SkillShare | Data Science And Business Analytics With Python [FCO]

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SkillShare | Data Science And Business Analytics With Python [FCO] (Size: 3.1 GB)
  1. Get Free Premium Accounts Daily On Our Discord Server!.txt 1.3 KB
  1. Introduction.mp4 57.2 MB
  1. Introduction.srt 4.3 KB
  10. Dealing with Huge Data.mp4 123.2 MB
  10. Dealing with Huge Data.srt 11.3 KB
  11. Combining Multiple Data Sources.mp4 44.5 MB
  11. Combining Multiple Data Sources.srt 5.1 KB
  12. Data Cleaning.mp4 13.8 MB
  12. Data Cleaning.srt 1.2 KB
  13. Dealing with Missing Data.mp4 100 MB
  13. Dealing with Missing Data.srt 8 KB
  14. Scaling and Binning Numerical Data.mp4 149.2 MB
  14. Scaling and Binning Numerical Data.srt 11.6 KB
  15. Validating Data with Schemas.mp4 118.4 MB
  15. Validating Data with Schemas.srt 9.7 KB
  16. Encoding Categorical Data.mp4 79.7 MB
  16. Encoding Categorical Data.srt 6.6 KB
  17. Exploratory Data Analysis.mp4 79.7 MB
  17. Exploratory Data Analysis.srt 9.4 KB
  18. Visual Data Exploration.mp4 115.8 MB
  18. Visual Data Exploration.srt 11.4 KB
  19. Descriptive Statistics.mp4 115.8 MB
  19. Descriptive Statistics.srt 6.2 KB
  2. Class Project.mp4 30 MB
  2. Class Project.srt 2.3 KB
  2. OneHack.us Premium Cracked Accounts-Tutorials-Guides-Articles Community Based Forum.url 409.6 B
  20. Dividing Data into Subsets.mp4 162.8 MB
  20. Dividing Data into Subsets.srt 12.9 KB
  21. Finding and Understanding Relations in the Data.mp4 68.1 MB
  21. Finding and Understanding Relations in the Data.srt 5.5 KB
  22. Machine Learning.mp4 19.7 MB
  22. Machine Learning.srt 1.4 KB
  23. Linear Regression for Price Prediction.mp4 93.3 MB
  23. Linear Regression for Price Prediction.srt 15.1 KB
  24. Decision Trees and Random Forests.mp4 73 MB
  24. Decision Trees and Random Forests.srt 7.1 KB
  25. Machine Learning Classification.mp4 119.2 MB
  25. Machine Learning Classification.srt 11.4 KB
  26. Data Clustering for Deeper Insights.mp4 77.1 MB
  26. Data Clustering for Deeper Insights.srt 9 KB
  27. Validation of Machine Learning Models.mp4 115.1 MB
  27. Validation of Machine Learning Models.srt 10.8 KB
  28. ML Interpretability.mp4 191.8 MB
  28. ML Interpretability.srt 17.2 KB
  29. Intro to Machine Learning Fairness.mp4 100.3 MB
  29. Intro to Machine Learning Fairness.srt 8.1 KB
  3. FTUApps.com Download Cracked Developers Applications For Free.url 204.8 B
  3. What is Data Science.mp4 66.4 MB
  3. What is Data Science.srt 5.9 KB
  30. Visuals & Reports.mp4 191.8 MB
  30. Visuals & Reports.srt 307.2 B
  31. Visualization Basics.mp4 191.8 MB
  31. Visualization Basics.srt 8.1 KB
  32. Visualizing Geospatial Information.mp4 65.1 MB
  32. Visualizing Geospatial Information.srt 5.7 KB
  33. Exporting Data and Visualizations.mp4 61.6 MB
  33. Exporting Data and Visualizations.srt 6.8 KB
  34. Creating Presentations directly in Jupyter.mp4 25.4 MB
  34. Creating Presentations directly in Jupyter.srt 2.9 KB
  35. Generating PDF Reports from Jupyter.mp4 32.7 MB
  35. Generating PDF Reports from Jupyter.srt 3.8 KB
  36. Conclusion and Congratulations!.mp4 36 MB
  36. Conclusion and Congratulations!.srt 2.6 KB
  4. FreeCoursesOnline.me Download Udacity, Masterclass, Lynda, PHLearn, etc Free.url 307.2 B
  4. Tool Overview.mp4 70.3 MB
  4. Tool Overview.srt 6 KB
  5. How To Find Help.mp4 133.5 MB
  5. How To Find Help.srt 15.7 KB
  6. Data Loading.mp4 6.9 MB
  6. Data Loading.srt 512 B
  7. Loading Excel and CSV files.mp4 68.8 MB
  7. Loading Excel and CSV files.srt 7.5 KB
  8. Loading Data from SQL.mp4 53.1 MB
  8. Loading Data from SQL.srt 5.5 KB
  9. Loading Any Data File.mp4 75.3 MB
  9. Loading Any Data File.srt 7.4 KB
  Exercises.zip 16.6 MB
  housing.csv 1.4 MB
  notebooks.zip 7.8 MB
  ▲ 79 total files

Description


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[SkillShare] Data Science and Business Analytics with Python [FCO]


Created by : Jesper Dramsch, PhD, Scientist for Machine Learning
Language : English
Released : 2022
Duration : 4h 3m
Course Source : https://www.skillshare.com/classes/Data-Science-and-Business-Analytics-with-Python/1489151284

About this Class

Business analytics and data science have become important skills across all industries. Knowing both how to perform analytics, as well as, sense checking analyses and understanding concepts is key in making decisions today.

Python has become the lingua franca of data science and is, therefore, the topic of this class. This class assumes Python knowledge if you'd prefer a high-level introduction without programming application to data science I have another class: The No-Code Data Science Master Class.

Programming can be intimidating, however, Python excels due to its readability and being freely available for all platforms including Linux, Mac and Windows. This class will assume some prior knowledge of Python syntax, but to establish a common learning environment some of the basics will be covered. We will cover the full data science workflow including:

• Loading data from files (e.g. Excel tables) and databases (e.g. SQL servers)
• Data cleaning
• Exploratory data analysis
• Machine learning
• Model validation and churn analysis
• Data visualization and report generation

In this class, we will use freely and openly available Python libraries including: Jupyter, NumPy, SciPy, Pandas, MatPlotLib, Seaborn, and Scikit-Learn and you will also learn how to quickly learn new libraries.

Hands-on Class Project

Create a PDF report of a data analysis in Python with at least one visualization.

Code available on here on Skillshare or on Github with interactive links.

Assignment: Use a dataset you have from a project you are working on. Prepare and analyze this data and create at least one meaningful visualization. The data could be sales, expenses, or your FitBit data! Make sure to anonymize the data in case anything is sensitive information! (If you don’t have any data, I have some data listed and even a data set you can use below!)

Deliverable: Create a Jupyter Notebook describing your analysis process that contains at least one visualization that tells a compelling story.

Details: The project will consist of loading data and performing the exploratory data analysis and visualizations outlined in the class. The project is relatively straight-forward, as the class will follow an applied structure that can be revisited for parts of the project analysis.

Students are encouraged to use their own datasets for the analysis, as these yield the most benefit in learning. Alternatively, it is also possible to search for data sets in the following places: Check course page for more detail....

Covered Topics

• Technology
• Data Visualization
• Python
• Science
• Scipy
• Data Analysis
• Data Science

About Author

Jesper Dramsch, PhD

a world-class scientist for machine learning working between physical data, data science and AI.

In my classes, you'll learn state-of-the-art methods to work with and gain insights from data. This takes the form of exploring data and gaining insights with modelling and visualizations. Whether you're a beginner, intermediate, or expert, these classes will deepen your understanding of data science.

I am trained as a geophysicist and shifted into data science and machine learning research and Python programming during work towards a PhD. During that time, I created educational notebooks on the machine learning contest website Kaggle (part of Alphabet/Google) and reached rank 81 worldwide. My top notebook has been viewed over 70,000 times at this point. Additionally, I have taught Python, machine learning and data science across the world in companies including Shell, the UK government, universities and several mid-sized companies. As a little pick-me-up in 2020, I have finished the IBM Data Science certification in under 48h. Now I am part of the coordinated organisation ECMWF.

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