientist makes use of math, statistics, and advanced analytic technologies like predictive modeling and machine learning to build models and extract knowledge from data for better business decision making.
Data Analyst vs Data Scientist – The Key Differences
The end goal of a data analyst and a data scientist are the same – how data can be used to make better business decisions. However, the skills of a data analyst are not as advanced as data scientists (for instance: analysts might not have the expertise to build new ML algorithms). For example, if you query a DBMS and find out that a mom who buys a frock also buys a matching tiara for the little one and share this finding with the advertorial team of the organization – you are a data analyst. On the contrary, if you build a recommendation algorithm recommending matching tiaras for baby girls based on the purchase history of the customer – you are a data scientist.
A data analyst and a data scientist are two different job roles but considering them from a career viewpoint they are complementary to each other rather than one being at a higher level than the other. Data analysts and data scientists are like the two wheels of the same barrel. The two job roles are overlapping in nature, however, the major difference between the two is the programming/coding expertise. And let us tell you learning how to code requires just a few months and not years so your career transition from data analyst to a data scientist is not much of a difficult transition career path.
Whether you’re interested in becoming a data scientist or a data analyst, both job roles are in huge demand with over 97,000 jobs available in data science and analytics and an average data scientist salary of 8 lakhs per annum. Data analysts are also highly paid, but the average data analyst salary in India is much lower than that of a data scientist. Nevertheless, even a data analyst job role is rewarding and beneficial on its own with great demand because the world needs analysts too. Moreover, taking on a data analyst job role is definitely a good way to get started with a career in data science.
How to make a career transition from Data Analyst to Data Scientist?
The career transition from data analyst to data scientist is not a linear progression where you begin your career as a data analyst and work your way up to a data scientist. You need to follow a series of practical steps and resources to climb up the success ladder as a data scientist. Here is the 3-step approach to making a successful career transition from data analyst to data scientist –
Step 1 : Understand and Exercise the Job Role of a Data Scientist
To be able to become a successful data scientist, you need to have a concise and clear knowledge of the differences between the profile of a data analyst and a data scientist. As a Data Scientist, you will have to bring a completely novel approach and perspective to understanding data. A data analyst might only be responsible for describing the trends and correlations while as a data scientist you will have to uncover new business questions and build machine learning models to answer those questions based on data.
Data Scientists are –
- Involved in building ETL pipelines
- Data Mining using Application Programming Interfaces (APIs)
- Responsible for data cleaning using data science programming languages like Python or R.
- Build predictive models using machine learning algorithms such as gradient boosting, linear regression, logistic regression, decision trees, Random Forest, and more.
- Evaluate models to validate the accuracy of analyses.
- Test and improve the accuracy of already built ML models.
- Build visualizations to narrate the story of an advanced analysis result.
- Build various automation tools and techniques like libraries to ease the day-to-day tasks for the data science team.
Having understood about a data scientist job role, you need to prepare yourself to learn and experiment considering a data scientist role as a completely new lifestyle. The best way is to begin exercising the data science skills you learn in your current role as you think deeply about the career transition. This will help you frame a clear picture of what data science skills you already know and what you need to learn. With the data available in your organisation, you can start leveraging your problem-solving skills and critical thinking today to prepare yourself for a better data science role tomorrow.
Another best approach to prepare and completely transition into a data science job role is to pursue a certified data science course that will give you complete 360-degree knowledge of all the required data science skills and diverse resources to help you prepare a perfect data science portfolio through multiple projects.
Step 2: Master the Art of Coding
As a data analyst, you might not have advanced programming expertise or coding skills because you have been involved only with identifying trends in an organization’s valuable data to project future business growth. However, a few analysts might be involved in coding in their day-to-day tasks but that would be confined only to cleaning and normalizing the company’s data. To become a data scientist having the ability to code is very critical. As a data scientist, you need to be comfortable with different programming environments to play with data as and when needed.
Understanding the syntax of a relevant programming language ( say Python, R, or Java) is necessary for any data scientist position (entry-level or senior). To make a career shift towards data science you need to learn how to implement the mathematical and statistical concepts with minimal efforts using various in-built functions and libraries in the programming language. The more coding you know the better it is for any data scientist position.
Try to work on some real-world data science projects to highlight your coding skills and strengths. This will help you master coding skills and make you better at handling the real-one in a production scenario.
Step 3: Develop Your Math and Model Building Skills
As a data analyst, you will be extracting, munging, and visualizing data to aid business decisions. Though mathematical logic is involved in the analysis part, it’s not heavy math. Data analysts are usually inclined towards the minimal requirement of mathematics while data science requires a strong mathematical foundation.
As a data scientist, you will have to write algorithms from scratch requiring an in-depth understanding of linear algebra and calculus so advanced math is an absolute necessity to understand how a machine learning algorithm works and behaves. Having a strong mathematical base helps understand the nature of the machine learning model and how it can be tweaked to improve its accuracy. Even if you are using the predefined libraries, it is essential to understand the calculations that are being performed behind the scenes before you can actually apply them to the actual business problem.
Apart from making maths your friend, you will need to enhance your model building skills by working with your peers and other data scientists to solve challenging business problems. This will help you explore your model building skills and evaluate them.
Advice from Springboard Experts on Career Transition
No one is going to push you to reach your data science destination. Be proactive in finding lucrative data science opportunities and volunteer yourself whenever a data science task comes up within your team or there are similar open data science job opportunities in your organization. Acquiring and learning all the data science skills might take longer than expected because even the best data scientists in the world still have lots to learn. Do not worry about having limitations with your skillset as you can just better with each day. All you need is a determination to reach a particular skill level.
The career transition from data analyst to a data scientist should be accompanied with a well-crafted transition plan. We suggest you do complete research on what the data scientist job role entails and then do a self-assessment of your existing analytic skills. Identify the experiences and skills gap that you need to fill while making the transition. Having identified the skills gap, brand yourself as a data scientist through the roles and responsibilities taken on by a data scientist with the goal of transforming into a successful enterprise data scientist. If the said transition steps are followed, we assure you will end up as one of the best data scientists in the organization with a diverse set of transferable skills. If transitioning to a data science career is your goal, Springboard offers various 1:1 mentoring-led and project-driven programs in Data Science and Machine Learning to help you sharpen your skills and make a smooth transition that comes along with a job guarantee. And that’s a wrap, hope you found this post helpful.