Note

Data Preprocessing: Removing Duplicates, Transformation of Data using function or mapping, replacing values, Handling Missing Data.

Analytics Types: Predictive, Descriptive and Prescriptive.

Time-series analysis

<aside> 💡 Math specially statistics, algebra etc is also very important for AI which can be studied from here.

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PyData Stack

The term PyData is not official but rather a colloquial title that refers to a collection of commonly used Python packages and tools within the data science community.

There are a lot of packages the main ones are:

• numpy
• pandas
• matplotlib
• jupyter notebook

There are some other packages often used with the packages discussed, but maybe not as often:

• sklearn
• statsmodels
• sympy
• nltk

Upgrade the Analysis Game

SQL and Pandas are both powerful tools for data scientists to work with data.