Changing your career path may sound daunting if you do not know what changes you may require to bring in your current skill set. The journey of career transition takes a lot of patience along with consistency. You must be well acquainted with the field before you jump into the process. These 7 steps will help you to ace the process of a career transition from Data Analyst to Data Scientist:
Step 1: Recognizing Data Science Trajectory
The first thing to do is to understand Data Science Projections. This simply implies the work that a Data Scientist does and how they address problems. Understanding the Data Science Path shall assist you in knowing whether this career transition is the right fit for you.
Data Science is a discipline that can be a little complex if you are new to it. To avoid any perplexing instances, you can read pieces of work written by famous Data Scientists and industry specialists. These resources shall offer insights into the field.
Another way by which you can acquire knowledge in this field is by enrolling in popular Data Science Courses. There are various renowned universities and online programs that provide the best certifications. USDSI®, Harvard, Pennsylvania, and other reputed credential providers and esteemed universities are world-renowned platforms that offer highly-rewarding certifications; enabling you to understand the concept of the field in a thorough manner.
Step 2: Understand the Data Science Tools
After you understand the Data Science Path, the next focus should be on gaining knowledge of the tools of the trade. Since Data Science is a vast field, it comprises a variety of tools. However, if you are new to the field, you should know these most popular ones:
- Python: An easy data science language that consists of many modules and libraries. It is a very popular programming language that is very useful for data analysis and machine learning.
- SQL: It is a database query language that a data analyst might already be familiar with. It is widely used to extract data from databases.
- R: This programming language uses a different syntax but is equally powerful. It is potentially used in data science scientific research.
Step 3: Mastering Machine Learning Algorithms
Machine learning is an integral part of Data Science. Understanding some basic machine learning algorithms is very important for you to transition your career from a Data Analyst to a Data Scientist. A few basic algorithms you must know are:
- Linear Regression
- Logistic Regression
- Decision Trees
- Gradient Boosting
Step 4: Start Building on Machine Learning Projects
Once you get the hang of the basics of machine learning, you are ready to exercise your skill set. To test your potential, you can now start working on some projects. Some machine learning project ideas are given below that you can try:
- Form a recommender system
- Provide predictions on stock prices
- Classify texts
Step 5: Expand Your Network
Data Science is a field that runs on collaboration. Data scientists need to work with others to accomplish goals. Hence, expanding your network is very crucial for a data scientist. Here are some ways that will help you to scale up your network:
- Speak to the data scientist in your team.
- Take part in data science meet-ups
- Join discussion groups
- Follow influencers on social media who make data science content
Step 6: Build an Online Presence
In contemporary times, holding an identity online is as significant as having a real-life one. An online portfolio shall assist you in showcasing your skills and the projects that you have worked on. Some basic pieces of information that you should include in your online portfolio are:
- About me page
- Project page
- Blog page
Step 7: Apply for Diversified Data Science Roles
Once you acquire knowledge and develop the right skill set in the data science field, it is time to opt for job roles.
Here are some tips that will enhance your hiring chances:
- Create a good resume
- customize your cover letter
- Highlight your projects- This will make you stand out among the others and make a good impression.
Conclusion
Making a career transition should not be a strenuous task if you do it the right way. Transitioning into a new career can be daunting, especially if you are unfamiliar with the technical skills required for data science. All you need to do is to have a proper guide before you start things. You can build your technical skills, gain practical experience, build a strong professional network, and apply for data science jobs.
