Ilgar Zarbaliyev (Excel World)

  📊 Data Analysts: A Data Analyst is a professional who 📊 collects, processes, and 📈 analyzes data to provide insights and support decision-making. Some common tools and technologies used by Data Analysts include: 📊 Microsoft Excel: A versatile spreadsheet software used for data entry, manipulation, and basic analysis. Microsoft Excel   Description: Microsoft Excel is a powerful and versatile spreadsheet software developed by Microsoft. It is a part of the Microsoft Office suite and is widely used for various tasks involving data organization, analysis, and visualization. Excel provides a grid of cells arranged in rows and columns, which users can use to input, manipulate, and analyze data.   Key Features:   Grid Interface: Excel's interface consists of a grid of cells, where each cell can contain text, numbers, formulas, or functions.   Formulas and Functions: Excel allows users to perform calculations on data using formulas an...

  📊 Data Analysts: A Data Analyst is a professional who 📊 collects, processes, and 📈 analyzes data to provide insights and support decision-making. Some common tools and technologies used by Data Analysts include: 🐍 R: Programming languages used for data analysis, statistical modeling, and visualization. R: R is a language specifically designed for statistical computing and graphics. It has a strong community of statisticians and data analysts who contribute to its rich ecosystem of packages and libraries tailored for data analysis. Some key features and libraries in R for data analysis and visualization include: RStudio: An integrated development environment (IDE) for R that provides a user-friendly interface for writing R code, visualizing data, and creating reports. dplyr: A package that simplifies the process of data manipulation, providing a set of functions for filtering, selecting, summarizing, and arranging data. ggplot2: A powerful and flexible data visualization packa...

  📊 Data Analysts: A Data Analyst is a professional who 📊 collects, processes, and 📈 analyzes data to provide insights and support decision-making. 🐍 Python/R: Programming languages used for data analysis, statistical modeling, and visualization. Python is a versatile and widely-used programming language that has gained significant popularity in the data science and analysis communities. It offers a rich ecosystem of libraries and tools for data manipulation, analysis, and visualization. NumPy: A fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a wide range of mathematical functions to operate on these arrays. Pandas: A powerful library for data manipulation and analysis. It introduces data structures like DataFrame, which makes handling and analyzing structured data more intuitive. Matplotlib: A widely-used 2D plotting library that produces high-quality figures and plots. It provides a flex...

  📊 Data Analysts: A Data Analyst is a professional who 📊 collects, processes, and 📈 analyzes data to provide insights and support decision-making. Some common tools and technologies used by Data Analysts include: 🤖 Basic Machine Learning Libraries: Libraries like scikit-learn in Python for performing simple machine learning tasks. When it comes to performing simple machine learning tasks, several libraries offer a user-friendly and efficient way to get started. These libraries provide tools and functions for tasks like data preprocessing, model training, evaluation, and more. One of the most well-known libraries for basic machine learning tasks is scikit-learn in Python, but there are also other alternatives worth considering. Here are some of the key libraries: Scikit-learn (sklearn): Scikit-learn is a widely used open-source machine learning library for Python. It offers simple and efficient tools for data mining and data analysis. It includes various algorithms for clas...

📊 Microsoft Excel: A versatile spreadsheet software used for data entry, manipulation, and basic analysis. 🐍 Python/R: Programming languages used for data analysis, statistical modeling, and visualization. 📓 Jupyter Notebooks: An interactive environment for writing and executing code, allowing Data Analysts to combine code, visualizations, and explanations. 🔄 Version Control (Git): Tools for tracking changes in code and collaborating with other analysts or data professionals. 🤖 Basic Machine Learning Libraries: Libraries like scikit-learn in Python for performing simple machine learning tasks.       📊 Data Analysts: A Data Analyst is a professional who 📊 collects, processes, and 📈 analyzes data to provide insights and support decision-making. Some common tools and technologies used by Data Analysts include: 📓 Jupyter Notebooks: An interactive environment for writing and executing code, allowing Data Analysts to combine code, visualiza...