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Taking Your Data Science Learning to The Next Level
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Taking Your Data Science Learning to The Next Level

Taking Your Data Science Learning to The Next Level – Data science is a hot topic right now, and for a good reason. Brands are aware that they have a large amount of untapped data, and there is a significant probability that they are not capitalizing on important insights about their business. Almost all major industry sectors are recruiting data science specialists to assist them in gaining meaningful insights from large data. The increased demand for data scientists has led to the growth of training institutions offering a variety of courses on data science.

This blog focuses on how to advance your data science learning and should help you make a better-educated decision regarding your learning path.

Career Progression in Data Science

There are two career pathways for any technical profession, including data science: management and individual contributor. In an individual contributor’s capacity, data scientists work on core projects, contribute code, do analyses, and construct ETL pipelines and machine learning models. Data scientists on the management path manage people, grow data strategy, and focus on putting the parts of a data organization together.

Individual contributors can choose to become managers or stay highly specialized data scientists as their careers progress. Nowadays, businesses expect data scientists to have a larger range of abilities and more cohesive specialization and teamwork.

To advance in the careers, both the organization and the individual must emphasize learning and development. It should be tailored to an individual’s personal needs and correlate to their strengths and weaknesses across data scientists’ fundamental skills and competencies: analytical ability, technical ability, problem formulation, influence, and communication.

Also, increasing one’s grasp of one subject area (say, fraud analytics) can help to make a bigger effect. A data scientist should be able to design frameworks that can help deepen understanding and drive scalability with this improved skill.

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Advance skills in the field of data science

To advance your data science learning, you can begin trying to acquire or master the following skills:

  • Modelling & statistics that include machine learning, statistical modelling, and model deployment.
  • Data Engineering includes data manipulation, distributed computing, and database administration.
  • Communications & Expertise that include giving insights, industry knowledge, and leading and mentoring junior employees.
  • Product management skills
  • Project management skills

Data scientists have a wide range of job opportunities. Data Science, being an interdisciplinary career, provides exposure and exit choices in a variety of fields. The data scientists concentrate on one specific area of engineering, business, or product. Ensuring that the necessary knowledge and abilities are developed opens the door to advancing towards these professions.

Final Thoughts

What skills you need to improve are determined by your job aspirations and general interests. As a result, it’s difficult to say whether you should focus on programming, statistics, project management, or business intelligence. It is important to note that this is on-the-job lifelong learning. However, focus on just one or two advanced skills at a time and devote a few hours per week to develop them. This will eventually build an in-demand skill set that will help you stand out from the crowd and increase your chances of getting the next data science position or promotion you apply for.

With interest in data science expanding year after year, several institutes/colleges are now providing PG Diploma in Data Science. Great Learning, for instance, provides a wide choice of courses on data science to help you accelerate your learning and professional growth.

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