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2.3 Lab: Introduction to Python
2.3.1 Getting Started
Covers essential setup structures for starting Python, such as the Jupyter environment and package installation methods. You can understand the default interpreter path that will serve as the basecamp for analysis.
2.3.2 Basic Commands
Quickly scans through very basic essential commands at the shell level, such as console output, data assignment, and returning length. You can examine basic Python data type structures like strings or lists and their compatibility.
2.3.3 Introduction to Numerical Python
How to use the NumPy package, the core foundation that enables powerful and fast computation of multi-dimensional data arrays (Array/Matrix). Takes time to get accustomed to specifying random seeds and generating random numbers.
2.3.4 Graphics
Brings in Matplotlib capabilities to visualize complex data trends like scatter plots and contour plots in the form of charts. Learns the technique of intuitively capturing information structures, correlations, and distribution patterns through graphs.
2.3.5 Sequences and Slice Notation
Deals with indexing techniques that directly access elements inside Python’s matrix objects or separate only a specific sequence interval. Aims for grammatical mastery in dividing and combining huge chunks of data into necessary sequences.
2.3.6 Indexing Data
A technique to not only manually specify the index of the desired range, but also filter by combining the results of logical truth values (Boolean). Practices specifying filter conditions to weed out only the information with specific conditions from a massive dataframe.
2.3.7 Loading Data
Learns how to actually load external data into a DataFrame in the Python environment using Pandas’ read_csv syntax.
An elementary process of importing and viewing initial data, checking for and handling non-existent Null values, etc.
2.3.8 For Loops
Learns block processing techniques, which are basic control statements that must be used when writing repetitive analysis pipelines or scripts. Approaches it with comparative grammar in preparation for using list comprehensions and vector operations.
2.3.9 Additional Graphical and Numerical Summaries
Learns everything from numeric summaries like describe to capture all data at a glance, to additional graphical techniques like histograms and box plots.
By understanding the location and dispersion of the entire dataset like the back of your hand, it adds momentum to future feature engineering execution.
Sub-Chapters
| < 2.2.3.1 K Nearest Neighbors | 2.3.1 Getting Started > |