Udemy - Data Science on Python 2021-22

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[ TutPig.com ] Udemy - Data Science on Python 2021-22
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Introduction to Python
    • 1. History and Introduction of Python.mp4 (49.1 MB)
    • 1. History and Introduction of Python.srt (7.1 KB)
    • 2. Installation of Anaconda Navigator for Jupyter Notebook and Spyder.mp4 (45.0 MB)
    • 2. Installation of Anaconda Navigator for Jupyter Notebook and Spyder.srt (7.7 KB)
    • 3. Spyder IDE.mp4 (57.3 MB)
    • 3. Spyder IDE.srt (9.6 KB)
    • 4. Jupyter Notebook.mp4 (17.1 MB)
    • 4. Jupyter Notebook.srt (4.1 KB)
    • 5. Basic Variables.mp4 (30.8 MB)
    • 5. Basic Variables.srt (6.8 KB)
    • 6. Numeric Operators.mp4 (20.4 MB)
    • 6. Numeric Operators.srt (4.5 KB)
    • 7. Isinstance and Operators.mp4 (29.1 MB)
    • 7. Isinstance and Operators.srt (8.6 KB)
    • 8. Types of data.mp4 (37.8 MB)
    • 8. Types of data.srt (9.6 KB)
    10. Building a Predictive Model (Logistic Regression) in Python
    • 1. Introduction to Logistic Regression Model.mp4 (40.7 MB)
    • 1. Introduction to Logistic Regression Model.srt (9.5 KB)
    • 2. Log odds Ratio.mp4 (40.4 MB)
    • 2. Log odds Ratio.srt (9.2 KB)
    • 3. Method of Logistic Regression.mp4 (15.7 MB)
    • 3. Method of Logistic Regression.srt (10.2 KB)
    • 4. Receivers Operating Characteristics Curve.mp4 (15.9 MB)
    • 4. Receivers Operating Characteristics Curve.srt (3.2 KB)
    • 5. Application of logistic regression in Python-Part 1.mp4 (43.3 MB)
    • 5. Application of logistic regression in Python-Part 1.srt (3.4 KB)
    • 6. Application of logistic regression in Python-Part 2.mp4 (39.0 MB)
    • 6. Application of logistic regression in Python-Part 2.srt (3.8 KB)
    • 7. Application of logistic regression in Python-Part 3.mp4 (44.9 MB)
    • 7. Application of logistic regression in Python-Part 3.srt (4.2 KB)
    • 8. Application of logistic regression in Python-Part 4.mp4 (46.3 MB)
    • 8. Application of logistic regression in Python-Part 4.srt (3.9 KB)
    11. Time Series theory and its application in Python
    • 1. Time Series Modelling (Theory).mp4 (62.3 MB)
    • 1. Time Series Modelling (Theory).srt (12.4 KB)
    • 2. Smoothing and Stationarity of Time Series.mp4 (43.6 MB)
    • 2. Smoothing and Stationarity of Time Series.srt (8.9 KB)
    • 3. AR, MA, ARIMA.mp4 (67.0 MB)
    • 3. AR, MA, ARIMA.srt (13.2 KB)
    • 4. Time series application in Python- Part 1.mp4 (68.4 MB)
    • 4. Time series application in Python- Part 1.srt (6.4 KB)
    • 5. Time series application in Python- Part 2.mp4 (28.9 MB)
    • 5. Time series application in Python- Part 2.srt (3.3 KB)
    • 6. Time series application in Python- Part 3.mp4 (47.0 MB)
    • 6. Time series application in Python- Part 3.srt (4.3 KB)
    • 7. Time series application in Python- Part 4.mp4 (44.7 MB)
    • 7. Time series application in Python- Part 4.srt (4.1 KB)
    • 8. Time series application in Python- Part 5.mp4 (53.3 MB)
    • 8. Time series application in Python- Part 5.srt (5.7 KB)
    • 9. Time series application in Python- Part 6.mp4 (107.1 MB)
    • 9. Time series application in Python- Part 6.srt (9.1 KB)
    12. Web Scraping using BeautifulSoup in Python
    • 1. A simple example on Web Scraping using Beautiful Soup in Python.mp4 (69.0 MB)
    • 1. A simple example on Web Scraping using Beautiful Soup in Python.srt (12.0 KB)
    2. Data Structures and Conditional Executions in Python
    • 1. Lists.mp4 (48.7 MB)
    • 1. Lists.srt (8.1 KB)
    • 2. Tuples.mp4 (61.3 MB)
    • 2. Tuples.srt (11.1 KB)
    • 3. Dictionaries.mp4 (89.8 MB)
    • 3. Dictionaries.srt (13.2 KB)
    • 4. Complex lists and repetitions.mp4 (19.8 MB)
    • 4. Complex lists and repetitions.srt (11.9 KB)
    • 5. More on Lists and Sets.mp4 (63.6 MB)
    • 5. More on Lists and Sets.srt (11.3 KB)
    • 6. Strings.mp4 (33.9 MB)
    • 6. Strings.srt (5.3 KB)
    3. Conditions and Loops in Python
    • 1. Application of If Condition in Python.mp4 (44.9 MB)
    • 1. Application of If Condition in Python.srt (8.8 KB)
    • 2. Nested IfElse condition in Python.mp4 (35.5 MB)
    • 2. Nested IfElse condition in Python.srt (5.7 KB)
    • 3. Examples on For and Nested For in Python.mp4 (64.1 MB)
    • 3. Examples on For and Nested For in Python.srt (10.1 KB)
    • 4. Application of Switch Case in Python.mp4 (23.2 MB)
    • 4. Application of Switch Case in Python.srt (3.3 KB)
    • 5. Pass, Break and Continue explained in Python.mp4 (50.4 MB)
    • 5. Pass, Break and Continue explained in Python.srt (8.3 KB)
    4. Working with Pandas in Python
    • 1. Introduction to Pandas.mp4 (27.5 MB)
    • 1. Introduction to Pandas.srt (4.0 KB)
    • 2. Subsetting and missing value imputation in Python.mp4 (52.9 MB)
    • 2. Subsetting and missing value imputation in Python.srt (7.1 KB)
    • 3. Crosstabs, Merging and Sorting in Python.mp4 (84.4 MB)
    • 3. Crosstabs, Merging and Sorting in Python.srt (8.6 KB)
    • 4. Exploring with Pandas.mp4 (76.7 MB)
    • 4. Exploring with Pandas.srt (9.8 KB)
    • 5. Data Munging with Pandas.mp4 (40.1 MB)
    • 5. Data Munging with Pandas.srt (4.3 KB)
    5. Plotting in Python
    • 1. Basic Plotting with matplotlib in Python.mp4 (39.7 MB)
    • 1. Basic Plotting with matplotlib in Python.srt (6.3 KB)
    • 2. Bar Chart Application in Python.mp4 (52.6 MB)
    • 2. Bar Chart Application in Python.srt (7.0 KB)
    • 3. Histogram in Python.mp4 (59.8 MB)
    • 3. Histogram in Python.srt (6.4 KB)
    • 4. Application of Grouped Bar Graph in Python.mp4 (67.8 MB)
    • 4. Application of Grouped Bar Graph in Python.srt (9.4 KB)
    • 5. Scatter Plot in Python.mp4 (82.1 MB)
    • 5. Scatter Plot in Python.srt (8.3 KB)
    • 6. Stackplot in Python.mp4 (98.1 MB)
    • 6. Stackplot in Python.srt (12.0 KB)
    • 7. Plotting with Plotly in Python Part-1.mp4 (23.2 MB)
    • 7. Plotting with Plotly in Python Part-1.srt (4.9 KB)

Description

Data Science on Python 2021-22



https://TutPig.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 86 lectures (10h 18m) | Size: 2.9 GB
A clear understanding about the data science theory, techniques and its application in Jupyter Notebook platform
What you'll learn:
This course will review common Python functionality and features along with Jupyter Notebook
The students will learn about the toolkits Python has for data cleaning and processing — pandas
The students will learn to create stunning data visualizations with matplotlib, and seaborn
The students will learn how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates
The students will be introduced to a variety of statistical techniques such a distributions, sampling and t-tests using real-world data
The students will involve into data cleaning activity and provide evidence for (or against!) a given hypothesis
The students will learn performing dimension reduction techniques like Factor analysis and Cluster Analysis
The students will learn how to perform predictive modelling using Python
The students will gain intensive knowledge in the spheres of Linear Regression, Logistic Regression and Time Series Regression using packages like Pandas, Numpy, scikit learn and others
The topics that will be covered in this course are listed below:
1. Introduction to Python
2. Data Structures and Conditional Executions in Python
3. Conditions and Loops in Python
4. Working with Pandas in Python
5. Plotting in Python
6. Statistical Analysis and Application in Python (part I)
7. Statistical Analysis and Application in Python (part II)
8. Theory of Factor and Cluster Analysis in Python
9. Building a Predictive Model (Linear Regression) in Python
10. Building a Predictive Model (Logistic Regression) in Python
11. Time Series theory and its application in Python
12. Web Scraping using BeautifulSoup in Python

Requirements
For better understanding Learn Python from Scratch by OrangeTree Global is recommended

Description
The following topics will be covered as part of this series. Each topic is described in detail with hands-on exercises done on Jupyter Notebook to help students learn with ease. We will cover all the nitty-gritty that you need to know to get started with Python along with the correction and handling of errors as and when they pop-up. The program builds a solid foundation by covering the most popular and widely used data science technologies and its applications.

Introduction to Python



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Udemy - Data Science on Python 2021-22


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Udemy - Data Science on Python 2021-22


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