Python for data science

Ali Mohsin
| July 18, 2022

Data Science Dojo has launched  Jupyter Hub for Computer Vision using Python offering to the Azure Marketplace with pre-installed libraries and pre-cloned GitHub repositories of famous Computer Vision books and courses which enables the learner to run the example codes provided.

What is computer vision?

It is a field of artificial intelligence that enables machines to derive meaningful information from visual inputs.

Computer vision using Python

In the world of computer vision, Python is a mainstay. Even if you are a beginner or the language application you are reviewing was created by a beginner, it is straightforward to understand code. Because the majority of its code is extremely difficult, developers can devote more time to the areas that need it.


computer vision python
Computer vision using Python

Challenges for individuals

Individuals who want to understand digital images and want to start with it usually lack the resources to gain hands-on experience with Computer Vision. A beginner in Computer Vision also faces compatibility issues while installing libraries along with the following:

  1. Image noise and variability: Images can be noisy or low quality, which can make it difficult for algorithms to accurately interpret them.
  2. Scale and resolution: Objects in an image can be at different scales and resolutions, which can make it difficult for algorithms to recognize them.
  3. Occlusion and clutter: Objects in an image can be occluded or cluttered, which can make it difficult for algorithms to distinguish them.
  4. Illumination and lighting: Changes in lighting conditions can significantly affect the appearance of objects in an image, making it difficult for algorithms to recognize them.
  5. Viewpoint and pose: The orientation of objects in an image can vary, which can make it difficult for algorithms to recognize them.
  6. Occlusion and clutter: Objects in an image can be occluded or cluttered, which can make it difficult for algorithms to distinguish them.
  7. Background distractions: Background distractions can make it difficult for algorithms to focus on the relevant objects in an image.
  8. Real-time performance: Many applications require real-time performance, which can be a challenge for algorithms to achieve.


What we provide

Jupyter Hub for Computer Vision using the language solves all the challenges by providing you an effortless coding environment in the cloud with pre-installed computer vision python libraries which reduces the burden of installation and maintenance of tasks hence solving the compatibility issues for an individual.

Moreover, this offer provides the learner with repositories of famous books and courses on the subject which contain helpful notebooks which serve as a learning resource for a learner in gaining hands-on experience with it.

The heavy computations required for its applications are not performed on the learner’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.

Listed below are the pre-installed python libraries and the sources of repositories of Computer Vision books provided by this offer:

Python libraries

  • Numpy
  • Matplotlib
  • Pandas
  • Seaborn
  • OpenCV
  • Scikit Image
  • Simple CV
  • PyTorch
  • Torchvision
  • Pillow
  • Tesseract
  • Pytorchcv
  • Fastai
  • Keras
  • TensorFlow
  • Imutils
  • Albumentations


  • GitHub repository of book Modern Computer Vision with PyTorch, by author V Kishore Ayyadevara and Yeshwanth Reddy.
  • GitHub repository of Computer Vision Nanodegree Program, by Udacity.
  • GitHub repository of book OpenCV 3 Computer Vision with Python Cookbook, by author Aleksandr Rybnikov.
  • GitHub repository of book Hands-On Computer Vision with TensorFlow 2, by authors Benjamin Planche and Eliot Andres.


Jupyter Hub for Computer Vision using Python provides an in-browser coding environment with just a single click, hence providing ease of installation. Through this offer, a learner can dive into the world of this industry to work with its various applications including automotive safety, self-driving cars, medical imaging, fraud detection, surveillance, intelligent video analytics, image segmentation, and code and character reader (or OCR).

Jupyter Hub for Computer Vision using Python offered by Data Science Dojo is ideal to learn more about the subject without the need to worry about configurations and computing resources. The heavy resource requirement to deal with large Images, and process and analyzes those images with its techniques is no more an issue as data-intensive computations are now performed on Microsoft Azure which increases processing speed.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Jupyter Notebook Environment dedicated specifically for it using Python. Install the Jupyter Hub offer now from the Azure Marketplace, your ideal companion in your journey to learn data science!

Try Now!

Albar Wahab
| June 22, 2022

Look into data science myths in this blog. The field of Data is an ever-growing field and often you’ll come across buzzwords surrounding it. Being a trendy field, sometimes you will come across statements about it that might be confusing or entirely a myth. Let us bust these myths, and ensure your doubts are clarified!

What is Data Science?

In simple words, data science involves using models and algorithms to extract knowledge from data available in various forms. The data could be large or small or could be structured such as a table or unstructured such as a document containing text and images containing spatial information. The role of the data scientist is to analyze this data and extract information from the data which can be used to make data-driven decisions.

data science myths, data science compass
The Flawed Data Science Compass


Now, let us dive into some of the myths:

1. Data Science is all about building machine learning and deep learning models

Although building models is a key aspect, it does not define the entirety of the role of a Data Scientist. A lot of work goes on before you proceed with building these models. There is a common saying in this field that is “Garbage in, garbage out.” Real-life data is rarely available in a clean and processed form, and a lot of effort goes into pre-processing this data to make it useful for building models. Up to 70% of the time can be consumed in this process.

This entire pipeline can be split up into multiple stages including acquiring, cleaning, and pre-processing data, visualization, analyzing, and understanding it, and only then are you able to build useful models with your data. If you are building machine learning models using the readily available libraries, your code for your model might end up being less than 10 lines! So, it is not a complex part of your pipeline.

2. Only people with a programming or mathematical background can become Data Scientists

Another myth surrounding is that only people coming from certain backgrounds can pursue a career in it, which is not the case at all! Data science is a handy tool that can help a business enhance its performance in almost every field.

For example, human resources is a field that might be distant from statistics and programming, but it has a very good implementation of data science as a use case. IBM, by collecting employee data, has built an internal AI system that can predict when an employee might quit using machine learning. A person with domain knowledge about the human resource field will be the best fit for building this model.

Regardless of your background, you can learn it online with our top-rated courses from scratch. Join one of our top-rated programs including Data Science Bootcamp and Python for Data Science and get started!

Join our Data Science Bootcamp today to start your career in the world of data. 

3. Data Analysts, Data Engineers, and Data Scientists all perform the same tasks

Data Analysts and Data Scientists roles have overlapping responsibilities. Data analysts carry out descriptive analytics, collecting current data and making informed decisions using it. For example, a data analyst might notice a drop in sales and will try to uncover the underlying cause using the collected company data. Data Scientists also make these informed business decisions. However, they involve using statistics and machine learning to predict the future!

Data Scientists use the same collection of data but use it to make predictive models that can predict future decisions and guide the company on the right actions to take before something happens. Data engineers on the other hand build and maintain data infrastructures and data systems. They’re responsible for setting up data warehouses and building databases where the collected data is stored.

4. Large data results in more accurate models

This myth might be partially wrong but partially right as well. Large data does not necessarily translate to higher accuracy of your model. More often, the performance of your model depends on how well you carry out the cleaning of your dataset and extraction of the features. After a certain point, the performance of your model will start to converge regardless of how much you increase the size of your dataset.

As per the saying “garbage in, garbage out”, if the data you have provided for the model is noisy and not properly processed, likely, the accuracy of the model will also be poor. Therefore, to enhance the accuracy of your models, you must ensure that the quality of the data you are providing is up to the mark. Only a greater quantity of relevant data will positively impact your model’s accuracy!

5. Data collection is the easiest part of data science

When learning how to build machine learning models, you would often go to open data sources and download a CSV or Excel file with a click of a button. However, data is not that readily available in the real world and you might need to go to extreme lengths to acquire it.

Once acquired, it will not be formatted and in an unstructured form and you will have to pre-process it to make it structured or meaningful. It can be a difficult, challenging, and time-consuming task to source, collect and pre-process data. However, this is an important part because you cannot build a model without any data!

Data comes from numerous sources and is usually collected over a period by using automation or manual resources. For example, for building a health profile of a patient, data about their visits will be recorded. Telemetry data from their health device such as sensors can be collected and so on. This is just the case for one user. A hospital might have thousands of patients they deal with every day. Think about all the data!

Please share with us some of the myths that you might have encountered in your data science journey.

Want to upgrade your data science skillset? checkout our Python for Data Science training. 

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