As big data technologies have been rising for a decade or two, the demand for data scientists in India has grown exponentially. The biggest reason is the drastic increase in unprocessed data all over the world. Data Science is not just a by-product of IT but has evolved more as an academic role that turns data into profit or efficient policies. If you’re wondering how to become a data scientist, you’ve come to the right place.
According to AIM research, the demand for data scientists has risen by 30.1% this year. Multiple corners of the industry have exposed data science, creating numerous career opportunities.
In this article, you will understand-
So let us begin by understanding what a data scientist actually does.
The answer to this question is complex, as data science is not just juggling data and finding solutions. It is a fusion of data, curiosity, creativity, tools, and understanding of the subject.
Becoming a data scientist requires knowledge about the industry and understanding the existing assumptions for clear and better results.
Data science is really useful for a company in its planning stage. It eventually helps in better management of problems, and political analysis, or gives solutions for any social problem or existing predictive models.
Data scientists are analytical experts who gather and analyze large amounts of data to solve problems or prove a point. They collect large sets of structured or unstructured data based on the subject and use various computer science, math, and statistics tools to interpret data for more comprehensible results.
To become a data scientist, a person must have strong analytical skills and knowledge of math, statistics, and programming languages. If the data in daily life makes sense to someone, they might have what it takes to become a data scientist.
The first step in becoming a data scientist is to obtain a degree in data science or a related field, such as computer science, mathematics, or statistics. A degree in data science will provide you with a solid foundation in the key concepts and techniques used in data science, including data analysis, data mining, statistical modeling, and machine learning.
If you’re interested in becoming a data scientist, there are several steps you can take to prepare yourself for this exciting and dynamic field. Here’s a detailed guide on how to become a data scientist:
Programming skills are essential for data scientists, as they are used to manipulate and analyze data, as well as develop models and algorithms. Some of the most used programming languages in data science include Python, R, Java, and SQL. It’s important to become proficient in at least one of these languages, as well as be familiar with other tools and technologies used in data science, such as Hadoop, Spark, and TensorFlow.
Data visualization is a critical skill for data scientists, as it allows them to communicate complex data insights in a clear and concise manner. You’ll need to be able to create visualizations that effectively convey your findings to stakeholders who may not have a technical background. Some of the most used data visualization tools include Tableau, Power BI, and D3.js.
Machine learning is a key area of data science, as it enables you to develop predictive models and other advanced analytics. You’ll need to be familiar with a variety of machine learning algorithms and techniques, including linear regression, logistic regression, decision trees, random forests, and neural networks. You should also be familiar with common machine learning frameworks, such as scikit-learn, Keras, and PyTorch.
Big data skills are essential for data scientists, as they are used to work with large and complex datasets. You’ll need to be familiar with distributed computing frameworks, such as Hadoop and Spark, as well as cloud computing platforms, such as AWS, Azure, and Google Cloud. You should also be familiar with data storage and retrieval technologies, such as SQL and NoSQL databases.
As a data scientist, you’ll need to be able to effectively communicate your findings and insights to stakeholders who may not have a technical background. This means being able to explain complex concepts in plain language, as well as being able to create visualizations and reports that effectively communicate your findings. You’ll also need to be able to collaborate effectively with other members of your team, as well as with stakeholders from other departments.
If you’re looking for an entry-level job in the field of data science, there are several steps you can take to increase your chances of success. Here are some tips on how to find an entry-level job in data science:
To land an entry-level job in data science, you need to have a strong foundation in technical skills like programming, machine learning, and data analysis. Consider taking online courses, attending workshops, or participating in data science competitions to build your skills and demonstrate your knowledge.
Employers in the field of data science are often looking for candidates who have practical experience working with real-world datasets. Consider creating a portfolio of projects that demonstrate your skills in data analysis, machine learning, or data visualization. To increase your chances of finding an entry-level job in data science, consider working on projects that demonstrate your skills and knowledge. Some ideas for projects could include:
Networking is a key component of any job search, and the field of data science is no exception. Attend conferences, meetups, and other events to connect with other professionals in the field, and consider joining online communities like LinkedIn or Kaggle to expand your network.
Look for entry-level positions in data science, data analysis, or data engineering, and apply to as many as you can. Don’t be discouraged if you don’t hear back from every application – finding a job in data science can take time, and persistence is key. In India, there are several job platforms that are ideal for finding data scientist jobs, including:
Preparing for a data scientist interview can be a challenging and nerve-wracking process, but with the right approach, you can increase your chances of success. Here are some tips on how to prepare for a data scientist interview:
The first step in preparing for a data scientist interview is to review the job description carefully. Make sure you understand the key responsibilities and requirements of the role, as well as the company culture and values.
Data science interviews often involve technical questions related to programming, machine learning, statistics, and data analysis. Be sure to brush up on these skills, and practice coding in languages such as Python, R, and SQL. Here are some sample interview questions:
In addition to technical questions, data science interviews often include behavioral questions to assess your problem-solving skills, communication skills, and teamwork abilities. Here are some examples of behavioral questions:
Before your interview, take the time to research the company and its mission, values, and culture. This will help you understand the company’s needs and how you can contribute to its success.
The more you practice, the more confident you will be during the interview. Consider doing mock interviews with friends or colleagues, and ask for feedback on your responses.
Related Read: How to become a software engineer
This online certification course is offered by IBM and provides learners with a comprehensive introduction to data science. The course covers a range of topics, including data analysis, data visualization, machine learning, and data ethics. Learners will complete a variety of hands-on projects using tools like Jupyter Notebooks, RStudio.
This certification course is offered by Microsoft and is designed for data scientists who work with Azure. The course covers topics such as data preparation, modeling, and deployment, and includes hands-on labs using tools like Azure Machine Learning and Azure Databricks.
This online certification course is offered by Google and is designed for learners who want to gain foundational skills in data analytics. The course covers a range of topics, including data analysis, data visualization, and data storytelling, and includes hands-on practice using tools like Google Analytics and Google Data Studio.
This certification program is offered by Harvard University and provides learners with a comprehensive introduction to data science. The course covers topics such as data wrangling, data visualization, statistical inference, and machine learning. Learners will complete a variety of hands-on projects using tools like R and Python.
This certification course is offered by Cloudera and is designed for data scientists who work with Hadoop and other big data technologies. The course covers topics such as data preparation, modeling, and deployment, and includes hands-on practice using tools like Apache Spark and Apache Hadoop.
Data Science and Data Analytics are two highly interconnected fields. In fact, they usually go hand-in-hand. However, the major points of difference are listed below
The average salary of a Data Scientist in India with less than a year’s experience starts from Rs 4.5 lakhs per annum. However, with experience, the salary growth is high in data science, where the salary with eight years of experience could be as high as Rs 25.8 Lakhs per annum on average.
There has been a considerable growth in the median salary of data scientists to Rs 16.8 lakhs annually, up by 25.4% in 2021. The number of data science professionals earning Rs 15-25 LPA and Rs 25-50 LPA bracket grew from 14.5% to 20.5% and 7.8% to 13.7%, respectively, against the growth in 2021. (Source: Economic Times)
In India, the data science industry is predicted to see enormous growth by 2026. the demand for data scientists in India is at an all-time high, and it has been expected that the field will create 11 Million job openings by 2026.
There is also a growing demand for experienced data scientists who have worked in other companies and have already gained experience creating new products and services. They may have worked on projects with a lot of data involved, which means they have the skills needed to handle vast amounts of data and use it to develop something new and innovative.
Data Scientists are in high demand across all industries. While there are many different types of companies, the following industries employ more data scientists than any other industry:
Healthcare is one of the largest employers of data scientists, with 1 in 4 employees working as data scientists. According to the Bureau of Labor Statistics, there will be 18,300 new jobs for data scientists by 2026.
SaaS (Software as a Service) companies need to collect large amounts of data from their customers, which requires data scientists to analyse and extract valuable information. These companies also have high employee turnover rates due to their fast-paced environment.
Financial services have been a hotbed for data scientists because they require a deep understanding of financial markets and their workings. This knowledge can only be gained through years of experience or education at a university-level program like an MBA or PhD program.
Here are some of the top recruiting companies for Data Scientists:
Amazon is usually looking for data scientists who have significant experience working with large datasets and who can help them use these datasets for making decisions on where to locate their headquarters or other important business decisions.
Microsoft has been hiring many people from across the globe since 2012, when it opened its first data centre in Amsterdam. The company has now set up more than 160 locations worldwide and is looking for people with expertise in machine learning, deep learning, artificial intelligence (AI), big data analytics and more so that they can help them improve their products.
IBM has an open job listing database where people can browse working on projects and available jobs and applies for them directly through their website. There are also several internships available at IBM’s Watson Research Center in New York City, where students can work on real-world problems while earning money while they complete their coursework.
While these are the industries where data scientists and data analysts are demanded, there are a few companies in India where data scientists can get lucrative offers. The companies are as follows:
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Not solely focused on IT, the job of a data scientist could be called an IT-enabled data management job. Unlike an IT job, where the main focus is on technological advancement and issues, data scientists work on enabling an organization with data management using IT skills.
They are a fusion of computer science, statistics, and math that discover any information from the given/collected data.
Yes, the job of a data scientist is not easy and comes with challenges. Even with all the skills like knowledge of computer science, statistics, and math, they could find themselves struggling due to increased competition in the field in recent years. Furthermore, the enormous size of data is another challenge many data scientists face, and thus they need sound data science skills to perform well on the job.
The demand for data scientists has increased exponentially in the past few years because of the growing data load. Everything is becoming computerized; thus, the data needs to be managed efficiently. According to Glassdoor, data scientists earn Rs 116100 per annum on average. Job opportunities are increasing, and there is no decline in sight.
Some data scientists have a background in formal, undergraduate, graduate and doctorate education in statistics, mathematics or computer science. This makes it easier for them to enter the data science domain as they already have most of the required skills. Many online and offline universities provide courses specifically related to data science.
1. Programming: Data scientists need to be well-versed in programming languages like Python, R, and SQL.
2. Statistical Analysis: Data scientists need to have a strong understanding of probability, data mining, and statistics.
3. Data Visualization: Data scientists need to be able to effectively visualize data to better communicate their findings.
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