Towards Data Science’s Post

Lee Vaughan's intro to NumPy part 4 — doing math with arrays + reading and writing array data. Read now and test your knowledge 👉 #NumPy #Python

##Session 2 (NumPy) - Completed 24 June 2024 Learned Concepts: - Initializing Arrays: Different methods to create arrays (e.g., `array()`, `zeros()`, `ones()`, `arange()`, `linspace()`). - Types of Arrays: Understanding various array types like 1D, 2D, and multi-dimensional arrays. - Shape and Size: Determining the shape and size of arrays using `.shape` and `.size`. - Accessing Specific Elements: Indexing and slicing arrays to access and modify elements. - Linear Algebra: Performing linear algebra operations like dot products, matrix multiplication, and finding determinants. - Statistics in Arrays: Applying statistical functions like mean, median, standard deviation, and sum on arrays. - Additional Topics: Covered various other short topics related to NumPy's capabilities. Note: • NumPy proved to be a gripping and essential topic, offering a powerful toolset for numerical computations. •Session 3 starts from tomorrow 25th June • Resource link for NumPy - https://lnkd.in/dcyp-FGN #python #programming #numpy

My presentation this week to the MS Excel Toronto meetup (register for free in comments) will be kicking off with a demo on the Faker package in Python 🎲🐍🎲. This package, available directly in Excel, generates synthetic data, such as names, addresses, and numerical values, that can be used to simulate real-world scenarios. With this and Python's rich suite of tools in probability, visualization and more, we can turn Excel workbooks into a user-friendly, mathematically-elegant simulation machine. I'll leave a link to register for the meetup as well as some additional resources in the comments.

Day 4 of 50 days of Statistics For Finance: Utilizing Numpy Arrays for Financial Time Series** When dealing with large datasets in finance, efficiency becomes key. The NumPy library in Python is a powerful tool that enables fast mathematical operations on large arrays and matrices of numerical data. It’s essential for working with financial time series data where performance is crucial, such as high-frequency trading or large-scale financial simulations. Today, we will cover how to leverage NumPy for handling financial time series data, demonstrate its importance, and explore how it simplifies complex operations like calculating returns, risk metrics, and correlation matrices. In the next post, we’ll explore how Pandas Series can be utilized for more advanced financial data manipulation and analysis. #Numpy #FinancialData #TimeSeriesAnalysis #QuantitativeFinance #DataScience #PythonFinance #StatisticsInFinance #AlgorithmicTrading #FinanceWithPython

𝐃𝐚𝐲 74 𝐨𝐟 #100𝐃𝐚𝐲𝐬𝐎𝐟𝐂𝐨𝐝𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧: 𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐢𝐧𝐠 𝐍𝐮𝐦𝐏𝐲 𝐏𝐨𝐰𝐞𝐫 🐍 🚀 Today's Pythonic NumPy Adventure: Array Creation, Linear Algebra, and Image Manipulation 🔍 𝐖𝐡𝐚𝐭 𝐈 𝐄𝐱𝐩𝐥𝐨𝐫𝐞𝐝: • 🔄 Created arrays manually using `np.array()` and harnessed array generation with `.arange()`, `.random()`, and `.linspace()`. • 📊 Analyzed the shape and dimensions of ndarrays for a deeper understanding. • 🔍 Expertly sliced and subsetted ndarrays based on their indices. • ⚙️ Explored linear algebra operations, including scalar operations and matrix multiplication. • 🔄 Leveraged NumPy's broadcasting to ensure compatibility in ndarray shapes. • 📸 Manipulated images in the form of ndarrays, showcasing the versatility of NumPy. 📂 𝐒𝐨𝐮𝐫𝐜𝐞 𝐂𝐨𝐝𝐞: https://lnkd.in/dbrb4VZi 𝐂𝐡𝐞𝐜𝐤 𝐨𝐮𝐭 𝐭𝐡𝐞 𝐝𝐞𝐦𝐨 𝐯𝐢𝐝𝐞𝐨 𝐛𝐞𝐥𝐨𝐰 👇 Exciting strides in NumPy magic and data manipulation! 🔢💻 #Python #NumPy #CodeProgress #DataScience #100DaysOfCode

Math Operations Calculator using OOP in Python Features: - Arithmetic Operations: Addition, Subtraction, Multiplication & Division Operator - Algebra Operations: Absolute Value, Square Root, Radical, Exponent, Logarithm, Factorial & Modulus - Linear Algebra Operations: Vector Addition, Cross Product, Dot Product, Matrix Multiplication, Transpose & Determinant Operator - Set Operations: Union Operator & Intersection Operator Github link: https://lnkd.in/dvTK2-Dm

https://lnkd.in/gdVBR72g Hello 👋 people from the future , Here I published the python library named "numbersD", now it is able to solve small math aptitude problems. But, soon it will become a library to solve more mathematical problems. Looking same minded people to contribute in this dream. #opensource #python #scientific_calculations #aptitude #math #Programming #colab

Bayesian optimisation has become increasingly popular, and the number of tools and packages is growing every day. But how hard can it be to implement? I set myself the challenge of creating a usable Bayesian optimisation package, including visualisation, in under 100 lines of standard Python and NumPy. No other libraries. 🧮 Custom trainable Gaussian processes from scratch 🎯 Metaheuristic-based optimization algorithm 📊 Acquisition function and experimental design loop 🖼️ Visualisation of function and acquisition function I want to demonstrate that whilst practitioners may not have the skills or time to implement BO for themselves, building it from scratch shouldn't be a Herculean task. It's always important to understand what is going on in the tools that we use, and simple doesn't mean less powerful. There are so many interesting domain-specific variants and problems across chemistry, drug-discovery, and experimental design, a lightweight starting point can be extremely useful. https://lnkd.in/ejug5B8h #BayesianOptimization #MachineLearning #PythonProgramming #DataScience This GIF is what results from my 97 lines!