| 1. A simple example on Web Scraping using Beautiful Soup in Python.mp4 | 69 MB | ||
| 1. A simple example on Web Scraping using Beautiful Soup in Python.srt | 12 KB | ||
| 1. Application of If Condition in Python.mp4 | 44.9 MB | ||
| 1. Application of If Condition in Python.srt | 8.8 KB | ||
| 1. Basic Plotting with matplotlib in Python.mp4 | 39.7 MB | ||
| 1. Basic Plotting with matplotlib in Python.srt | 6.3 KB | ||
| 1. History and Introduction of Python.mp4 | 49.1 MB | ||
| 1. History and Introduction of Python.srt | 7.1 KB | ||
| 1. Introduction to Analysis of Variance.mp4 | 13.6 MB | ||
| 1. Introduction to Analysis of Variance.srt | 5.3 KB | ||
| 1. Introduction to Logistic Regression Model.mp4 | 40.7 MB | ||
| 1. Introduction to Logistic Regression Model.srt | 9.5 KB | ||
| 1. Introduction to Pandas.mp4 | 27.5 MB | ||
| 1. Introduction to Pandas.srt | 4 KB | ||
| 1. Lists.mp4 | 48.7 MB | ||
| 1. Lists.srt | 8.1 KB | ||
| 1. Sampling Theory and Estimation.mp4 | 52.9 MB | ||
| 1. Sampling Theory and Estimation.srt | 13.4 KB | ||
| 1. Simple Linear Regression Model (Theory).mp4 | 52.8 MB | ||
| 1. Simple Linear Regression Model (Theory).srt | 13.1 KB | ||
| 1. Theory on Factor Analysis.mp4 | 27 MB | ||
| 1. Theory on Factor Analysis.srt | 9.1 KB | ||
| 1. Time Series Modelling (Theory).mp4 | 62.3 MB | ||
| 1. Time Series Modelling (Theory).srt | 12.4 KB | ||
| 10. Cluster Analysis Application in Python- Part 1.mp4 | 25.9 MB | ||
| 10. Cluster Analysis Application in Python- Part 1.srt | 2.6 KB | ||
| 10. Understanding Chi-Square Test.mp4 | 16.4 MB | ||
| 10. Understanding Chi-Square Test.srt | 6.1 KB | ||
| 11. Application of Chi-Square Test in Python.mp4 | 39.8 MB | ||
| 11. Application of Chi-Square Test in Python.srt | 9.4 KB | ||
| 11. Cluster Analysis Application in Python- Part 2.mp4 | 34.1 MB | ||
| 11. Cluster Analysis Application in Python- Part 2.srt | 3.6 KB | ||
| 12. Cluster Analysis Application in Python-3.mp4 | 51.3 MB | ||
| 12. Cluster Analysis Application in Python-3.srt | 6 KB | ||
| 12. Correlation and Partial Correlation Concept.mp4 | 16.4 MB | ||
| 12. Correlation and Partial Correlation Concept.srt | 6.1 KB | ||
| 2. Bar Chart Application in Python.mp4 | 52.6 MB | ||
| 2. Bar Chart Application in Python.srt | 7 KB | ||
| 2. Calculation of Total Sum of Square, Error Sum of Square, and Model Sum of Square.mp4 | 23.5 MB | ||
| 2. Calculation of Total Sum of Square, Error Sum of Square, and Model Sum of Square.srt | 8 KB | ||
| 2. Installation of Anaconda Navigator for Jupyter Notebook and Spyder.mp4 | 45 MB | ||
| 2. Installation of Anaconda Navigator for Jupyter Notebook and Spyder.srt | 7.7 KB | ||
| 2. Log odds Ratio.mp4 | 40.4 MB | ||
| 2. Log odds Ratio.srt | 9.2 KB | ||
| 2. Method of Least Squares and Goodness of Fit.mp4 | 42.5 MB | ||
| 2. Method of Least Squares and Goodness of Fit.srt | 12.3 KB | ||
| 2. Nested IfElse condition in Python.mp4 | 35.5 MB | ||
| 2. Nested IfElse condition in Python.srt | 5.7 KB | ||
| 2. Sampling Bias, Parameters and Estimates.mp4 | 26.6 MB | ||
| 2. Sampling Bias, Parameters and Estimates.srt | 6.1 KB | ||
| 2. Smoothing and Stationarity of Time Series.mp4 | 43.6 MB | ||
| 2. Smoothing and Stationarity of Time Series.srt | 8.9 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 | ||
| 2. Theory of Factor Analysis- Part 1.mp4 | 30.3 MB | ||
| 2. Theory of Factor Analysis- Part 1.srt | 7.1 KB | ||
| 2. Tuples.mp4 | 61.3 MB | ||
| 2. Tuples.srt | 11.1 KB | ||
| 3. AR, MA, ARIMA.mp4 | 67 MB | ||
| 3. AR, MA, ARIMA.srt | 13.2 KB | ||
| 3. Crosstabs, Merging and Sorting in Python.mp4 | 84.4 MB | ||
| 3. Crosstabs, Merging and Sorting in Python.srt | 8.6 KB | ||
| 3. Dictionaries.mp4 | 89.8 MB | ||
| 3. Dictionaries.srt | 13.2 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 | ||
| 3. Goodness of Fit and Multicollinearity.mp4 | 65.4 MB | ||
| 3. Goodness of Fit and Multicollinearity.srt | 15.6 KB | ||
| 3. Histogram in Python.mp4 | 59.8 MB | ||
| 3. Histogram in Python.srt | 6.4 KB | ||
| 3. Introduction to Testing of Hypothesis.mp4 | 53.1 MB | ||
| 3. Introduction to Testing of Hypothesis.srt | 12 KB | ||
| 3. Method of Logistic Regression.mp4 | 15.7 MB | ||
| 3. Method of Logistic Regression.srt | 10.2 KB | ||
| 3. Spyder IDE.mp4 | 57.3 MB | ||
| 3. Spyder IDE.srt | 9.6 KB | ||
| 3. Theory of Factor Analysis- Part 2.mp4 | 44.5 MB | ||
| 3. Theory of Factor Analysis- Part 2.srt | 11.5 KB | ||
| 3. Types of Anova.mp4 | 20.8 MB | ||
| 3. Types of Anova.srt | 5.8 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 | ||
| 4. Application of One-Way Anova in Python.mp4 | 42.8 MB | ||
| 4. Application of One-Way Anova in Python.srt | 9.4 KB | ||
| 4. Application of Switch Case in Python.mp4 | 23.2 MB | ||
| 4. Application of Switch Case in Python.srt | 3.3 KB | ||
| 4. Autocorrelation and Heteroskedasticity.mp4 | 34.2 MB | ||
| 4. Autocorrelation and Heteroskedasticity.srt | 9.3 KB | ||
| 4. Complex lists and repetitions.mp4 | 19.8 MB | ||
| 4. Complex lists and repetitions.srt | 11.9 KB | ||
| 4. Concepts of Hypothesis Testing.mp4 | 44.9 MB | ||
| 4. Concepts of Hypothesis Testing.srt | 9.5 KB | ||
| 4. Exploring with Pandas.mp4 | 76.7 MB | ||
| 4. Exploring with Pandas.srt | 9.8 KB | ||
| 4. Factor Analysis Application in Python- Part 1.mp4 | 6.3 MB | ||
| 4. Factor Analysis Application in Python- Part 1.srt | 2.7 KB | ||
| 4. Jupyter Notebook.mp4 | 17.1 MB | ||
| 4. Jupyter Notebook.srt | 4.1 KB | ||
| 4. Receivers Operating Characteristics Curve.mp4 | 15.9 MB | ||
| 4. Receivers Operating Characteristics Curve.srt | 3.2 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. Application of Two-Way Anova in Python.mp4 | 33 MB | ||
| 5. Application of Two-Way Anova in Python.srt | 6.9 KB | ||
| 5. Application of linear regression in Python-Part 1.mp4 | 47.2 MB | ||
| 5. Application of linear regression in Python-Part 1.srt | 4.9 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 | ||
| 5. Basic Variables.mp4 | 30.8 MB | ||
| 5. Basic Variables.srt | 6.8 KB | ||
| 5. Data Munging with Pandas.mp4 | 40.1 MB | ||
| 5. Data Munging with Pandas.srt | 4.3 KB | ||
| 5. Factor Analysis Application in Python- Part 2.mp4 | 49 MB | ||
| 5. Factor Analysis Application in Python- Part 2.srt | 5 KB | ||
| 5. More on Lists and Sets.mp4 | 63.6 MB | ||
| 5. More on Lists and Sets.srt | 11.3 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 | ||
| 5. Scatter Plot in Python.mp4 | 82.1 MB | ||
| 5. Scatter Plot in Python.srt | 8.3 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 | ||
| 5. What is P-value and its Significance.mp4 | 40.9 MB | ||
| 5. What is P-value and its Significance.srt | 6.7 KB | ||
| 6. Application of linear regression in Python-Part 2.mp4 | 49.2 MB | ||
| 6. Application of linear regression in Python-Part 2.srt | 4.5 KB | ||
| 6. Application of logistic regression in Python-Part 2.mp4 | 39 MB | ||
| 6. Application of logistic regression in Python-Part 2.srt | 3.8 KB | ||
| 6. Factor Analysis Application in Python-Part 3.mp4 | 33.5 MB | ||
| 6. Factor Analysis Application in Python-Part 3.srt | 3.9 KB | ||
| 6. Numeric Operators.mp4 | 20.4 MB | ||
| 6. Numeric Operators.srt | 4.5 KB | ||
| 6. Stackplot in Python.mp4 | 98.1 MB | ||
| 6. Stackplot in Python.srt | 12 KB | ||
| 6. Strings.mp4 | 33.9 MB | ||
| 6. Strings.srt | 5.3 KB | ||
| 6. Time series application in Python- Part 3.mp4 | 47 MB | ||
| 6. Time series application in Python- Part 3.srt | 4.3 KB | ||
| 6. What are various kinds of T-Tests.mp4 | 30 MB | ||
| 6. What are various kinds of T-Tests.srt | 6.1 KB | ||
| 7. Application of One Sample T-Test in Python.mp4 | 81.8 MB | ||
| 7. Application of One Sample T-Test in Python.srt | 13.3 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 | ||
| 7. Isinstance and Operators.mp4 | 29.1 MB | ||
| 7. Isinstance and Operators.srt | 8.6 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 | ||
| 7. Theory on Cluster Analysis- Part 1.mp4 | 30.9 MB | ||
| 7. Theory on Cluster Analysis- Part 1.srt | 8 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. Application of Two Sample T-Test in Python.mp4 | 64.8 MB | ||
| 8. Application of Two Sample T-Test in Python.srt | 9 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 | ||
| 8. Plotting with Plotly in Python Part-2.mp4 | 112.3 MB | ||
| 8. Plotting with Plotly in Python Part-2.srt | 8.9 KB | ||
| 8. Theory on Cluster Analysis- Part 2.mp4 | 26.1 MB | ||
| 8. Theory on Cluster Analysis- Part 2.srt | 7 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 | ||
| 8. Types of data.mp4 | 37.8 MB | ||
| 8. Types of data.srt | 9.6 KB | ||
| 9. Application of Paired Sample T-test in Python.mp4 | 102.6 MB | ||
| 9. Application of Paired Sample T-test in Python.srt | 14.7 KB | ||
| 9. Plotting with Plotly in Python Part-3.mp4 | 77.3 MB | ||
| 9. Plotting with Plotly in Python Part-3.srt | 7.3 KB | ||
| 9. Theory on Cluster Analysis- Part 3.mp4 | 36.8 MB | ||
| 9. Theory on Cluster Analysis- Part 3.srt | 8.6 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 | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 174 total files | |||
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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