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Python for data science

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Ayesha Saleem
| April 4

Are you interested in learning Python for Data Science? Look no further than Data Science Dojo’s Introduction to Python for Data Science course. This instructor-led live training course is designed for individuals who want to learn how to use Python to perform data analysis, visualization, and manipulation. 

Python is a powerful programming language used in data science, machine learning, and artificial intelligence. It is a versatile language that is easy to learn and has a wide range of applications. In this course, you will learn the basics of Python programming and how to use it for data analysis and visualization. 

Learn the basics of Python programming and how to use it for data analysis and visualization in Data Science Dojo’s Introduction to Python for Data Science course. This instructor-led live training course is designed for individuals who want to learn how to use Python to perform data analysis, visualization, and manipulation. 

Why learn Python for data science? 

Python is a popular language for data science because it is easy to learn and use. It has a large community of developers who contribute to open-source libraries that make data analysis and visualization more accessible. Python is also an interpreted language, which means that you can write and run code without the need for a compiler. 

Python has a wide range of applications in data science, including: 

  • Data analysis: Python is used to analyze data from various sources such as databases, CSV files, and APIs. 
  • Data visualization: Python has several libraries that can be used to create interactive and informative visualizations of data. 
  • Machine learning: Python has several libraries for machine learning, such as scikit-learn and TensorFlow. 
  • Web scraping: Python is used to extract data from websites and APIs.
Python for data science
Python for Data Science – Data Science Dojo

Python for Data Science Course Outline 

Data Science Dojo’s Introduction to Python for Data Science course covers the following topics: 

  • Introduction to Python: Learn the basics of Python programming, including data types, control structures, and functions. 
  • NumPy: Learn how to use the NumPy library for numerical computing in Python. 
  • Pandas: Learn how to use the Pandas library for data manipulation and analysis. 
  • Data visualization: Learn how to use the Matplotlib and Seaborn libraries for data visualization. 
  • Machine learning: Learn the basics of machine learning in Python using sci-kit-learn. 
  • Web scraping: Learn how to extract data from websites using Python. 
  • Project: Apply your knowledge to a real-world Python project. 


Python is an important programming language in the data science field and learning it can have significant benefits for data scientists. Here are some key points and reasons to learn Python for data science, specifically from Data Science Dojo’s instructor-led live training program:
 

  • Python is easy to learn: Compared to other programming languages, Python has a simpler and more intuitive syntax, making it easier to learn and use for beginners. 
  • Python is widely used: Python has become the preferred language for data science and is used extensively in the industry by companies such as Google, Facebook, and Amazon. 
  • Large community: The Python community is large and active, making it easy to get help and support. 
  • A comprehensive set of libraries: Python has a comprehensive set of libraries specifically designed for data science, such as NumPy, Pandas, Matplotlib, and Scikit-learn, making data analysis easier and more efficient. 
  • Versatile: Python is a versatile language that can be used for a wide range of tasks, from data cleaning and analysis to machine learning and deep learning. 
  • Job opportunities: As more and more companies adopt Python for data science, there is a growing demand for professionals with Python skills, leading to more job opportunities in the field. 


Data Science Dojo’s instructor-led live training program provides a structured and hands-on learning experience to master Python for data science. The program covers the fundamentals of
Python programming, data cleaning and analysis, machine learning, and deep learning, equipping learners with the necessary skills to solve real-world data science problems.  

By enrolling in the program, learners can benefit from personalized instruction, hands-on practice, and collaboration with peers, making the learning process more effective and efficient 

Some common questions asked about the course 

  • What are the prerequisites for the course? 

The course is designed for individuals with little to no programming experience. However, some familiarity with programming concepts such as variables, functions, and control structures is helpful. 

  • What is the format of the course? 

The course is an instructor-led live training course. You will attend live online classes with a qualified instructor who will guide you through the course material and answer any questions you may have. 

  • How long is the course? 

The course is four days long, with each day consisting of six hours of instruction. 

Conclusion 

If you’re interested in learning Python for Data Science, Data Science Dojo’s Introduction to Python for Data Science course is an excellent place to start. This course will provide you with a solid foundation in Python programming and teach you how to use Python for data analysis, visualization, and manipulation.  

With its instructor-led live training format, you’ll have the opportunity to learn from an experienced instructor and interact with other students. Enroll today and start your journey to becoming a data scientist with Python.

register now

Data Science Dojo
Ali Mohsin
| July 18

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

Repositories

  • 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.

Conclusion

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!

Data Science Dojo
Albar Wahab
| June 22

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

Myths

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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