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Data Science Internship Prep
·
Module 2
Data Science Foundations: NumPy and Pandas
1
Vectorized Operations in NumPy
Perform vectorized mathematical operations on NumPy arrays.
2
Pandas Data Selection with `loc`, `iloc`, and Boolean Indexing
Create, select, and manipulate data in Pandas Series and DataFrames using `loc`, `iloc`, and boolean indexing.
3
File I/O with CSV and Excel
Read data from and write data to common file formats like CSV and Excel.
4
Missing Data Handling Techniques
Handle missing data using various strategies (e.g., dropping, filling, interpolation).
5
Initial Data Inspection with Pandas
Perform summary statistics and initial data inspection using `.info()`, `.describe()`, and `.value_counts()`.
6
Combining DataFrames: Merge, Join, and Concatenate
Merge, join, and concatenate multiple DataFrames to combine different data sources.
7
Summarizing Data with `groupby`
Apply grouping and aggregation operations using `groupby` to summarize data.
8
Data Type Conversion and String Cleaning
Convert data types and handle messy or inconsistent string data.
Previous module
SQL for Advanced Data Analysis
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Data Visualization and Exploratory Data Analysis (EDA)