DataDisca Environment

Basic & Compulsory Knowledge for Any Data Scientist

1. Python
   1. NumPy
   2. Pandas
   3. pandas profiling
   4. dtale
   5. PEP8 standard
2. SQL
3. Python Visualisations
   1. Plotly
   2. Folium
4. Dashboards
   1. Tableau
   2. Power BI
5. Classical Machine Learning
   1. Clustering
   2. Classification
   3. Regression
   4. Time series analysis
6. Neural Networks (Deep Learning)
   1. Computer vision
   2. NLP
   3. Time series forecasting
7. Parallel processing
8. Linux (Ubuntu)
9. Coding environment (Company Specific)
   • PyCharm, Jupyter Notebook and GitHub are used in DataDisca
   • Git Markdown https://guides.github.com/features/mastering-markdown/
10. Applied data science

Students

If you are a university student do not forget the following.

1. Tableau professional edition
   • Free for students.
     • https://www.tableau.com/academic/students
   • Can renew annually
   • Renew carefully to get the maximum benefit
2. Pycharm professional
   • Free for students.
     • https://www.jetbrains.com/community/education/#students
   • Can renew annually
   • Renew carefully to get the maximum benefit
   • All the products are free for students
     • https://www.jetbrains.com/products/
     • DataGrip is also found to be useful in data-to-day data science
       • https://www.jetbrains.com/datagrip/
3. MATLAB
   • Depends on university subscriptions
   • https://www.mathworks.com/products/matlab.html
4. MATHEMATICA
   • Depends on university subscriptions
   • https://www.wolfram.com/mathematica/

Forums

1. KDnuggets (https://www.kdnuggets.com/)

   KDnuggets is a professional data science forum. High quality and up to date data science topics are discussed there. Please subscribe and read articles. We will discuss some articles as time permits. You can recommend good articles for us. Further, KDnuggets has interview questions and skill lists that you should acquire.

2. Kaggle (https://www.kaggle.com/)

   Data science competitions are there for you to participate. Use Kaggle effectively not exhaustively.

3. UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php)

   This is so far the most popular data collection. This is often the first location that you should visit to find a dataset.

4. GitHub (https://github.com/)

   GitHub is one of the popular source code hosting services. Create an account there if you do not have one already.

DataDisca Recommended Learning Materials

All of the following are freely available on YouTube

Basic Data Science

1. Machine Learning by Andrew Ng
   • https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN
2. Deep Learning Specialization by Andrew Ng
   1. Course 1
      • https://www.youtube.com/watch?v=CS4cs9xVecg&list=PLkDaE6sCZn6Ec-XTbcX1uRg2_u4xOEky0
   2. Course 2
      • https://www.youtube.com/watch?v=1waHlpKiNyY&list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc
   3. Course 3
      • https://www.youtube.com/watch?v=dFX8k1kXhOw&list=PLkDaE6sCZn6E7jZ9sN_xHwSHOdjUxUW_b
   4. Course 4
      • https://www.youtube.com/watch?v=ArPaAX_PhIs&list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF
   5. Course 5
      • https://www.youtube.com/playlist?list=PLBKkR6WkznLd1QC3jZw8qMbkBhfmw-hjM
      • https://www.youtube.com/watch?v=_i3aqgKVNQI&list=PLkDaE6sCZn6F6wUI9tvS_Gw1vaFAx6rd6

Standard Courses

1. Stanford CS229: Machine Learning | Autumn 2018
   • https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU
2. Stanford CS224N: NLP with Deep Learning | Winter 2019
   • https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z
3. Stanford CS230: Deep Learning | Autumn 2018
   • https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb
4. Stanford Computer Vision
   • https://www.youtube.com/playlist?list=PLf7L7Kg8_FNxHATtLwDceyh72QQL9pvpQ
5. Academic Writing
   • https://www.youtube.com/playlist?list=PLfStqOUD1DapggSvxsmDJ_tsbb3envUxK

Useful Links

1. Python
   • Intermediate Python
     • https://www.youtube.com/watch?v=HGOBQPFzWKo&t=8412s
2. Tableau
   • Tableau Full Course In 8 Hours https://www.youtube.com/watch?v=Wh4sCCZjOwo
3. English
   • Some practical YouTube channels

Data Repositories

Access Here

Note

Copyrights of technologies and contents are owned by their respective owners.