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

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  • LanguageEnglish
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  • Last checkedJun. 22nd '22
  • Date uploadedJun. 20th '22
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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.

Files:

[FreeCoursesOnline.Me] SkillShare - Data Science and Business Analytics with Python 0. Websites you may like
  • 1. Get Free Premium Accounts Daily On Our Discord Server!.txt (1.3 KB)
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  • 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 (100.0 MB)
  • 13. Dealing with Missing Data.srt (8.0 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 (30.0 MB)
  • 2. Class Project.srt (2.3 KB)
  • 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 (73.0 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 (9.0 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. 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 (0.3 KB)
  • 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 (36.0 MB)
  • 36. Conclusion and Congratulations!.srt (2.6 KB)
  • 4. Tool Overview.mp4 (70.3 MB)
  • 4. Tool Overview.srt (6.0 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 (0.5 KB)
  • 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)

Code:

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