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:
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.
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.
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:
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.
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.
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:
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.
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.
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!
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:
Some of the commonly required skills are:
The candidates need to focus particularly on the following skills to provide quality results in meeting daily requirements:
Some additional soft skills crucial to a data science career are analytical thinking, strong teamwork, critical thinking, and business knowledge.
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.
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.
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.
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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.
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.
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.
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.
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.
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.
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.