Are you a young professional looking for guidance on how to succeed in the Indian job market? As you enter the workforce, it's essential to equip yourself with skills that will help you stand out among other candidates. That's where data science tools come in handy.
Data science tools are becoming increasingly important in today's job market and can unlock your career potential. In this article, we'll take a closer look at how these powerful tools can boost your career prospects.
Understanding Data Science
Data science allows you to extract meaningful insights from raw data and make informed decisions. It combines statistical analysis, machine learning, and computer science to reveal hidden patterns in data, predict future trends and events, and provide valuable business intelligence.
You can benefit from data science tools regardless of your industry or job role. Data visualisation is a crucial aspect of data science because it enables you to present complex findings in a manner that is easy to understand.
Understanding Data Science Tools
As a young professional looking for career growth opportunities, understanding the functions of different data science tools is essential.
Here are ten tools that you should know:
1. Python is a programming language used for data analysis and visualisation.
2. R is another programming language frequently used for statistical computing.
3. Tableau is a business intelligence software that enables users to create interactive dashboards, reports, and charts.
4. Hadoop is an open-source framework used for big data processing.
5. SAS is a software suite used for advanced analytics.
6. Spark is an open-source engine designed for big data processing.
7. Apache Flink is another open-source tool that processes large amounts of streaming data.
8. TensorFlow is an open-source library used for building machine learning models.
9. Pandas is a powerful toolkit used for cleaning and manipulating complex datasets.
10. NumPy is an open-source extension tool for Python that mainly deals with arrays.
Job Roles that Require Data Science Tools
There are four main roles within data science, each with specific requirements in terms of skill set and tool knowledge.
- Data analysts are responsible for collecting, analysing, and interpreting large sets of structured or unstructured datasets using analytical tools such as Excel, SQL, or R.
- Data scientists work on large-scale problems, handle complex datasets, and develop machine learning models. They require knowledge of languages like Python or R, along with machine learning platforms like Tensorflow or Scikit-Learn.
- Data Engineers: Build the infrastructure necessary to store, process, and manage large amounts of data and need data visualisation tools like Tableau and the Hadoop framework.
- Data architects are responsible for designing the overall structure of an organisation's data architecture and also need data visualisation tools like Tableau and the Hadoop framework.
The salary range for each of these roles varies depending on the company and its location. On average, a data analyst can earn 5-8 lakhs per year, while a data scientist can earn 8–15 lakhs per year.
Data engineers make around 7–14 lakhs per year, while data architects can earn upwards of 12 lakhs per annum.
Job Potential and Career Paths
The data science field is expanding rapidly and has great potential for growth in India. With increasing digitalization, the demand for professionals who can analyse data is also on the rise.
According to industry experts, by 2025, India will have a requirement of over 1.5 lakh data professionals. With this ever-increasing demand, there are numerous career paths that one can choose from based on their interests and skills.
The Indian market has seen an increase in the number of job openings for data scientists, with almost all major industries looking to hire people with these skills.
By 2030, the McKinsey Global Institute predicts that India will need up to 200k data scientists to meet current demands. Hence, it is safe to say that individuals equipped with relevant data science tools and knowledge can look forward to lucrative careers.
Education and Certification Options
Data science is a vast field that requires a combination of practical experience and theoretical knowledge. Here are some educational options available:
1. University degrees available with average fees:
- Bachelor’s degree: ₹2–5 LPA
- Master’s degree: ₹4-13 LPA
2. Certification programmes offering specialised skills with average fees:
- Data Science Fundamentals: ₹40k–₹80K
- Machine Learning Algorithms: ₹50k–₹1L
- Big Data Technologies: ₹60K–₹1.2L
3. Online courses:
There are various online platforms that offer online courses on particular tool functions, like Tableau or Python. The cost of completing these courses varies widely between ₹50k to ₹2 lakhs on average.
Upskilling Opportunities and Industry Growth
In the data science industry, upskilling is crucial to staying on top of industry developments. Therefore, it is important for professionals to continuously update their skill set.
AI and machine learning are emerging technologies that have helped automate various tasks previously done by humans. With increasing developments in these fields, the use of data science tools is expected to become more sophisticated.
Upskilling programmes like boot camps and certification courses can help professionals keep track of such changes while staying relevant in their respective fields.
FAQs
1. How much coding is required for data science?
To be an effective data scientist, you need to have a strong foundation in coding. However, the amount of coding required may vary depending on the specific job role and industry sector.
2. Is a master’s degree necessary to get a job in data science?
While having a master's degree can increase your chances of securing a high-paying job in data science, it is not always necessary. Relevant work experience and certifications can also make you a competitive candidate.
3. Which data science tool is best for high-level computations?
Python is one of the most popular and powerful programming languages used by data scientists for high-level computations.
4. How much data is enough for data science?
The amount of data required for effective analysis depends on the task at hand and its complexity. However, as little as 50MB of well-curated and structured data could be sufficient.
5. Can WIZR help me choose between data science certification programmes?
Yes, WIZR provides reliable guidance on different certification programmes available in the market, along with financing options that suit your budget and needs without any hassle!
The data science industry is continually evolving, and learning new technologies and tools will keep you ahead of the curve. The demand for data-related skills is growing, with a 29% increase in demand for data analysts alone reported by LinkedIn.
Choosing the right beginner's course and upskilling depends on your interests, experience level, and budget.
WIZR can help you with both: choosing the right course to upskill your data science knowledge and providing financial assistance to fund your education.
