Opportunities in this domain – applications for both real world and career development, will continue to grow rapidly. Data science is ranked #2 in the top 20 in-demand skills, and according to Glassdoor data, in 2021 alone there will be more than 37,000 new jobs for machine learning engineers, data analysts, data scientists and business analysts, among other roles. Offer viewed.
Data Science has become a growth catalyst for many fields over the years. It has enhanced business operations and helped organizations to access massive amounts of data for better decision making. The high demand and myriad applications of the domain offer career growth beyond traditional roles and this has made it a very promising career prospect.
With the onset of the COVID-19 pandemic, data science has become an essential tool for tackling real-world problems for various sectors such as health, entertainment, retail, finance, supply chain, transportation, and much more. Over the years, Data Science has evolved from encompassing only analytics and statistics to empowering decision making, predictions and actions as well. It has equipped us with the scientific capabilities to better predict and ultimately influence future outcomes based on past data.
Opportunities in this domain – applications for both real world and career development, will continue to grow rapidly. Data science is ranked #2 in the top 20 in-demand skills, and according to Glassdoor data, in 2021 alone there will be more than 37,000 new jobs for machine learning engineers, data analysts, data scientists and business analysts, among other roles. Offer viewed.
The ever-increasing demand for data science experts has not been met due to a lack of skilled and industry-ready candidates to harness the power of data. It is recommended for young professionals to understand and develop the skills required for various data science roles so that they can tap into this high-demand domain and build a successful career.
Here are some essential tips to start a career in data science:
1. Choose the Right Role
Working in the data science domain can include a wide variety of roles ranging from data analysts, data scientists, data visualization specialists, data engineers, machine learning specialists, and more. When starting a career at Datascape, it is important to land a role that aligns with your skill set, educational background, work experience, and interests.
2. Update your skill set with courses
After deciding on a role, the next step is to learn the nuances of that area and understand it deeply, which will help you move forward in that role. A great way to learn these is by engaging with real-world case studies through programs and courses on platforms like Great Learning. The platform presents learning in a unique blended format with live and recorded classes, combining an immersive classroom experience with online learning. Practical learning gives learners the opportunity to acquire job-ready skills through projects, assignments and real-life problem-solving scenarios.
3. Choose a Data Science Tool/Language to Learn
The selection of tools/languages can be a fundamental problem for data science enthusiasts. The answer is to choose a mainstream tool/language to start a career in Data Science. To help with this, Great Learning offers an array of certificate and degree programs in data science, ranging from basics like tool/language selection to advanced post-graduate degrees.
4. Find and Join Peer Groups
Finding like-minded peers who are interested in the same field as you will help you stay motivated and discover your greater potential in the field. Additionally, co-workers can provide a fresh perspective to your problem-solving efforts, as well as enable networking avenues that facilitate a meaningful career journey. You can also find the right peer network for you through platforms like Great Learning.
5. Focus on practical applications
Practical and practical education is more important than just theoretical knowledge as it allows you to gain practical experience for more meaningful career outcomes. Real-life applications also add to the repertoire of a data science professional with their current skill set. It is only when you begin to actively contribute to applicable use cases that your relevance and value creation within an organization mix.
6. Learn Communication and Soft Skills
A data science professional can wear many hats. Modeling for forecasting or pattern formation requires a lot of critical thinking, from exploring the data to getting some initial insight. This often then leads to the ability to better communicate the insights to key stakeholders so that they can make informed decisions. To become a full-fledged professional, you also need to develop the right kinds of soft skills, such as critical thinking, listening, persuasive communication and problem-solving abilities.
Mistakes and challenges for data scientists
Given the various processes and capabilities required, it is hard for a data scientist to avoid making mistakes on the way to a successful career. Some of these mistakes include learning too many tools/languages at once, not paying enough attention to the fundamentals, giving low priority to data visualization and data exploration, not using a structured approach to problem-solving, and most commonly, Including not staying updated with the trends. In area. These are the internal challenges faced by most of the individuals.
From an outside perspective, the challenges faced by data scientists include collaboration, especially when working on projects. This can present a serious challenge as the job of a Data Scientist demands them to interact with multiple individuals at once. They need to analyze vast amounts of existing data and often get to the point where they get stuck. It is common for companies to have different stored data structures and inconsistent data formats, making it difficult for data scientists to validate data. Thus, data integrity is often a challenge from an external perspective.
The job market and the future of data science
Today, the demand for data science is huge, with many entrepreneurs putting significant amounts of time and money into it. A job and average salary data repository platform, in fact, note that data scientists in USA receive an average base salary of USD 141,804 (INR 1,10,89,966) per year, and Indian data scientists INR per year 8,24,562 is obtained. The future of data science rests on dynamic developments in Big Data, Machine Learning and Artificial Intelligence. These cutting-edge fields give professionals with the latest skills an upper hand in today’s new-age job market, powering their careers with the data-driven skills of the future.
Highlighting the brand ethos and pedagogy, a spokesperson for Great Learning said, “Quality education with an industry-relevant curriculum is the need of the hour. Great Learning understands the impact of these current gaps and brings in experienced board faculty who make teaching and stay up-to-date with the latest developments. To maximize learners’ takeaways from their journey with us, we use a mentored learning approach to provide individualized attention to learners The modern world needs professionals with industry-ready skills who can unlock the potential of data, and we aim to create these leaders of tomorrow with a holistic learning approach to unleash the maximum potential of each learner. I have to help.”
Start your career with the best education
Part of the BYJU’S GROUP, GREAT LEARNING is a leading global ed-tech company for professional and higher education. The platform is on a mission to enable transformational learning and career success in the digital economy for professionals and students around the world and has so far impacted over 5 million learners from 170+ countries.
