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Tag: python for data science

Why Python is a Necessary Data Scientist Skill

Posted on by Imarticus Learning

Key Takeaway

  • Python for data science is a skill everybody in the profession should possess.
  • Python is the most ideal language for its ease of use, wide libraries, and community support.
  • A structured learning path with courses and practice fast tracks your journey.
  • The average data scientist salary is reflective of the high demand for Python skills.
  • Python ensures its relevance in all applications of data science due to its versatility.

Why Python is a Must-Have Skill for Data Scientists

Introduction

Are you contemplating a career in data science yet unsure about what programming language to hone? If so, you’re not the only one! Certainly, many people preparing for a career in data science ask themselves similar questions. The answer is simple: Python. Known as one of the most disruptive #technologies in the industry, Python has become the preferred programming language amongst professionals who are keen on establishing their success in data science. But why dive into this particular programming language? This blog examines the benefits of utilizing Python for data science and why it is an essential skill for anyone aiming to prosper in the field. 

Why Python is Popular Among Data Scientists

Python’s popularity among data scientists isn’t accidental. The following factors contribute to its dominance:

  • Ease of Learning: Python’s simple syntax makes it beginner-friendly. This accessibility allows newcomers to quickly grasp the basics, enabling them to focus on data analysis and problem-solving rather than syntax intricacies.
  • Huge Libraries: Libraries such as NumPy, Pandas, and Matplotlib ease the manipulation, analysis, and visualization of data. The above libraries feature pre-built functionalities that save so much time and effort, making the workflow with data efficient and aligned.
  • Community Help: An active community helps Python users. Be it in forums, tutorials, or online courses, they are there to support everybody, regardless of skill levels.
  • Versatility: It’s not just limited to data science; you can also use Python in web development, automation, and more. This adaptability ensures that learning Python is a valuable investment for long-term career growth.
  • Industry Demand: Companies across industries prefer Python for its efficiency in handling data-driven tasks. Its widespread use in AI and machine learning projects further solidifies its relevance for aspiring data scientists.

In mastering Python, they receive a skill which not only adheres to industry standards but also one in which it offers flexibility in exploring other tech domains.

Key Features of Python for Data Science

Feature Description
Open Source Free to use with an extensive repository of resources.
Rich Ecosystem Offers specialized libraries like SciPy and TensorFlow.
Cross-Platform Compatible with Windows, macOS, and Linux.
Integrative Capabilities Works seamlessly with other tools and platforms.
High Scalability Handles projects of all sizes effectively.

Role of Python in Data Science Applications

Python has an important place in the following domains of data science:

  • Data Cleaning and Preprocessing: Pandas library simplifies the handling of data manipulation. They offer operations such as filtering, grouping, and merge of datasets within minimal code that assures high quality of data before analysis.
  • Data Visualization: Matplotlib and Seaborn make possible breathtakingly beautiful graphics. Through these libraries, trend identification, outliers, and patterns in datasets are easier to find, hence better decision-making.
  • Machine Learning: Scikit-learn and TensorFlow allow for predictive models to be constructed. That level of simplicity where Python is involved makes it easy to implement even scary algorithms for newcomers.
  • Big Data Analysis: Python can analyze large data with the scalability feature of PySpark. The capability to support distributed computing guarantees it processes enormous amounts of data effectively.
  • Statistical Analysis: SciPy and Statsmodels libraries offer powerful statistical analysis tools in hypothesis testing, regression, and probabilistic analysis. This makes it a tool for all kinds of data scientists.
  • Automation: Automation of repetitive data extraction, development of reports, or the web scraping wherein machine productivity is increased.

Rich in versatility, Python is a language-on-which describe many application use for data science-on-which professionals could seize a great opportunity for dealing with real-world challenges effectively. Check out this video to learn more about Python.

Python vs Other Programming Languages

Language Strengths Limitations
Python Easy to learn, rich libraries, versatile Slower than compiled languages
R Excellent for statistical analysis Limited in versatility outside data science
Java Great for scalability and robustness More complex and less beginner-friendly
SQL Superb for database management Not suitable for general programming tasks

How to Learn Python for Data Science

Here are steps to begin with Python:

  • Join a Data Science Course: A good course to learn the fundamentals and advanced usages of Python.
  • Practice on Projects: Find datasets to practice on platforms such as Kaggle.
  • Join Forums and Social Groups: Join active forums and groups that are discussing Python.
  • Read Documentation: Follow the official Python documentation for thorough knowledge.
  • Update Yourself: Be updated with all the blogs and publications that report the latest in Python.

Also Read:

https://imarticus.org/blog/python-interview-questions/ 

FAQs About Python for Data Science

Why is Python an essential utility for a data scientist?

Python makes data analysis, machine learning, and visualization simple and is, thus, essential for any data scientist.

How long would it take to learn Python for data science?

Based on practice and personal experience, completing the basics of Python and its applications in data science will take you around 3-6 months on average.

What are the best resources for learning Python?

The best are online courses and textbooks like “Python Crash Course” or platforms like Imarticus Learning.

Can I become a data scientist without knowing Python?

It could be possible, but Python is too versatile and has high demand in the industry; therefore, you should develop the skill.

Is Python better than R for data science?

Python enjoys much broader use as a general-purpose language and is utilized widely in different domains; R is more specialized for statistical tasks.

What is the average salary of a data scientist?

The average data scientist receives ₹6L/yr., according to Glassdoor.

What are the top Python data science libraries?

Must-know libraries are NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow.

Is Python enough for data science?

Python can cover most data science needs, but knowledge of SQL and statistics is also beneficial.

How do I practice Python for data science?

Use platforms like Kaggle for projects and practice coding regularly.

Final Words

Python, in the ever-accelerating world of data science, flies as an all-rounder tool, rather than just a programming language; thus, it is a transformative capability. With its versatility, extensive libraries, and unquestionable popularity in the data science market, it is thus a core instrument for any data scientist to adapt with. From starters to seasoned professionals, learning Python opens up numerous paths toward more opportunities and improved career prospects. Start learning Python now and get initiated into your tryst with becoming a distinguished data scientist in the fiercely competitive field. Learning Python is the first step towards a stellar career in data science! Enroll for a data science course right now and step into a whole new world of opportunity, powered by dynamic fields!

Understanding the basics of data visualization with python

Posted on by Imarticus Learning

Data visualization has become an increasingly important part of the data analysis process in recent years. Many analysts have found that a picture is worth a thousand words, and in this case, it just might be true. You could say that good data visualization can save even more than 1,000 words–it can save lives! Let’s explore some basics of making compelling visualizations with Python.

What is Data Visualization?

Data visualization represents data in a visual form. You can use visualizations to help people understand data more efficiently, ranging from simple graphs to complex infographics. Data visualization is an increasingly popular field with many practical applications. For example, you can use it for business intelligence gathering and analysis or education purposes. Some experts consider data visualization to be a vital part of the expanding field of big data.

Data types and how they get visualized?

There are many types of data, including categorical, univariate, multivariate normal, and so on. Data visualization methods vary depending on the type of data represented. For example, there are several other ways to express categorical data than with graphs.

Univariate data is usually best displayed in a simple bar graph or line graph. Categorical information is often best represented by a pie chart. Multivariate data can be shown in a radar graph or spider chart, while multivariate average data get visualized with a scatter plot.

How to use Python for data visualization?

Python is an easy-to-use programming language that You can use for data visualization. Many libraries, including matplotlib, make it possible to create visualizations without much technical knowledge.

You can even create interactive online visualizations using Python. For example, you can use Python to create visualizations for the Vega-Lite specification, which allows you to create interactive online data visualization. Due to its flexibility and ease of use, it has become one of the most popular languages for data science. It is perfect for working with large amounts of data because it can easily handle large lists or arrays.

Python-based data visualization libraries are beneficial because they typically allow for rapid prototyping of visualizations. It makes them an excellent choice for exploratory data analysis because you can quickly try out different algorithms and processes. The downside is that they can sometimes be challenging to use for more complex projects.

Explore and Learn Python with Imarticus Learning

Industry specialists created this postgraduate program to help the student understand real-world Data Science applications from the ground up and construct strong models to deliver relevant business insights and forecasts. This python tutorial is for recent graduates and early-career professionals (0-5 years) who want to further their careers in Data Science and Analytics, the most in-demand job skill.

Some course USP:

This Python for data science course for students is with placement assurance aid the students to learn job-relevant skills.

 

Impress employers & showcase skills with the certification in Python endorsed by India’s most prestigious academic collaborations.

World-Class Academic Professors to learn from through live online sessions and discussions.

Understanding Linear Discriminant Analysis in Python for Data Science

Posted on by Imarticus Learning

When we are working with more than two classes in data, LDA or Linear Discriminant Analysis is the best classification technique we can use. This model provides very important benefits to data mining, data retrieval, analytics, and Data Science in general such as the reduction of variables in a multi-dimensional dataset.

This is very useful for minimizing the variance between the means of the classes while maximizing the distances between the same. LDA removes excess variables while retaining most of the necessary data. This is extremely crucial for Applied Machine learning and various Data Science applications such as complex predictive systems.

What is Linear Discriminant Analysis?

LDA is a linear classification technique that allows us to fundamentally reduce the dimensions inside a dataset while also retaining most of the crucial data and utilizing important information from each of the classes. Multi-dimensional data contains multiple features that have a correlation with other features. Using dimensionality reduction, one can easily plot multidimensional data into two or three dimensions.

This also helps make data more cognizable for non-technical team members while still being highly informative (with more relevant details). LDA estimates the probabilities of new sets of inputs belonging to each class and then makes predictions accordingly.

Classes with the highest probability of having new sets of inputs are identified as the output class for making these predictions. The LDA model uses Bayes Theorem for estimating these probabilities from classes and data belonging to these classes.

LDA allows unnecessary features that are “dependent”, to be removed from the dataset when converting the dataset and reducing its dimensions. LDA is also very closely related to regression analysis and analysis of variance. This is due to all of their core objectives of trying to express individual dependent variables as linear combinations of other measurements or features.

However, Linear Discriminant Analysis uses a categorical dependent variable and continuous independent variables. Unlike different regression methods and other classification methods, LDA assumes that independent variables are distributed normally. For example, logistic regression is only useful when working with classification problems that have two classes.

How is LDA used in Python?

Using LDA is quite easy, it uses statistical properties that are predicted from the given data using various distribution methods such as multivariate Gaussian (when there are multiple variables). Then these statistical properties are used by the LDA model for making predictions. In order to effectively use the LDA model or to use Python for Data Science, one must first employ various libraries such as pandas, matplotlib, and numpy.

First, you must import a dataset such as the ones available in the UCI Machine Learning repository. You can also use scikit-learn to import a library more easily. Then, a data frame must be created that contains both the classes and the features.

Once that is done, the LDA model can be put into action, which will compute and calculate within the classes and class scatter matrices. Then, new matrixes will be created and new features will be collected. This is how a successful LDA model can be run in Python to obtain LDA components.

Conclusion

Linear Discriminant Analysis is one of the most simple and effective methods for classification and due to it being so preferred, there were many variations such as Quadratic Discriminant Analysis, Flexible Discriminant Analysis, Regularized Discriminant Analysis, and Multiple Discriminant Analysis. However, these are all known as LDA now. In order to learn Python for Data Science, a reputed PG Analytics program is recommended.

Is Python Required for Data Science? How Long Does It Take to Learn Python for Data Science?

Posted on by Imarticus Learning

Data Science and its analytics require good knowledge and the flexibility to work with statistical data including various graphics. Python is tomorrow’s language and has a vast array of tools and libraries. Its installation program Anaconda works with many operating systems and protocols like XML, HTML, JSON, etc. It scores because it is an OO language well-suited for web development, gaming, ML and its algorithms, Big Data operations, and so much more.
Its Scipy module is excellent for computing, engineering and mathematical tasks allowing analysis, modeling, and even recording/ editing sessions in IPython which has an interactive shell supporting visualization and parallel computing of data. The decorators of functionality are a good feature in Python. Its latest V3.6 features the asyncio module, API stability, JIT compiler, Pyjion, and CPython aids.

Uses of Python:

Learn-by-doing for tasks involving python for data science and Big data Analytics will help in the following.
Web development can be easy with Flask, Bottle, Django, Pyramid, etc especially to cover even the backend REST APIs.
Game development is enhanced through Pygame where you can use the module to create a video game.
Computer VisionTools like Opency, Face detection, Color detection, etc is available in Python.
Scraping the web from websites that cannot expose data due to lack of an API is regularly done by price-comparison e-commerce sites, news and data aggregators using Python libraries like BeautifulSoup, Requests, Scrapy, PyMongo or Pydoop.
Tasks involving ML algorithms like identification of fingerprints, predicting stock prices, spam detection etc using ML is supported by Python’s modules like Theano, Scikit-learn, Tensorflow, etc. Even Deep Learning is possible with Tensorflow.
Developing cross-platform GUI desktop application is a breeze with the Python modules of PyQt, Tkinter etc.
Made-easy Robotics uses Raspberry Pi as its core which can be easily coded on Python.
Data Analysis from both offline/online data needing cleaning can be achieved in Pandas. Matplotlib can help find patterns and data visualization which are essential steps before applying any ML algorithm.
Browser Automation tasks like browser opening, FB posts and status are quick using Python’s Selenium.
Content Management tasks including advanced ones are relatively faster with Plone, Django, CMS etc.
Big Data libraries are more flexible and use as a learning tool.

How to Learn Python:

Here is a step-by-step approach to becoming a Kaggler on Python from an absolute Python newbie complete with tools and ready to kick-start your career in data-sciences.
Step 1: Read, learn and understand why you are using Python:
Zero in on your reasons for learning to use Python, its features and why it scores in data sciences.
Step 2: Machine set-up procedures:
Firstly use Continuum.io to download Anaconda. Just in case you need help, refer to complete instructions for the OS by just clicking on the link.
Step 3: Python language fundamentals learning:
It is always better to gain experience from a reputed institute like Imarticus Learning for doing a course on data analytics and data sciences. Their curriculum is excellent and includes hands-on practice, mentoring and enhancing practical skills in Python.
Step 4: Use Python in interactive coding and Regular Expressions:
When using data from various sources the data will need cleaning before the analytics stage. Try assignments like choosing baby-names and data wrangling steps to become adept at this task.
Step 5: Gain proficiency in Python libraries like Matplotlib, NumPy, Pandas and SciPy.
Practice in these frequently used libraries is very important. Try out these following tasks and resources like NumPy tutorial and NumPy arrays, SciPy tutorials, Matplotlib tutorial, the ipython notebook, Pandas, Data munging and exploratory analysis of data.
Step 5: Use Python for Visualization:
A good resource is linked in the CS 109 lecture series.
Step 7: Imbibe ML and Scikit-learn:
These are very important data analysis steps.
Step 8: Use Python and keep practicing:
Try hackathons like Kaggle, DataHack and many others.
Step 9: Neural networks and Deep Learning
Try out short courses on the above topics to enhance your skills.
In conclusion, many reputed institutes offer a Data science Course. The course at Imarticus also offers other advantages such as learning through convenient modes and timings, global updated industry-relevant curriculum, extensive hands-on practice and certification that ensure you use the mentorship to be career and job-ready from the very first day.