Introduction
The global talent shortage is providing a fantastic opportunity for newcomers, grads, and software professionals wishing to transfer careers. Data science will change several industries, including yours. Today, quintillions of bytes of data are produced each day around the world. Social media postings, online purchases, and digital movies make up a small portion of them. Even the images were taken with cell phones to the sensors in malls. There is no denying that data science nowadays is essential to an organization's success. This is responsible for the incredible growth in the number of data science-related occupations. Despite the growing demand for data scientists, there is a gap between the supply of skilled practitioners and the demand. Read below to know which data science career track will be useful for you to promote your career growth.
Data Science Career Track Profiles For Career Transition
There is a similarity in the job roles described related to data science. The applicant might not be able to distinguish between the various duties associated with data science jobs. Although most recruiters use the right description. Due to the mismatch between the job description and the work role, the applicant may mistakenly apply for the wrong positions and miss out on potential opportunities.
Find your ideal position
In order to address interesting business issues, data science is applied across a number of domains (including marketing, finance, HR, etc.). Selecting a data science job title inside your industry is your first step. Let us give you an illustration of this.
Go ahead and assume that you are a digital marketer who wants to switch to data science. The following job names appear in a list of job posts when you search "marketing data science jobs":
- Senior data scientist - marketing
- Senior marketing data analyst
- Seminar in marketing analytics
Now review each job description to determine which one most closely aligns with your existing skill set (in terms of domain knowledge).
Even in this rapidly expanding industry, there is a misconception about job roles. Due to a lack of clarity on job responsibilities, data science transitioners risk losing their ideal position.
Below are the few data science careers tracks that are in high demand
- Data Analyst
- Data Scientist
- Data Engineer
- Data Architect
- Statistician
- Business Analyst
- Database Administrator
- Data and Analytics Manager
The field of data science is still in the development stage. Many data scientists and machine learning engineers have not begun their careers in this direction. They transitioned from other fields, much like we did, and possibly a lot of you who are reading this. We'll discuss the tools and abilities you'll need to make the shift to data science, as well as any obstacles you might encounter and how to deal with them.
Join a course that provides training on any of the aforementioned skills. This is the pivotal moment for the majority of data science transitioners. You have worked hard to develop both your technical and soft skills, and you now believe that you are ready to enter the data science industry.
Essential Technical Skills for Data Science Transition
You must learn (and master) several critical skills if you want to succeed in data science. Many skills are frequently transferable between domains, albeit they may vary depending on your role and the project.
- Discrete mathematics programming, Mathematics and Statistics Linear algebra, inferential statistics, differential calculus, and calculus.
- Regular Expressions, Control Structures, Managing Dataframes, Loop Function, Getting and Giving Data putting machine learning algorithms into practice
- Hadoop Ecosystem (Flume, Pig, Hive, Sqoop), Apache Spark, Big Data Lakes, Spark MLLib, No SQL.
- SAP BI, Tableau, Business Intelligence Oracle Fusion, SQL, and Microsoft Power BI.
- Data Engineering/Big Data.
- Machine learning, Regression, dimensionality reduction, classification, feature engineering, segmentation, training, and model deployment are all available with Scikit-Learn,
- Contemporary machine learning: TensorFlow, Deep Neural Networks, Autoencoders, Reinforcement Learning, Keras Learning, Artificial neural networks, Convolutional Neural Networks
Will Learning Programming Help You Get A Job In Data Science?
The fact is that no single data science strategy is effective for everyone. There has previously been a discussion of the many roles that are available in this field. Depending on the demands of each role as well as the project or business you are working for, a different skill set is required.
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Is Your Previous Data Science Work Experience Helpful?
To find a job in data science, you should make every effort to put the effort you have put in to reach where you are today to good use. You must understand, though, that it could not always be the case for you.
The data science sector will only consider your prior work experience if it is pertinent to this topic or if you are coming from a related industry. If you are fully changing fields, it is unlikely that your prior work experience will matter. Changing to data science, for instance, after years in software testing Not only are you changing careers, but you are also looking for a new job. When a recruiter looks over your resume, their initial thought is, "What value can he/she add to the organization/project?"
Conclusion
If you are a newbie, your odds of passing an interview for a position in data science are reduced. Serious newcomers, however, possess a considerable competitive advantage. Many tech companies prefer data scientists who are self-made and self-taught due to their knack for hard work and quick learning.You must first thoroughly examine the most recent developments in the field of data science. Read blogs and watch YouTube videos to learn about the principles of data science, including data wrangling and building data pipelines. Connect with data science experts on job-focused platforms like LinkedIn. To learn more, enroll in a data science course offered by The IoT Academy. It will help you in your career transition and choose the stream you prefer.
