How to Land and learn a Data Science by Internships


By iNeuBytes Content Writer


Data science is a rapidly growing field, and there is a high demand for data scientists across a wide range of industries. This means that there are many job opportunities available, and the demand for data scientists is expected to continue to grow in the future. According to Glassdoor, the average salary for a data scientist in the United States is $120,000 per year [1] and INR 10,25,000 per annum in India [2]. To land and learn, let us know more details about data science.

What is Data Science?

Data science[3] is an interdisciplinary field that involves the extraction of insights and knowledge from structured and unstructured data. It encompasses a wide range of techniques and tools from various fields, such as statistics, machine learning, computer science, and domain-specific expertise. Learning data science involves several steps, including data collection, cleaning, and pre-processing, data exploration and visualization, modelling, and deployment.

The process starts by collecting and gathering data from various sources, such as databases, social media, and sensors. Once the data is collected, it is cleaned and pre-processed to remove any errors or inconsistencies. After that, the data is explored and visualised to gain insights and understand patterns. Then, data scientists use various machine learning algorithms and techniques to develop models that can make predictions and automate decision-making. Finally, the models are deployed to be used in real-world applications.

Data science plays a vital role in many industries, such as finance, healthcare, retail, transportation, and more. It is used to make predictions, automate decision-making, and optimise processes. For example, in finance, data science is used for risk management; in healthcare, it is used for personalised medicine; and in retail, it is used for personalised marketing and recommendation systems. In this blog, we will discuss where to learn data science online, how to land and learn data science by internship for practical exposure, and finally, we will also discuss the benefits.

Free Best Resources to Learn Data Science Online:

Free online resources for learning data science can be an excellent way to get started in this field or to advance your existing skills. These resources include online courses, tutorials, videos, and forums that provide a wealth of information on data science concepts, tools, and techniques.

They can be a great way to learn at your own pace and on your own schedule, and they offer a wide range of options for all skill levels, from beginners to advanced learners. Additionally, many of these resources are interactive and provide hands-on experience with real-world data, which can be extremely beneficial for understanding the concepts and applying them to real-world problems. With the right approach and dedication, anyone can use these free resources to learn data science and start building a career in this exciting field.

Data Science Tutorials: Data Science Tutorials [4] is a popular YouTube channel that provides a wide range of data science tutorials, including tutorials on Python, R, and machine learning. The channel covers a variety of topics such as data cleaning, data visualization, and machine learning. It also provides great walkthroughs and explanations of the concepts. It has a large following and many positive reviews from viewers.

Sentdex: Sentdex [5] is a popular YouTube channel that provides a wide range of data science tutorials, including tutorials on Python, data visualization, and machine learning. The channel has a great approach to teaching; it's interactive and easy to follow, and it also provides a lot of resources and supplementary materials, making it a great channel for learning data science.

Data Science Dojo: Data Science Dojo [6] is a popular YouTube channel that provides a wide range of data science tutorials, including tutorials on Python, R, and machine learning, as well as interviews with data scientists. The channel covers a variety of data science topics with a great level of detail, and it provides a great approach to apply the concepts to real-world problems. It has a large following and many positive reviews from viewers. 
With these YouTube channel resources, you can surely learn the data science for free and for understanding the roadmap of data science.

Roadmap of Data Science:

Data science is a rapidly growing field that requires a diverse set of skills, including programming, statistics, machine learning, and data visualization. With so many different areas to learn, it can be difficult to know where to start and how to approach learning data science.

A roadmap for data science provides a clear path to follow, breaking down the field into manageable steps and helping you to focus on the most important concepts and skills. By following a roadmap, you can ensure that you are learning the right things at the right time and making steady progress towards your goals.

data science roadmap

Figure shows the roadmap of data science learning by iNeuBytes

  1. Basic Python: Start by learning the basics of Python, including data types, variables, loops, and functions.
  2. Statistics and Probability: Learn the fundamentals of statistics and probability, including descriptive statistics, probability distributions, and hypothesis testing.
  3. Advanced Python: Continue to learn more advanced Python concepts, such as object-oriented programming, error handling, and working with libraries like NumPy and Pandas.
  4. Data Visualization: Learn how to use data visualization libraries like Matplotlib, Seaborn, and Tableau to create effective visualizations of data.
  5. Machine Learning: Learn the basics of machine learning, including supervised and unsupervised learning, and linear and logistic regression.
  6. Data Manipulation: Learn how to clean, preprocess, and manipulate data using tools like Pandas and SQL.
  7. Deployment: Learn how to deploy models in production, including techniques for model versioning, monitoring, and scaling.
  8. Deep Learning: Learn about deep learning techniques, including neural networks and convolutional neural networks, and the corresponding libraries like TensorFlow, Pytorch.
  9. Projects: Work on data science projects to apply what you've learned and gain practical experience.
  10. Interview Preparation: Learn how to prepare for data science interviews and familiarize yourself with common data science interview questions.
  11. Placement: Begin applying for data science jobs.

Additionally, following a roadmap for data science learning given above figure by iNeuBytes helps to avoid wasting time in learning unimportant concepts and helps to stay on the learning track that to be mostly to make better progress. It also helps to align with the industry requirements, which makes it easy to secure a job in the industry.

How to Land and learn a Data Science by Internships?

Data science is a rapidly growing field, and it requires a combination of technical skills, mathematical knowledge, and business acumen. It's a very dynamic field, and data scientists must keep updating their knowledge and skills to stay relevant. Landing a data science internship in 2023 can be a competitive process, but with the right approach and preparation, you can increase your chances of success. Here are some tips to help you land and learn from a data science internship in 2023:

  1. Build your skills: One of the most important things you can do to increase your chances of landing a data science internship is to build your skills in data science and related fields. This includes learning programming languages such as Python and R, as well as data visualization and machine learning tools.
  2. Networking is key to finding a data science internship: Attend data science meetups and conferences, reach out to professionals in the field, and connect with alumni from your school who work in data science.
  3. Create a portfolio: A portfolio that showcases your data science projects and skills can help you stand out from other applicants. Make sure to include examples of your work, as well as any relevant statistics and metrics that demonstrate the impact of your projects.
  4. Learn about the company and their data science needs: Before applying for an internship, research the company and their data science needs. This will help you tailor your application and demonstrate how your skills and experience align with their requirements.
  5. Apply early: Many companies begin their recruitment process well in advance, so it's important to apply early to increase your chances of being considered.
  6. Be prepared for the interview: Prepare for the interview by researching the company and the role, as well as practising your answers to common data science interview questions.
  7. Be open to learn: Be open to learning during the internship; be curious, ask questions, and try to learn as much as possible from the people you are working with.

By following these tips, you can increase your chances of landing a data science internship in 2023. Remember, even if you don't land the internship you wanted, don't be discouraged; try again, and keep learning and improving your skills. Speaking about internships, iNeuBytes is providing virtual internships in the field of data science, where micro-assessments with a major project under mentor guidance will be great for starting a career. There are also various benefits to settling and learning a language.


[1] Data science Salaries, United States, Online available at :,14.htm , accessed on january 2023

[2] Data science Salaries, India, Online available at :,12.htm,            accessed on january 2023

[3] Sarker, I.H. Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN COMPUT. SCI. 2, 377 (2021). 

[4] Data Science Tutorials, Online available at :,  accessed on january 2023

[5] sentdex , Online available at :,  accessed on january 2023

[6] Data Science Dojo, Online available at :,  accessed on january 2023