Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2 GB
  • Uploaded Bytutsnode
  • Downloads137
  • Last checkedSep. 29th '21
  • Date uploadedSep. 26th '21
  • Seeders 27
  • Leechers13
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Infohash : B34650AD4D11A1700D5F3D8B3A002B32BE9AA382


Description

This course is ideal for you, if you wish is to start your path to becoming a Data Scientist!

Data Scientist is one of the hottest jobs recently the United States and in Europe and it is a rewarding career with a high average salary.

The massive amount of data has revolutionized companies and those who have used these big data has an edge in competition. These companies need data scientist who are proficient at handling, managing, analyzing, and understanding trends in data.

This course is designed for both beginners with some programming experience or experienced developers looking to extend their knowledge in Data Science!

I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single skill in data manipulation using Python libraries for data science.

In this comprehensive course, I will guide you to learn how to use the power of Python to manipulate, explore, and analyze data, and to create beautiful visualizations.

My course is equivalent to Data Science bootcamps that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With over 90 HD video lectures, including all examples presented in this course which are provided in detailed code notebooks for every lecture. This course is one of the most comprehensive course for using Python for data science on Udemy!

I will teach you how to use Python to manipulate and to explore raw datasets, how to use python libraries for data science such as Pandas, NumPy, Matplotlib, and Seaborn, how to use the most common data structures for data science in python, how to create amazing data visualizations, and most importantly how to prepare your datasets for advanced data analysis and machine learning models.

Here a few of the topics that you will be learning in this comprehensive course:

How to Set Your Python Environment
How to Work with Jupyter Notebooks
Learning Data Structures and Sequences for Data Science In Python
How to Create Functions in Python
Mastering NumPy Arrays
Mastering Pandas Dataframe and Series
Learning Data Cleaning and Preprocessing
Mastering Data Wrangling
Learning Hierarchical Indexing
Learning Combining and Merging Datasets
Learning Reshaping and Pivoting DataFrames
Mastering Data Visualizations with Matplotlib, Pandas and Seaborn
Manipulating Time Series
Practicing with Real World Data Analysis Example

Enroll in the course and start your path to becoming a data scientist today!
Who this course is for:

I designed this course to be valuable for people who are interested in data science and data analysis with python.
If you want to learn data science with python, this course will be a valuable starting point.
This course is for you if your intention is to learn how to use Python’s data science tools and libraries such as Jupyter notebook, NumPy, Pandas, Matplotlib, Seaborn, and related tools to effectively store, manipulate, and gain insight from data.

Requirements

It is advantageous to have basic python knowledge, but it is not required to understand the material in this course. However, people with no previous basic knowledge of python need to focus first on module 2, 3, 4, and 5, that would be enough to comprehend the rest material in this course.

Last Updated 9/2021

Files:

Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn [TutsNode.com] - Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn 15. Module 15 Real World Data Analysis Example
  • 5. Housing Dataset Analysis -Part Five.mp4 (40.0 MB)
  • 5. Housing Dataset Analysis -Part Five.srt (8.7 KB)
  • 4. Housing Dataset Analysis -Part Four.srt (6.2 KB)
  • 3. Housing Dataset Analysis -Part Three.srt (4.5 KB)
  • 1. Housing Dataset Analysis -Part One.srt (4.2 KB)
  • 2. Housing Dataset Analysis -Part Two.srt (4.2 KB)
  • 4. Housing Dataset Analysis -Part Four.mp4 (31.9 MB)
  • 3. Housing Dataset Analysis -Part Three.mp4 (29.7 MB)
  • 2. Housing Dataset Analysis -Part Two.mp4 (23.3 MB)
  • 1. Housing Dataset Analysis -Part One.mp4 (10.8 MB)
4. Module 4 Data Structures And Sequences In Python
  • 2. List.srt (9.2 KB)
  • 5. Short Quiz.html (0.2 KB)
  • 4. Set.srt (2.3 KB)
  • 1. Tuple.srt (5.0 KB)
  • 3. Dictionary.srt (3.3 KB)
  • 2. List.mp4 (36.4 MB)
  • 1. Tuple.mp4 (19.8 MB)
  • 3. Dictionary.mp4 (14.8 MB)
  • 4. Set.mp4 (4.5 MB)
1. Introduction
  • 2. How to Download Course Notebooks.mp4 (38.1 MB)
  • 2.1 How to download course notebooks.pdf (140.2 KB)
  • 2. How to Download Course Notebooks.srt (6.2 KB)
  • 3. Overview of Course Curriculum.srt (5.9 KB)
  • 1. Course Introduction.srt (4.5 KB)
  • 3. Overview of Course Curriculum.mp4 (27.1 MB)
  • 1. Course Introduction.mp4 (13.9 MB)
11. Module 11 Data Wrangling2 Combining and Merging Datasets
  • 1. Merging Datasets on Keys (common columns).srt (8.2 KB)
  • 3. Concatenating Along an Axis.srt (7.2 KB)
  • 1. Merging Datasets on Keys (common columns).mp4 (36.9 MB)
  • 2. Merging Datasets on Index.srt (3.1 KB)
  • 4. Short Quiz.html (0.2 KB)
  • 3. Concatenating Along an Axis.mp4 (30.1 MB)
  • 2. Merging Datasets on Index.mp4 (15.0 MB)
7. Module 7 Pandas Dataframe
  • 2. Dataframe in Pandas.srt (7.7 KB)
  • 6. Indexing, Slicing and Filtering.srt (6.1 KB)
  • 1. Series in Pandas.srt (5.9 KB)
  • 7. Arithmetic with Dataframe.srt (5.0 KB)
  • 9. Descriptive Statistics with Dataframe.srt (4.7 KB)
  • 8. Sorting Series and Dataframe.srt (4.4 KB)
  • 4. Reindexing in Series and DataFrames.srt (3.0 KB)
  • 10. Correlation and Covariance.srt (4.1 KB)
  • 3. Index Objects.srt (4.0 KB)
  • 11. Short Quiz.html (0.2 KB)
  • 5. Deleting Rows and Columns.srt (3.2 KB)
  • 2. Dataframe in Pandas.mp4 (34.0 MB)
  • 6. Indexing, Slicing and Filtering.mp4 (25.3 MB)
  • 1. Series in Pandas.mp4 (24.6 MB)
  • 7. Arithmetic with Dataframe.mp4 (22.1 MB)
  • 9. Descriptive Statistics with Dataframe.mp4 (20.3 MB)
  • 8. Sorting Series and Dataframe.mp4 (20.3 MB)
  • 3. Index Objects.mp4 (18.8 MB)
  • 10. Correlation and Covariance.mp4 (18.7 MB)
  • 4. Reindexing in Series and DataFrames.mp4 (13.4 MB)
  • 5. Deleting Rows and Columns.mp4 (5.4 MB)
13. Module 13 Data Visualization with Matplotlib and Seaborn
  • 7. Adding Annotations and Drawings on a Plot.srt (7.6 KB)
  • 2. Creating Figures and Subplots.srt (6.7 KB)
  • 14. Factor Plots for Categorical Data.srt (6.4 KB)
  • 11. Bar Plots with Seaborn.srt (5.6 KB)
  • 13. Scatter Plots and Pair Plots.srt (5.4 KB)
  • 12. Histograms and Density Plots.srt (5.1 KB)
  • 6. Adding Texts and Arrows on a Plot.srt (4.9 KB)
  • 4. Customizing Ticks and Labels.srt (4.9 KB)
  • 9. Line Plots with Dataframe.srt (4.8 KB)
  • 5. Adding Legends.srt (4.4 KB)
  • 10. Bar Plots with Dataframes.srt (4.2 KB)
  • 3. Changing Colors, Markers and Linestyle.srt (4.0 KB)
  • 8. Saving Plots to a File.srt (3.9 KB)
  • 1. Introducing Matplotlib Library.srt (3.2 KB)
  • 15. Short Quiz.html (0.2 KB)
  • 13. Scatter Plots and Pair Plots.mp4 (35.8 MB)
  • 7. Adding Annotations and Drawings on a Plot.mp4 (31.5 MB)
  • 2. Creating Figures and Subplots.mp4 (31.2 MB)
  • 4. Customizing Ticks and Labels.mp4 (28.6 MB)
  • 12. Histograms and Density Plots.mp4 (27.8 MB)
  • 14. Factor Plots for Categorical Data.mp4 (27.3 MB)
  • 9. Line Plots with Dataframe.mp4 (26.7 MB)
  • 11. Bar Plots with Seaborn.mp4 (25.7 MB)
  • 5. Adding Legends.mp4 (24.6 MB)
  • 6. Adding Texts and Arrows on a Plot.mp4 (23.4 MB)
  • 3. Changing Colors, Markers and Linestyle.mp4 (21.9 MB)
  • 10. Bar Plots with Dataframes.mp4 (20.6 MB)
  • 8. Saving Plots to a File.mp4 (18.1 MB)
  • 1. Introducing Matplotlib Library.mp4 (11.5 MB)
6. Module 6 NumPy Arrays
  • 2. Creating Ndarrays.srt (7.4 KB)
  • 10. Mathematical and Statistical Methods.srt (6.6 KB)
  • 7. Boolean Indexing.srt (6.1 KB)
  • 1. What Is NumPy Arrays (Ndarrays).srt (2.9 KB)
  • 3. Data Types for Ndarrays.srt (4.9 KB)
  • 6. Indexing and Slicing-Part two.srt (4.7 KB)
  • 9. Transposing Arrays.srt (2.0 KB)
  • 13. Short Quiz.html (0.2 KB)
  • 8. Fancy Indexing.srt (4.3 KB)
  • 5. Indexing and Slicing-Part One.srt (4.2 KB)
  • 11. Sorting Arrays.srt (3.5 KB)
  • 12. File Input and Output with Arrays.srt (3.2 KB)
  • 4. Arithmetic with NumPy Arrays.srt (3.1 KB)
  • 10. Mathematical and Statistical Methods.mp4 (35.4 MB)
  • 2. Creating Ndarrays.mp4 (31.2 MB)
  • 7. Boolean Indexing.mp4 (28.2 MB)
  • 11. Sorting Arrays.mp4 (21.2 MB)
  • 3. Data Types for Ndarrays.mp4 (20.6 MB)
  • 6. Indexing and Slicing-Part two.mp4 (19.8 MB)
  • 8. Fancy Indexing.mp4 (18.0 MB)
  • 5. Indexing and Slicing-Part One.mp4 (17.8 MB)
  • 12. File Input and Output with Arrays.mp4 (15.6 MB)
  • 4. Arithmetic with NumPy Arrays.mp4 (12.9 MB)
  • 1. What Is NumPy Arrays (Ndarrays).mp4 (11.5 MB)

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