Learn about Python-based data analytics, discover data manipulation and cleaning techniques, explore Jupyter for insights, and gain skills to plot and visualize data effectively.

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In this course, you will get an introduction to Python-based data analytics. You will learn the inner workings of Python to the point where you can confidently find and manipulate data sources and use a Jupyter environment to derive insights from your data.

By the end of this course, you will be able to find and clean data sources, plot your data, and use Python functionalities that show graphs of data.

It’s your all-in-one guide to teaching yourself data analytics in 30 days.

Data Analytics in Python

# Course conclusion
Congratulations on finishing this course! Having completed this course, you now have practical experience on how to:

1. Filter and subset a Pandas Data Frame

2. Group Pandas Data Frames by categorical features, and analyze the resultant groups

3. Handle dates in Pandas, and perform analysis based on extracted `date` features

4. Apply functions to rows or columns to perform more complex computations and analyses

5. Merge or combine multiple DataFrames based on different strategies

6. Plot graphs and histograms to analyze data using Plotly

7. Understand results through domain knowledge and tools like regression lines



You have applied different methods to perform the above points, and have now covered a very good portion of the Data Analytics. Congratulations!


We will wrap up this course with a final note for our learners. 

Wrap Up!

We will wrap up this course with a final note for our learners.

Before We Begin

Comparing Wages With Consumer Price Index Data

Wages and CPI: Reality Check

Working With Major US Storm Data

Property Rights and Economic Development

How Representative Is Your Government?

Does Wealth Influence The Prevalence Of Mental Illness?

Do Birthdays Make Elite Athletes?

Does Literacy Impact The Income of People

Conclusion

Appendix

Wrap Up!

Course conclusion