Applied Python for Data Science (TTPS4876) Uzbekistan Eğitimi

Who Should Attend?

Participants should have a solid foundation in Python and introductory Pandas concepts. This course assumes prior experience with Python syntax, basic data structures, and simple data manipulation in Pandas.

Working in a hands-on, applied learning environment, participants will learn to:

• Advanced Data Ingestion & Preparation: Efficiently import, clean, and export complex datasets using Pandas, preparing data for deeper analysis and reuse.
• Sophisticated Data Selection & Indexing: Confidently navigate and subset data using advanced indexing techniques, Boolean logic, and multi-indexing for hierarchical datasets.
• Data Aggregation & Summarization: Apply groupby() operations and aggregation functions to analyze trends, patterns, and summaries across large datasets.
• Data Transformation & Reshaping: Transform, merge, and reshape datasets to support more effective analysis and streamlined analytical workflows.
• Functional Data Processing: Apply user-defined and third-party functions to Pandas objects to extend analytical capabilities and customize data processing.
• Advanced Data Visualization: Create clear, informative, and visually compelling data visualizations using advanced Matplotlib features and Seaborn enhancements.
• NumPy for Analytical Efficiency: Utilize NumPy arrays and operations to manipulate large numerical datasets and improve analytical performance.
• Applied Scientific Computing with SciPy: Gain practical exposure to key SciPy subpackages to support statistical analysis, optimization, and scientific workflows.

1. Pandas input and output

   Reading data into Pandas dataframes and exporting to various formats.

   • General input considerations
   • Reading CSV Files
   • Data cleaning
   • Reading other data formats
   • Exporting data
2. Pandas filtering and sorting

   Selecting subsets of dataframes for focused analysis.

   • Indexing rows and columns
   • Multi-indexing
   • Selection by conditions
   • Sorting data
3. Pandas grouping and aggregation

   Consolidating data and providing sums and other aggregate values.

   • Using groupby()
   • Aggregate functions
   • Using data summaries
   • Alternate approaches
4. Pandas Data Transformation

   Manipulating datasets for simpler analysis.

   • Applying functions to data
   • Renaming columns and indexes
   • Inserting and removing data
   • Combining and merging dataframes
   • Reshaping datasets
5. Advanced Matplotlib

   Going beyond the basics with Matplotlib.

   • Components of a figure
   • Multiple plots
   • Complex plots
   • Matplotlib options and settings
   • Customing styles (and everything else)
6. Seaborn

   Learning how Seaborn supplements and improves on Matplotlib.

   • What does Seaborn provide?
   • Using themes
   • Advanced plot types
   • Fine-tuning the details
7. Using NumPy

   Loading large datasets into NumPy arrays for further analysis.

   • NumPy basics
   • Creating arrays
   • Indexing and slicing
   • Builtin functions()
   • Reading and writing data
8. Useful SciPy subpackages

   A look at some of the 20-odd SciPy subpackages.

   • What is SciPy?
   • scipy.stats
   • scipy.interpolate
   • scipy.optimize