| 1. Importing Dataset.mp4 | 70 MB | ||
| 1. Importing Numpy Package and Basic Commands.mp4 | 48.6 MB | ||
| 1. Importing Numpy Package and Basic Commands.srt | 11 KB | ||
| 1. Introduction to Jupyter Notebook.mp4 | 42.9 MB | ||
| 1. Introduction to Jupyter Notebook.srt | 11.3 KB | ||
| 1. Introduction to Numpy.mp4 | 48 MB | ||
| 1. Introduction to Numpy.srt | 9.9 KB | ||
| 1. Introduction to Pandas.mp4 | 41 MB | ||
| 1. Introduction to Pandas.srt | 11.5 KB | ||
| 10. Concatenate Functions.mp4 | 45.9 MB | ||
| 10. Concatenate Functions.srt | 9.1 KB | ||
| 10. Sorting Dataframes.mp4 | 40.3 MB | ||
| 10. Sorting Dataframes.srt | 5.8 KB | ||
| 11. Summary Statistics.mp4 | 42.7 MB | ||
| 11. Summary Statistics.srt | 7.4 KB | ||
| 12. Dealing with Duplicate Values.mp4 | 45.2 MB | ||
| 12. Dealing with Duplicate Values.srt | 7.1 KB | ||
| 2. Comparision Between List.mp4 | 56 MB | ||
| 2. Comparision Between List.srt | 10.6 KB | ||
| 2. Creating Dataframe from Series and Dictionary.mp4 | 73.1 MB | ||
| 2. Creating Dataframe from Series and Dictionary.srt | 11.6 KB | ||
| 2. Head Tail and Unique Function.mp4 | 43.7 MB | ||
| 2. Missing Values Introduction.mp4 | 48.4 MB | ||
| 2. Missing Values Introduction.srt | 11.7 KB | ||
| 3. Accessing Column.mp4 | 45.9 MB | ||
| 3. Accessing Column.srt | 7.8 KB | ||
| 3. Imputation.mp4 | 29 MB | ||
| 3. Imputation.srt | 5.7 KB | ||
| 3. Making Dataframe from Dictionary.mp4 | 55.1 MB | ||
| 3. Making Dataframe from Dictionary.srt | 6.9 KB | ||
| 3. Numpy on Basis of Memory and Time.mp4 | 27.4 MB | ||
| 3. Numpy on Basis of Memory and Time.srt | 5.6 KB | ||
| 4. Concatenate Dataframe.mp4 | 45.9 MB | ||
| 4. Concatenate Dataframe.srt | 9.1 KB | ||
| 4. Rename Variables.mp4 | 45.5 MB | ||
| 4. Why we are using Numpy and why not List.mp4 | 85.3 MB | ||
| 4. Why we are using Numpy and why not List.srt | 15 KB | ||
| 4. Working with Different Conditions.mp4 | 75 MB | ||
| 4. Working with Different Conditions.srt | 13.5 KB | ||
| 5. Dropping Variables.mp4 | 59.4 MB | ||
| 5. Dropping Variables.srt | 7.3 KB | ||
| 5. Joins and Pivot.mp4 | 55.9 MB | ||
| 5. Joins and Pivot.srt | 8 KB | ||
| 5. Numpy Operations and Subsetting.mp4 | 27.4 MB | ||
| 5. Numpy Operations and Subsetting.srt | 5.6 KB | ||
| 6. 2D Numpy Arrays.mp4 | 30.9 MB | ||
| 6. 2D Numpy Arrays.srt | 7.8 KB | ||
| 6. Descriptive Statisitcs.mp4 | 60.9 MB | ||
| 6. Descriptive Statisitcs.srt | 8.3 KB | ||
| 6. Unipivot Dataframe.mp4 | 61.9 MB | ||
| 6. Unipivot Dataframe.srt | 9.4 KB | ||
| 7. Dataframe Operations.mp4 | 69.2 MB | ||
| 7. Dataframe Operations.srt | 9.4 KB | ||
| 7. Group by Functions.mp4 | 72.4 MB | ||
| 7. Group by Functions.srt | 12.2 KB | ||
| 7. Subsetting Operations.mp4 | 44.9 MB | ||
| 7. Subsetting Operations.srt | 9 KB | ||
| 8. Descriptive Statistics in Numpy Arrays.mp4 | 35.9 MB | ||
| 8. Descriptive Statistics in Numpy Arrays.srt | 7.8 KB | ||
| 8. Filtering Functions.mp4 | 75.4 MB | ||
| 8. Filtering Functions.srt | 12.7 KB | ||
| 8. Slicing.mp4 | 51.7 MB | ||
| 8. Slicing.srt | 8 KB | ||
| 9. Array Updating.mp4 | 29.4 MB | ||
| 9. Dicing.mp4 | 47.6 MB | ||
| 9. Dicing.srt | 6.8 KB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 68 total files | |||
Data Visualization with Numpy and Pandas
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 35 lectures (4 hour, 55 mins) | Size: 1.73 GB
Learn how to get you up and running with data analysis and visualization using NumPy and Pandas
What you'll learn
This course has been focused on training folks on Pandas and NumPy. All the concepts that revolve around these libraries will be detailed very precisely through this course. The sole objective of this course is to enrich the trainees with the entire set of skills that are required to work with these python-based libraries.
The goal of this course is to make the trainees expert on working with Pandas and NumPy python libraries. This training will be helping folks to achieve proficiency in introducing the concept of data science with the help of libraries that we will be covering here.
Requirements
Like we always say to candidates what makes the difference is to have a learning attitude, apart from this we will take care of everything.
Basic knowledge of Python and Mathematics (like Linear algebra understanding).
No prior information for machine learning is needed.
Basic computer programming terminologies.
Passion to learn new technology
If You Need More Courses, kindly Visit and Support Us -->> https://FreeCourseWeb.com
Thank You.
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