Good opportunities come by rarely. When life hands you a chance to climb your career ladder, you must seize the rungs and start hauling yourself upwards. The opportunity to upgrade from a data analyst to a data scientist is one such opportunity. If you want to make the most of it but don’t know where to begin, this blog may be the right guide for your career transition journey.
So, let me begin with the question:
Why Should You Become A Data Scientist?
Whether you are a data analyst or a data scientist, the goal is the same- to make data usable for aiding business decisions. But what makes a data scientist more valuable are the skills you acquire in this job. As a “data scientist”, you don multiple hats as a research scientist, statistician, mathematician, computer scientist, and more.
So, to answer the above question, you ought to consider becoming a data scientist because :
1. There’s high demand.
As the importance of data burgeons around the world, so does the demand for skilled data scientists proficient in handling it. And the best thing about this job is its versatility which makes it compatible with all major industries like IT, technology, retail, healthcare, and finance.
2. The remuneration is good.
A high-demand job mostly comes with a good salary package. This holds true for data scientists. Since you handle more complex tasks with your specialized skills and shoulder a wider range of responsibilities, you can command a higher salary than data analysts.
3. You collaborate with diverse teams.
As you take on bigger responsibilities as a data scientist, you get to work with professionals across diverse disciplines. You may have to discuss many data-related aspects with business and data analysts, software engineers, and project managers. This diverse collaborative experience abundantly enriches your experience and knowledge.
4. You work with advanced technologies.
As a data scientist, you get an early introduction to advanced technologies like Machine Learning, Artificial Intelligence, and Deep learning. Hence, you get to work and understand these latest technologies and stay on top of the latest developments.
5. You gain access to big data.
An organization’s operations, customers, and products can be better understood using data scientists’ large, complex datasets. Data scientists have direct and unlimited access to these datasets for deriving actionable insights for the organization.
Of course, the role of a data scientist is challenging, but it pays richly in terms of remuneration and career scope. But more than anything else, this job can be very soul-satisfying as it empowers you to make strategic contributions to your organization.
As you transition from data analyst to data scientist, you will need all your technical skills, expertise and, experience you can gather. Having worked as a data analyst, you are already adept at thinking about data in unique ways. However, you need to sharpen a whole set of technical skills to perform as a data scientist.
Key Skill Sets to Transition From Data Analyst to Data Scientist
Your transition journey from data analyst to data scientist begins by upskilling your programming and coding expertise, one of the biggest skill-based differences between jobs. Although coding will not take more than a few months to master, you may also need to build other skills, such as:
- Proficiency in data languages like Python, R, and others related to your field of interest.
- Relational databases like Oracle, SAP HANA, Microsoft SQL Server MySQL, and PostgreSQL.
- Proficiency in OAuth, Microsoft Azure, and IBM Watson.
- Natural Language Processing, Deep Learning, Neural Networks, Optical Character Recognition, and Computer Vision.
- Post graduation or Ph.D. in Statistics, Computer Science, or Software Engineering.
- Proficiency in ML algorithms like SVM, KNN, Random Forest, Decision Tree, Linear Regression, and Logistic Regression.
- Distributed computing like Spark, MapReduce, and Hadoop.
- Data visualization such as ggplot, Plotly, RShiny, and Matplotlib.
Along with these newly acquired skills, you should also polish your existing technical proficiencies gained from your data analyst experience. This list includes:
- Data analysis to interpret and draw insights from data to help your organization make informed decisions.
- Data manipulation and visualization to clean, transform, and prepare large datasets for analysis.
- SQL for data querying and database manipulation.
- Excellent business acumen to drive business decisions and deliver data-based insights that support business objectives.
- Experimental mindset and in-depth knowledge of statistical methodologies.
Now, once you have polished your existing skills and added more technical abilities to your repertoire, you can start working your way up to becoming a data scientist. Of course, there’s no straight-shot way of doing it but to advance through the ranks step by step. Here’s a step-by-step guide to take you through the process.
8 Steps to Transition From Data Analyst to Data Scientist
Step 1: Understand your role.
To successfully don the role of a data scientist, you must have a profound understanding of how this role differs from that of a data analyst. Once you have gained this basic understanding, you can view data from a data scientist’s perspective.
Step 2: Hone your skills.
Learning and experimenting in a data scientist’s role requires different problem-solving and analytical skills. Hence it necessitates a shift in skillset and mindset. If you seriously consider transitioning from data analyst to data scientist, you should start applying your acquired skills and add new ones to your repertoire.
Step 3: Create a portfolio.
Owning a professional data analytics portfolio is important if you are a data analyst aspiring to become a data scientist. Your portfolio should mirror your passion for data science by highlighting your pet projects and interests. It should also reflect your seriousness about transitioning to a career in data science, even if you lack any experience.
Step 4: Optimize your Math skills.
This is the right time to bring your Math and modeling proficiencies to the forefront. You need these skills to:
Step 5: Prioritize coding.
Coding and mastery of Python, R, and Java are must-haves for any data scientist wanting to make a mark in their career. Whether yours is an entry-level or senior data scientist’s job, your coding skills are necessary for:
- Understanding the syntax of relevant programming languages.
- Effortlessly applying mathematical and statistical concepts.
- Utilizing built-in libraries and functions to programming languages.
In short, the better your coding skills, the easier your job will be.
Step 6: Acquire valuable certifications.
Consider investing in a certified data science boot camp. As tough as it may be, it will make a difference in your understanding of data science. A well-rounded certification course will arm you with the latest data science skills and resources. With all this at your disposal, you can breeze through multiple projects and add more value to your portfolio.
Step 7: Create a network of contacts.
As a data scientist, who you know in this field is as important as what you know about it. Your network of acquaintances, friends, peers, and colleagues in data science is priceless. If you already have a thriving network, expand and strengthen it with new contacts. Attend industry conferences, keep track of your peer’s work, and talk to them about it. Don’t hesitate to reach out to experienced colleagues for help. Who knows? Your best career opportunities may come right from this network.
Step 8: Stay in touch.
Read voraciously about what’s happening in data science, data architecture, and modeling. Browse the latest trends and technologies through printed articles, journals, and websites. Follow industry leaders and newsmakers on social media platforms. For example, you can check out Andrew Ng, Kirk Borne, Lillian Pierson, or Hilary Mason to get a grip on the latest developments in data science.
Make the Shift With a Well-Crafted Transition Plan
No career transition is successful without a solid, failproof plan, a lot of hard work, and patience. To take the leap from data analyst to data scientist, I suggest you do the following:
- Understand what your data scientist job entails
- Assess your existing technical skills
- Identify the new skills you will need
- Understand your new roles and responsibilities
Now, go ahead and make your mark as a skilled data scientist! Good luck!