Reading list for data science
Data: Python, JupyterNotebook, Tableau, DBeaver, Spyder, PhpStorm, Notepad++
App: Tkinter, Flask, Kivy, PyInstaller, Py2App/Exe, Google Cloud Engine, Dash, PyVan
Python: Selenium, BeautifulSoup, DiffLib, tqdm, py2exe, pyInstaller, tkinter, ki
Web: Google Analytics, Google AdWords, Google Trends, Google Search, Ubersuggest (Neil Patel app), Domain authority and TF / CF analysis websites, Google PageSpeed Insights, Website Performance analysis websites, SVG Converter (onlineconverterfree.com), TinyPNG & JPEG, Yakaferci
Website: Laravel, PWA, WordPress, ContactForm7, Yoast SEO, Caldera Form, Duplicator, Elementor, Enlighter, TablePress, FileZilla, Prestashop, uiGradient, icons8, hover effect & box shadow websites, CSS & PHP & HTML beautifier & minifier
Content: Adobe Illustrator, Adobe Photoshop, Adobe InDesign, Pexels, TinyPNG & JPEG, TextSpeech, Pixabay, FreePik, iMovie, video format converter, Gamblr, Hootsuite, Facebook, Instagram, Linkedin, Pinterest, Twitter
Mix: Github, Gist, Anaconda, Docker, Thunderbird, Apowersoft, TeamViewer, Slack, Loom, TextSpeech, Gretl, DropBox, MailExtractor, Mailjet/MailChimp/Dolibarr, Regex101, Google MyBusiness
Microsoft: Excel, PowerPoint, Skype, Word, Azure, Access, OneDrive, SharePoint, Outlook, Teams
Ajoutez votre titre ici
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
I propose a list of links that will be useful in your daily data scientist. These links are based on research and readings (mainly Towards Data Science, Medium and KDNuggets). For a better organisation, there are 3 parts: resources, tools and more. The list is regularly updated. You can also visit my Tool Box page for more resources.
