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Python for Everything: Python + Django = Web Development Python + Matplotlib = Data Visualization Python + Flask = Web Applications Python + Pygame = Game Development Python + PyQt = Desktop Applications Python + TensorFlow = Machine Learning Python + FastAPI = API Development Python + Kivy = Mobile App Development Python + Pandas = Data Analysis Python + NumPy = Scientific Computing

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Planning for Data Science or Data Engineering Interview. Focus on SQL & Python first. Here are some important questions which you should know. 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Find out nth Order/Salary from the tables. 2- Find the no of output records in each join from given Table 1 & Table 2 3- YOY,MOM Growth related questions. 4- Find out Employee ,Manager Hierarchy (Self join related question) or Employees who are earning more than managers. 5- RANK,DENSERANK related questions 6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.) 7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN. 8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers. 9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure. 10-Identify and remove duplicate records from a table. SQL Interview Resources: https://topmate.io/analyst/864764 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 1- Reversing a String using an Extended Slicing techniques. 2- Count Vowels from Given words . 3- Find the highest occurrences of each word from string and sort them in order. 4- Remove Duplicates from List. 5-Sort a List without using Sort keyword. 6-Find the pair of numbers in this list whose sum is n no. 7-Find the max and min no in the list without using inbuilt functions. 8-Calculate the Intersection of Two Lists without using Built-in Functions 9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response. 10-Implement a function to fetch data from a database table, perform data manipulation, and update the database. Python Interview Resources: https://topmate.io/analyst/907371 Join for more: https://t.me/datasciencefun ENJOY LEARNING 👍👍

Create a Progress Bars using Python

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⌨️ Calculate derivatives in Python

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⌨️ Asynchronous code

Asynchronous code is an approach to writing code that allows multiple tasks to be performed simultaneously within a single process. This is achieved through the use of asynchronous functions and coroutines. Unlike synchronous code, which executes each task sequentially, asynchronous code can run multiple tasks “in parallel” and organize their execution using iterations and callback calls.

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Some useful PYTHON libraries for data science NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++ SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices. Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot. Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community. Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction. Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator. Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data. Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets. Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data. Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information. SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code. Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient. Additional libraries, you might need: os for Operating system and file operations networkx and igraph for graph based data manipulations regular expressions for finding patterns in text data BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

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