This section establishes the foundation for working with Python as a data scientist. It covers essential programming constructs, data structures, and libraries, as well as techniques for importing, cleaning, and transforming raw datasets. Learners develop skills to organize data efficiently, ensuring a reliable basis for subsequent analysis.

PYTHON ESSENTIALS FOR ANALYSIS

Learning Objectives

Install/configure Python; identify main libraries (NumPy, Pandas)
Manipulate Python data structures (lists, dictionaries) and Pandas DataFrames
Create basic visualizations using common plotting libraries

Indicative Content

Core Python Syntax
- Variables, loops, conditionals for data exploration
NumPy & Pandas
- Array operations, indexing, slicing, merging DataFrames
Simple Plots
- Histograms, line plots, bar charts for initial data understanding

2) DATA MANAGEMENT & CLEANING

Learning Objectives

Import/export data (CSV, Excel, SQL) and handle missing/duplicated/outlier values
Rename columns, recode variables, derive new ones, merge/append datasets
Perform group-wise aggregations for summarized outputs

Indicative Content

Reading Files
- Techniques for CSV, Excel, and database imports
Data Quality Checks
- Inspecting structure (.info(), .describe(), null counts)
Cleaning & Transformation
- Resolving duplicates, imputing missing data, managing outliers
Merging & Aggregation
- Joining DataFrames, concatenation, group-based summaries

TOOLS & METHODOLOGIES (BASIC PYTHON & DATA HANDLING)

Python Libraries
- Data Manipulation: NumPy, Pandas for fundamental array and table operations
- Visualization: Matplotlib, Seaborn for basic plotting (histograms, bar charts, etc.)
Data Ingestion
- Techniques for reading CSV, Excel, SQL databases
- Ensuring data integrity via checks (.info(), .describe())
Data Cleaning
- Identifying/removing duplicates, handling null values, outlier detection
- Reshaping and merging datasets for analysis

‹ FUNDAMENTALS OF TIME SERIES

PREDICTIVE MODELING FOUNDATIONS ›