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https://github.com/nafisalawalidris/tools-for-data-science

It covers popular languages (Python, R, SQL) and libraries (NumPy, Pandas) used in the field. The author shares their objectives of teaching data analysis, web development, and critical thinking skills. The repository also includes code examples, explanations of arithmetic expressions, and contact information for the author.
https://github.com/nafisalawalidris/tools-for-data-science

arithmetic-expressions data-analysis data-science data-visualization languages libraries matplotlib numpy pandas programming python r sql tools web-development

Last synced: 19 days ago
JSON representation

Host: GitHub
URL: https://github.com/nafisalawalidris/tools-for-data-science
Owner: nafisalawalidris
Created: 2023-03-17T12:32:13.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2023-06-24T22:46:39.000Z (over 1 year ago)
Last Synced: 2024-11-22T21:16:33.456Z (3 months ago)
Topics: arithmetic-expressions, data-analysis, data-science, data-visualization, languages, libraries, matplotlib, numpy, pandas, programming, python, r, sql, tools, web-development
Language: Jupyter Notebook
Homepage:
Size: 10.7 KB
Stars: 1
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

cognitiveclass.ai logo

My Jupyter Notebook on IBM Watson Studio

Nafisa Lawal Idris

Data Scientist

Data science fascinates me as it provides the necessary tools to uncover hidden patterns and insights from the vast amounts of information generated daily, and I find it incredibly satisfying to use this knowledge to automate tasks and solve real-world problems, while pushing the boundaries of the field.

Popular Data Science Languages

Python

Java

Julia

MATLAB

Scala

Popular Data Science Libraries

NumPy

Pandas

Matplotlib

Scikit-learn

TensorFlow

PyTorch

SciPy

Statsmodels

OpenCV

XGBoost

Table of Data Science Tools

Tools
Description

Python
General-purpose programming language with extensive support for scientific computing and data analysis

R
Statistical programming language for data analysis and visualization

SQL
Query language used for managing and manipulating relational databases

Tableau
Data visualization tool for creating interactive dashboards and reports

Power BI
Business intelligence platform for creating visualizations and analyzing data

Excel
Spreadsheet software with built-in data analysis tools

Jupyter Notebook
Web application for creating and sharing documents containing live code, equations, visualizations, and narrative text

Apache Hadoop
Distributed storage and processing system for big data

Apache Spark
Distributed computing system for processing large-scale data sets

Databricks
Cloud-based platform for processing and analyzing big data using Apache Spark

Arithmetic Expressions

Arithmetic expressions are a fundamental part of mathematics and programming. In mathematics, arithmetic expressions are used to represent numeric calculations, such as addition, subtraction, multiplication, and division. In programming, arithmetic expressions are used to perform calculations on numerical data types, such as integers and floating-point numbers.

Examples of arithmetic expressions:

2 + 3: addition of 2 and 3, which evaluates to 5.

5 - 2: subtraction of 2 from 5, which evaluates to 3.

4 * 6: multiplication of 4 and 6, which evaluates to 24.

10 / 2: division of 10 by 2, which evaluates to 5.

3 ** 4: exponentiation of 3 to the power of 4, which evaluates to 81.

These examples demonstrate the basic arithmetic operators in Python, which are + (addition), - (subtraction), * (multiplication), / (division), and ** (exponentiation). By combining these operators with numeric operands, we can create complex arithmetic expressions that perform various calculations.

Multiplies and Adds numbers



        # multiply two numbers

        a = 3

        b = 4

        c = a * b

        print(c)

            # add two numbers

    x = 10

    y = 7

    z = x + y

    print(z)

Converts minutes to hours



    # convert minutes to hours

    minutes = 145

    hours = minutes / 60

    print(hours)

Objectives

To introduce learners to the fundamentals of data science and full-stack web development.

To provide hands-on experience with popular data science tools and libraries, such as Python, NumPy, Pandas, and Matplotlib.

To teach learners how to build and deploy web applications using HTML, CSS, JavaScript, and popular frameworks such as React and Node.js.

To equip learners with the skills and knowledge to analyze data, build predictive models, and make data-driven decisions.

To help learners develop their critical thinking and problem-solving skills, as well as their ability to communicate complex technical concepts to non-technical stakeholders.

By the end of this program, learners would have a strong foundation in data science and full-stack web development and be able to apply these skills to real-world problems and projects.

Author: Nafisa Lawal Idris

Link to My GitHub