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 Docker has been very popular service in the IT industry. Let’s find out how it helps Data Scientists. Environment Reproducibility:  Docker provides an easy way to package all the dependencies required for a specific data science project into a single container. This allows data scientists to create a standardized environment that can be easily replicated across different machines and platforms, ensuring that the code runs the same way everywhere. Portability:  Docker containers are portable and can be easily moved between machines and platforms. This means that data scientists can easily share their work with others and deploy their models into production environments with minimala effort. Version Control:  Docker images can be versioned just like code, allowing data scientists to easily roll back to a previous version if needed. This can be especially useful when working with large datasets and complex models that require frequent updates. Scalability:  Docker...

Did you know that even though you have a great Data Science Project, you may still get rejected? Here are five ways you can explain your Data Science Project in better way. High-level overview Provide a brief summary of what the project is about, what problem it aims to solve, and what data sources are being used. This approach can be useful for non-technical stakeholders who want a broad understanding of the project. Technical explanation Provide a more detailed explanation of the algorithms and models being used, the data processing techniques involved, and any other technical aspects of the project. This approach is useful for technical stakeholders who want to understand the inner workings of the project. Business impact Explain how the project will impact the business, whether it's by reducing costs, increasing revenue, or improving customer satisfaction. This approach is useful for stakeholders who are interested in the business outcomes of the project. Visual aids Use visual...

 Did you know that you can visualise data that has higher dimensions? We can do it using TSNe. TSNe stands for t - distributed stochastic neighbour embedding What is TSNe? TSNe is a statistical method that is used to visualise high dimensional data by giving each datapoint a location on two or three dimensional map. It is a non linear dimensional reduction technique. How TSNe works? First it constructs a probability distribution over pairs of high dimensional objects. Similar objects are provided high probability and dissimilar objects are provided low probability. TSNe then defines a point on low dimensional map for similar objects. When to use TSNe? Usually with big data we have large number of columns or parameters associated with the data. Representing such data on two or three dimensional map is impossible. We cannot always go for deleting columns as it would lose all of its value then. In such cases we can use TSNe to repsent visually without losing much value. How to use TSN...

 Many people have heard about ChatGPT and now even many important people are also using it. To make this blog more interesting I have also used ChatGPT to get information So I logged on the ChatGPT website and asked the following questions: 1.  I want to write a blog to tell people about you. That's great! I'm glad you're interested in learning more about me. As a language model, I am a program that is able to generate text based on a given prompt or input. I am trained on a large dataset of text, allowing me to understand and respond to a wide range of topics. If you have any specific questions about my capabilities or how I work, I would be happy to help. 2. Can you tell me example of datasets that you were trained on? I was trained on a diverse range of text data which includes books, articles, websites and more, these dataset are called Common Crawl, and WebText. The training data is sourced from a variety of websites and books, it's a diverse dataset that represent...

For beginners in Data Science who have already learned about Supervised learning and unsupervised learning have surely passed through the name Reinforcement Learning. In this blog you will get to know what Reinforcement is, and important things you should know about it. What is Reinforcement Learning? In simple words Reinforcement Learning is training a model by going though a again and again and getting better at predicting by reward system. So an initial stage is there and a final stage is there. The final stage has reward. The model needs to go from initial stage to final stage. Model will start with trial and error. As it reaches the final stage it has set of movement or path. Next time it will try to find out more efficient way to reach its final stage. After a set of iterations the most efficient way is finalised and treated as final prediction. Robotics Implementation         The most relatable and practical application of Reinforcement Learning is in Rob...

Did you know that data science is so viral that right now people from every field and background wants to be part of it? By the end of this blog, you will get to know why Data Science is going viral. 1.  JOB OPPORTUNITIES After the pandemic, there is a major change in the way people are working and how people are working. Majority of job now don't give you security. People are finding IT jobs more reliable. They are finding work from home more manageable.      Out of these there is a lot of Vacancies in Data Science and majority of people have successfully switched to Data Science (including me). People do ask me also from various field if they could also switch. Sure some learning is required, after which a job opportunity is always waiting for you. 2.  SOCIAL MEDIA FEATURES Nowadays, some very fascinating features are now available on different social medias. If we talk about Snapchat - we have different filters available. On Instagram we are able to get diffe...

Did you know that data science is actually more fun than it is a field of study and getting a job in it? By the end of this blog, you will get to know some fun things you can do with data science. 1.  PREDICTING ANYTHING Data Science allows you to predict anything. Literally, anything required that you have the previous data. From weather forecast to whether a team will win the match or not. It's amazing we say that past can define your future. Data Science is actually doing it. If something happens twice or more times consistently, we also make a prediction that it will happen again. Data Science does the same. Just providing the basis for mathematical equations and theorems. 2.  RECOGNISING IMAGES  Recognizing images is one of the most revolutionary things done by data science. It plays a very vital role in curating your social media feed that we will discuss later. One of the most beneficial merits is that it is used in medical industries. It has the power to iden...