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Python is an abundant source of libraries. A Python library is a gathering of functions that assist one to perform many actions. It has myriad inbuilt libraries. Python contains ample libraries for data science. This tutorial covers Python libraries for a data scientist. Python categorizes these libraries according to their title role in data science. Let’s see  Python libraries for a data scientist: A. Data Cleaning and Data Manipulation Pandas NumPy Spacy SciPy B. Data Gathering Beautiful Soap Scrapy Selenium C. Data Visualisation Matplotlib Seaborn Bokeh Plotly D. Data Modelling Scikit-Learn PyTorch TensorFlow Theano E. Image Processing Scikit-Image Pillow OpenCV F. Audio Processing pyAudioAnalysis Librosa Madmom 1) Pandas Pandas is one of the most popular data analysis and data manipulation libraries. It is an open-source library. DataFrame is the chief data structure of the Pandas library. DataFrame stores and mana...

As we are moving into the digital era, technologies such as data science & big data are most commonly used. They have become the buzzword as well as the most significant assets in the world of IT. Often people tend to use these terms interchangeably, but the fact is that there are major differences among these concepts. Since both the fields are interlinked & quite confusing, I have listed the major differences among them in a simple manner. Meaning Data Science  is a multidisciplinary field which comprises mathematics, programming, statistics and deals with both structured & unstructured data. It is a branch of study that involves everything associated with data cleansing, preparation & analysis. Big data  refers to massive volumes of complex sets of data that cannot be processed & analyzed through conventional technologies. The concept of big data is associated with 5 V’s i.e., velocity, variety, volume, variability & veracity of data w...

Python has a large number of libraries that have been specially developed for the purpose of data science and analysis. It provides a lot of useful libraries that help you in manipulating data, exploratory data analysis, and building models. “Python has been a charmer for data scientists” 1. Pandas: Pandas stand for “Python Data Analysis Library". Pandas is one of the most powerful libraries for data manipulation. In the pandas' library, there are various numbers of import and export functions. It also includes a method for data structures What can you do with Pandas? Indexing, manipulating, renaming, sorting Update, Add, Delete columns handle missing data or NANs 2. NumPy: Numpy is the most important package in Python, it is a general-purpose array-processing package. NumPy contains generic multi-dimensional data. What can you do with NumPy? Basic array operations Advanced array operations Work with DateTime or Linear Algebra Slicing...

Let us see some parameters and rating. The information below is to choose the best one and I give a score to each of these 3 languages- Background SAS - It has been the undisputed market leader in commercial analytics space. The Software offers a huge array of statistical functions has a good GUI for the people to learn quickly & provides technical support. It ends up being the most expensive option & always enriched with statistical functions. Python- It is a multi-purpose, free & open source programming language and becomes very popular in Data Science due to its active community & Data Mining Libraries. R- It is a free & open-source programming language used to perform advanced data analysis tasks. Because of its open-source nature, latest technology gets released quickly and it is a very cost-effective option . SAS   is being preferred by big corporations because they offered highly reputed customer service, which is why SAS has an advanta...

In today’s competitive market, Data scientists need to upskill and upgrade themselves as per the changing demands in the industry. They must possess the knowledge and application of programming languages that better amplify the Data Science industry. These are the top programming languages: Python -  Python is an interpreter based high-level programming language that is mostly used for Data Science and Software Development(Data Analysis, data mining, wrangling, visualisations and developing predictive models, Natural Language Processing, and Computer Vision). It is one of the most popular languages because of its versatility, scalability, and code-readability(even YouTube has migrated to Python due to its scalability). Also, it is an object-oriented, open-source and easy to learn programming language. R -  R is considered as the most popular analytical tool in the world nowadays. It is used in data analysis, statistical modelling, time-series forecasting, clustering, ...

Experts working with data science applications don’t want to get stuck down with complex programming requirements. They want to use simple & easy programming language & hence prefer python to perform tasks hassle-free. It's a powerful & versatile language that allows you to do more with less code. According to IEEE Spectrum’s 2019 rankings, Python is firmly on top followed by Java, C, C++ & R. The major reasons for the popularity of python are- The major reasons for the popularity of python are- Huge community Because of its vast applications such as scripting, development & so on, it has the support of a huge community, & hence brings many python experts together. Simplicity Python is easy to read, understand & even simpler to set up. It doesn’t face any problems like classpath in Java & compiler issues in C++. All you need is its installation & you are ready to run it. Large standard library Python comes with a large ...