| 1. Basic NumPy arrays zeros().mp4 | 8.7 MB | ||
| 1. Basic NumPy arrays zeros().srt | 1.4 KB | ||
| 1. Installing Libraries.mp4 | 8.4 MB | ||
| 1. Installing Libraries.srt | 716.8 B | ||
| 1. Introduction to NumPy arrays.mp4 | 7.2 MB | ||
| 1. Introduction to NumPy arrays.srt | 716.8 B | ||
| 1. Introduction to Python.mp4 | 5.8 MB | ||
| 1. Introduction to Python.srt | 1.1 KB | ||
| 1. What is a Python Pandas DataFrame.mp4 | 6.4 MB | ||
| 1. What is a Python Pandas DataFrame.srt | 1.1 KB | ||
| 10. Arithmetic Operators.mp4 | 8 MB | ||
| 10. Arithmetic Operators.srt | 2 KB | ||
| 10. Element wise addition.mp4 | 9 MB | ||
| 10. Element wise addition.srt | 1.8 KB | ||
| 10. Statistical summary of the DataFrame.mp4 | 6.6 MB | ||
| 10. Statistical summary of the DataFrame.srt | 512 B | ||
| 11. Comparison Operators.mp4 | 5.5 MB | ||
| 11. Comparison Operators.srt | 819.2 B | ||
| 11. Element wise subtraction.mp4 | 6.4 MB | ||
| 11. Element wise subtraction.srt | 819.2 B | ||
| 11. Slicing rows using bracket operators.mp4 | 8.8 MB | ||
| 11. Slicing rows using bracket operators.srt | 1.4 KB | ||
| 12. Element wise multiplication.mp4 | 6.1 MB | ||
| 12. Element wise multiplication.srt | 716.8 B | ||
| 12. Indexing columns using bracket operators.mp4 | 5.9 MB | ||
| 12. Indexing columns using bracket operators.srt | 921.6 B | ||
| 12. Logical Operators.mp4 | 12.7 MB | ||
| 12. Logical Operators.srt | 2.9 KB | ||
| 13. Boolean list.mp4 | 8.1 MB | ||
| 13. Boolean list.srt | 1.2 KB | ||
| 13. Conditional statements.mp4 | 8.5 MB | ||
| 13. Conditional statements.srt | 2 KB | ||
| 13. Element wise division.mp4 | 6.4 MB | ||
| 13. Element wise division.srt | 1 KB | ||
| 14. Filtering Rows.mp4 | 9.2 MB | ||
| 14. Filtering Rows.srt | 1.5 KB | ||
| 14. Loops.mp4 | 14.9 MB | ||
| 14. Loops.srt | 3.8 KB | ||
| 14. Matrix multiplication.mp4 | 8.9 MB | ||
| 14. Matrix multiplication.srt | 1.5 KB | ||
| 15. Filtering rows using & and operators.mp4 | 9.4 MB | ||
| 15. Filtering rows using & and operators.srt | 2 KB | ||
| 15. Sequences Lists.mp4 | 12.3 MB | ||
| 15. Sequences Lists.srt | 3.3 KB | ||
| 15. Statistics.mp4 | 10.3 MB | ||
| 15. Statistics.srt | 2.7 KB | ||
| 16. Filtering data using loc().mp4 | 21.7 MB | ||
| 16. Filtering data using loc().srt | 3.8 KB | ||
| 16. Sequences Dictionaries.mp4 | 10.8 MB | ||
| 16. Sequences Dictionaries.srt | 2.6 KB | ||
| 17. Adding and deleting rows and columns.mp4 | 17.3 MB | ||
| 17. Adding and deleting rows and columns.srt | 3 KB | ||
| 17. Sequences Tuples.mp4 | 8.7 MB | ||
| 17. Sequences Tuples.srt | 1.2 KB | ||
| 18. Functions Built-in Functions.mp4 | 6 MB | ||
| 18. Functions Built-in Functions.srt | 409.6 B | ||
| 18. Sorting Values.mp4 | 10.7 MB | ||
| 18. Sorting Values.srt | 1.6 KB | ||
| 19. Exporting and saving pandas DataFrames.mp4 | 10.7 MB | ||
| 19. Exporting and saving pandas DataFrames.srt | 1.3 KB | ||
| 19. Functions User-defined Functions.mp4 | 10.2 MB | ||
| 19. Functions User-defined Functions.srt | 3.4 KB | ||
| 2. Basic NumPy arrays ones().mp4 | 5.5 MB | ||
| 2. Basic NumPy arrays ones().srt | 819.2 B | ||
| 2. Creating NumPy arrays.mp4 | 32.6 MB | ||
| 2. Creating NumPy arrays.srt | 4.5 KB | ||
| 2. Importing Libraries.mp4 | 8.7 MB | ||
| 2. Importing Libraries.srt | 1.7 KB | ||
| 2. Setting up Python.mp4 | 15.5 MB | ||
| 2. Setting up Python.srt | 3.1 KB | ||
| 2. What is a Python Pandas Series.mp4 | 4.1 MB | ||
| 2. What is a Python Pandas Series.srt | 716.8 B | ||
| 20. Concatenating DataFrames.mp4 | 7.6 MB | ||
| 20. Concatenating DataFrames.srt | 921.6 B | ||
| 21. groupby().mp4 | 16.9 MB | ||
| 21. groupby().srt | 3.3 KB | ||
| 3. Basic NumPy arrays full().mp4 | 3.9 MB | ||
| 3. Basic NumPy arrays full().srt | 1.1 KB | ||
| 3. DataFrame vs Series.mp4 | 3.8 MB | ||
| 3. DataFrame vs Series.srt | 409.6 B | ||
| 3. Indexing NumPy arrays.mp4 | 26.6 MB | ||
| 3. Indexing NumPy arrays.srt | 4.9 KB | ||
| 3. Pandas Library for Data Science.mp4 | 3 MB | ||
| 3. Pandas Library for Data Science.srt | 716.8 B | ||
| 3. What is Jupyter.mp4 | 6.1 MB | ||
| 3. What is Jupyter.srt | 1 KB | ||
| 4. Adding a scalar.mp4 | 8.6 MB | ||
| 4. Adding a scalar.srt | 1.3 KB | ||
| 4. Anaconda Installation Windows, Mac & Ubuntu.mp4 | 31.1 MB | ||
| 4. Anaconda Installation Windows, Mac & Ubuntu.srt | 5.9 KB | ||
| 4. Array shape.mp4 | 4.3 MB | ||
| 4. Array shape.srt | 512 B | ||
| 4. Creating a DataFrame using lists.mp4 | 13.6 MB | ||
| 4. Creating a DataFrame using lists.srt | 3.6 KB | ||
| 4. NumPy Library for Data Science.mp4 | 5.3 MB | ||
| 4. NumPy Library for Data Science.srt | 819.2 B | ||
| 5. Creating a DataFrame using a dictionary.mp4 | 5.7 MB | ||
| 5. Creating a DataFrame using a dictionary.srt | 1 KB | ||
| 5. How to implement Python in Jupyter.mp4 | 4.5 MB | ||
| 5. How to implement Python in Jupyter.srt | 819.2 B | ||
| 5. Iterating Over NumPy Arrays.mp4 | 20.3 MB | ||
| 5. Iterating Over NumPy Arrays.srt | 4.4 KB | ||
| 5. Pandas vs NumPy.mp4 | 6.3 MB | ||
| 5. Pandas vs NumPy.srt | 512 B | ||
| 5. Subtracting a scalar.mp4 | 6.7 MB | ||
| 5. Subtracting a scalar.srt | 921.6 B | ||
| 6. Loading CSV data into python.mp4 | 10.1 MB | ||
| 6. Loading CSV data into python.srt | 1.6 KB | ||
| 6. Managing Directories in Jupyter Notebook.mp4 | 16.4 MB | ||
| 6. Managing Directories in Jupyter Notebook.srt | 3.4 KB | ||
| 6. Matplotlib Library for Data Science.mp4 | 5.7 MB | ||
| 6. Matplotlib Library for Data Science.srt | 512 B | ||
| 6. Multiplying by a scalar.mp4 | 9.1 MB | ||
| 6. Multiplying by a scalar.srt | 1 KB | ||
| 7. Changing the Index Column.mp4 | 6.5 MB | ||
| 7. Changing the Index Column.srt | 1 KB | ||
| 7. Dividing by a scalar.mp4 | 8.9 MB | ||
| 7. Dividing by a scalar.srt | 1.5 KB | ||
| 7. InputOutput.mp4 | 8 MB | ||
| 7. InputOutput.srt | 2 KB | ||
| 7. Seaborn Library for Data Science.mp4 | 2.8 MB | ||
| 7. Seaborn Library for Data Science.srt | 307.2 B | ||
| 8. Inplace.mp4 | 8.5 MB | ||
| 8. Inplace.srt | 1.3 KB | ||
| 8. Raise to a power.mp4 | 5.3 MB | ||
| 8. Raise to a power.srt | 716.8 B | ||
| 8. Working with different datatypes.mp4 | 6.6 MB | ||
| 8. Working with different datatypes.srt | 1.1 KB | ||
| 9. Examining the DataFrame Head & Tail.mp4 | 4.8 MB | ||
| 9. Examining the DataFrame Head & Tail.srt | 716.8 B | ||
| 9. Transpose.mp4 | 5.3 MB | ||
| 9. Transpose.srt | 819.2 B | ||
| 9. Variables.mp4 | 8.7 MB | ||
| 9. Variables.srt | 1.9 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 136 total files | |||
Introduction to Python, Numpy & Pandas by AlgoLab Finance
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 67 lectures (1h 56m) | Size: 536.5 MB
Master Python, NumPy & Pandas for Data Science in a fun and interesting manner
What you'll learn:
Learn to use Pandas for Data Analysis
Learn to work with numerical data in Python
Learn statistics and math with Python
Learn how to code in Jupiter Notebook
Learn how to install packages in Python
Requirements
No prior data science knowledge required
No programming experience needed
Description
When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.
Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 699.7 MB | freecoursewb | 2 weeks | 4 | 1 | |
| 2.7 GB | freecoursewb | 3 weeks | 12 | 1 | |
| 1.7 GB | freecoursewb | 1 month | 20 | 8 | |
| 1.2 GB | freecoursewb | 1 month | 5 | 8 | |
| 794.5 MB | freecoursewb | 1 month | 0 | 0 |
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