Have you hit a career plateau after gaining expertise as a software engineer? Are you considering a transition within the same domain in order to open up exciting possibilities? Leverage your previous skills and expand your knowledge set, opening the paths to a better polished YOU. A career transition from software engineer to data scientist can be a perfect option. Familiarize yourself with the nuances of the same in the following article.
Here’s what we’ll cover:
- Who is a Software Engineer?
- Who is a Data Scientist?
- Steps To Make A Career Transition From Software Engineer To Data Scientist
- Skills Required For Transition From Software Developer to Data Scientist
- Projects to Work on When Transitioning From Software Developer to Data Scientist
- Role of Interview Kickstart in Career Transitioning
- Frequently Asked Questions on Career Transition from Software Engineer to Data Scientist
Who is a Software Engineer?
Software Engineers deal with the designing, development, testing, and maintenance of software applications. They apply their knowledge and principles of programming languages to build software solutions for the users. The multiple applications of the software vary for business, network control systems, computer games, and middleware.
Who is a Data Scientist?
Data scientists handle data in different ways and through varying techniques to come up with effective solutions for business problems. They are used in decision-making, forecasting outcomes, providing insights, recommendations, and other endeavors by seeking patterns and trends in data collection.
Steps To Make A Career Transition From Software Engineer To Data Scientist
A software engineer with in-depth knowledge of programming languages can put their expertise to grow their career as data scientists. They need a few modifications in their possessed skill set through the following steps:
Step 1: Begin with Basics
The beginning should involve learning or getting familiar with the important concepts of data science. Revising mathematics, statistics, and other fundamental concepts through online or offline resources is helpful. Analyze your familiar programming language and explore the trending ones in the current job market. Though Python dominates the world, other languages like R, Java, and C++ are also among the commonly used ones.
Step 2: Transit Yourself to Data First
A software engineer’s life revolves around codes and programs, while that of a data scientist is around data. The transition here requires considering data to be the top priority and working according to the data rather than code. The individuals must be more focused on controlling, storing, auditing, processing, and using the data in comparison to dealing with use cases and version control of code as software engineers. However, data scientists do use code to manipulate and analyze data, so it is not a complete departure from coding.
Step 3: Master Algorithms
Data scientists basically deal with Machine Learning and Deep Learning, specifically with numerous algorithms. Although there are many other algorithms used in the field, the choice of which ones to learn depends on the specific area of data science you get into. Here are the few that you must get familiar with:
- K-means Clustering Algorithm
- Apriori Algorithm
- Naive Bayes Classification Algorithm
- K-Nearest Neighbors
- Linear, logistic, and polynomial regression
- Decision Trees
- Random Forest
- Principal Component Analysis
- Support Vector Machines
- Linear Discriminant Analysis
Step 4: Handle Libraries
Gaining familiarity with previously stated algorithms gives you an upper hand in handling the libraries associated with different programming languages. The predefined libraries here provide the easy usage and implementation of algorithms.
Step 5: Choose Your Area of Expertise
Data science deals with multiple actions in data, offering a wide scope of expertise and jobs. Therefore, understanding the right action to deal with the data helps you gain expertise in a specific area with minimum effort. Candidates seeking a transition from software engineering to data science must opt for specialized areas like Computer Vision, Machine Learning, Natural Language Processing, and others.
Step 6: Practice
Focus more on getting well-versed with data handling, algorithms, and other routines of data scientists. Additionally, focus more on gaining skills and expertise with relevant and advanced tools and technologies to become and remain wanted in the job market in the new career domain. Remember, staying up to date is the key!
Skills Required For Transition From Software Developer to Data Scientist
Since both professions belong to computer science backgrounds, there are few common skills. Yet, the different focuses of both professionals require specific skill sets as well. Let us begin with learning the common ones before heading towards the latter required ones:
Common Skills Between Data Science and Software Engineering
Some of the commonly required skills are:
- Programming languages such as JavaScript, SQL, C++, Python, and R
- Libraries and frameworks
- Data structures and algorithms
- Version control
- Databases and SQL
- Software Development Life Cycle (SDLC)
- Algorithmic thinking
- Data handling
Skills Specific to Data Scientist Career
The candidates need to focus particularly on the following skills to provide quality results in meeting daily requirements:
- Statistics and probability
- Machine Learning
- Data analysis
- Domain knowledge
- Big data technologies
- Data mining and feature engineering
- Tools and libraries
- Advanced Mathematics
- Data modeling, analysis, and visualization
- Data science software programs like NoSQL, D3, Hadoop/Apache, JavaScript
Some additional soft skills crucial to a data science career are analytical thinking, strong teamwork, critical thinking, and business knowledge.
Projects to Work On When Transitioning From Software Developer to Data Scientist
One of the shortcuts to enter successfully in any new field is by gaining hands-on experience. Fields like Data Science come with numerous freely and easily available projects to gain hands-on experience. Here are some projects that you can work on to increase your chances of landing a job while standing ground for salary negotiation despite being fresher:
It is good to enhance your resume to show the practical application of Python. The project will require knowledge of the Deep Learning library Keras and the NLP toolkit NLTK, along with some other libraries. It is helpful for seeking jobs in business industries with a specific focus on customer service processes. You can also use it for customer reply analysis and mapping.
- Classifying breast cancer
Playing relevance in the healthcare industry, it can also work on Python language with dataset IDC or Invasive Ductal Carcinoma. It is of significance to learn prediction and forecasting preventive strategies. The project will provide experience in image analysis, getting familiarity with Convolutional neural networks, confusion matrices, and different Python packages and libraries.
Interested in working in the social media sector? Here is a project idea to showcase your expertise in R programming language. The candidate will be working on the janeaustenR dataset to understand and analyze people’s opinions, which is further of interest in prediction. It refers to understanding emotions and giving critical insights. You will be handling tidytext packages and lexicons.
- Credit card fraud detection
The banking sector-based project will provide familiarity with both Python or R or any of these programming languages of your choice. The data set to be used here will comprise credit card transactions. The project encompasses understanding customer’s expenditure methods and sites to identify fraudulent actions. You will be gaining familiarity with artificial neural networks. Decision trees and logistic regression.
It is another important sector in today’s scenario that promises exciting job opportunities. Here, you can experience Python and data set or package news.ccv. The project will require you to develop models with algorithms like PassiveAgreesiveClassifier and TfidfVectorier for separating the real news. You will get familiar with Jupyter Lab and Python libraries like scikit-learn, NumPy, and Pandas.
Role of Interview Kickstart in Career Transitioning
Interview Kickstart is an online platform harboring top industry experts from leading companies in the world. The experts, who are recruiting managers themselves, come together to create our courses. The aim is to provide the best training for interview preparation in the field of choice of the candidate.
Dealing with multiple IT and computer science-related courses, Data Science is among our leading courses. Having trained 17,000+ tech professionals and a record of $1.2M as the highest offer received by an IK alum, an IK course is all you need to land a successful career in tier-1 companies.
If you are willing to switch your career, our FREE webinar will provide insight into the program. Enroll now!
Frequently Asked Questions on Career Transition From Software Engineer to Data Scientist
Q1. Can a software engineer become a data scientist?
Ans. With the current advancement of technology and the passion of individuals, nothing is impossible. Career transition among different or same domains is also possible. Possession of common skill sets rather eases the transition journey.
Q2. Do data scientists work alone?
Ans. The data science field requires working on projects that are generally contributed to by different teams. Individuals with different sets of expertise and skills come together to execute a project. Hence, data scientists work more in collaboration or as a team rather than individually.
Q3. Can a person be both a software engineer and a data scientist?
Ans. The stated two professions belong to the same industries. Therefore, a person can practice both as a software engineer and a data scientist. Even possession of these skills increases one’s expertise and perspective to deal with problems in the domain.
Q4. Does Google or NASA hire data scientists?
Ans. Yes, besides these top sought-after institutions, multiple other multinational companies hire data scientists. Industries in every sector leverage the potential of data science using data scientists.
Q5. Can Indian data scientists work in the USA?
Ans. An Indian data scientist is permitted to work in the USA if they meet all the requirements, including expertise level and skills. They can work in the USA regardless of their country of origin to fulfill the company’s demands.
Q6. What are the different types of data science jobs?
Ans. The four different types of data science jobs are data analyst, Machine Learning engineer, data architect, database administrator, business intelligence analyst, and business intelligence developer, among others.
Q7. What are the four branches of data science?
Ans. The four branches of data science are data analytics, data mining, artificial intelligence, and machine learning. There are other branches as well, but the listed ones are more popular.
