PART 1: DATA ANALYTICS FOUNDATION

PART 1: DATA ANALYTICS FOUNDATION

Data Management In Python

Efficient data management is crucial for preparing datasets before any statistical analysis. This section addresses importing and exporting data from diverse sources, examining and cleaning data, subsetting and sorting, merging, appending, and aggregating. A high-level overview of ETL processes is also included to ensure Data Analysts understand the broader context of extracting, transforming, and loading data.

Importing Data from Various Formats

Learning Objectives

Apply methods to load data from diverse formats (CSV, Excel, SAS, STATA, SPSS, database tables), manage missing values upon import, and examine how the 'Extract' stage of ETL retrieves data from sources such as APIs and databases for subsequent transformations.

Indicative Content

  • Python Functions:

    • pd.read_csv() for CSV files.

    • pd.read_excel() for Excel files.

    • pd.read_sas() for SAS files.

    • pd.read_sql() for database tables.

  • Handling Missing Values:

    • Use parameters like na_values in pd.read_csv() to handle missing data during import.

  • Extract: Pulling data from sources like databases, APIs, or flat files using Python libraries (requests, SQLAlchemy).

Exporting Data

Learning Objectives

Demonstrate how to save data frames to multiple external formats and assess various export methods, explaining how the 'Load' phase of ETL delivers processed data to target systems.

Indicative Content

  • Python Functions:

    • .to_csv() for exporting to CSV.

    • .to_excel() for exporting to Excel.

    • .to_sql() for exporting to databases.

  • Load: Delivering processed data to target systems (e.g., data warehouses, BI tools).

Data Exploration

Learning Objectives

Inspect dataset structure and missingness, identify data quality issues (missing values, outliers, inconsistencies), and interpret insights to guide subsequent cleaning and analysis steps.

Indicative Content

  • Python Functions:

    • .info() for dataset structure.

    • .head() and .tail() for viewing rows.

    • .describe() for summary statistics.

    • .isnull().sum() for checking missing values.

Data Cleaning and Derivation

Learning Objectives

Apply renaming, recoding, and derivation techniques to transform raw data into a clean, analysis-ready format, and explain how the 'Transform' step in ETL addresses data standardization and joining.

Indicative Content

  • Python Functions:

    • .rename() for renaming columns.

    • .assign() for deriving new variables.

    • pd.cut() for recoding variables into categories.

  • Transform: Cleaning, standardizing, and joining data to create consistent datasets.

Detection and Imputation of Missing Data

Learning Objectives

Detect missing values, apply exclusion or imputation methods, and evaluate effective handling to avoid bias or errors in analysis.

Indicative Content

  • Python Functions:

    • .isnull() for detecting missing values.

    • .dropna() for excluding missing values.

    • .fillna() for imputing missing values (e.g., mean, median, mode, forward fill).

Subsetting Data

Learning Objectives

Extract rows, columns, and subsets based on conditions to focus on relevant data and apply efficient filtering for targeted analysis and reporting.

Indicative Content

  • Python Functions:

    • .loc[] for label-based subsetting.

    • .iloc[] for position-based subsetting.

    • Boolean conditions for filtering rows.

Sorting Data

Learning Objectives

Sort data by one or more variables in ascending or descending order, and explain how ordering enhances readability and pattern recognition.

Indicative Content

  • Python Functions:

    • .sort_values() for sorting by columns.

Merging and Appending Data

Learning Objectives

Apply joins to merge datasets, append observations for unified datasets, and synthesize data from multiple sources to create comprehensive datasets for analysis.

Indicative Content

  • Python Functions:

    • pd.merge() for joining datasets (e.g., inner, outer, left, right joins).

    • pd.concat() for appending rows or columns.

Aggregating Data

Learning Objectives

Group data by variables to compute summary statistics and analyze how aggregation can simplify complex datasets and reveal key insights.

Indicative Content

  • Python Functions:

    • .groupby() for grouping data.

    • Aggregation methods like .sum(), .mean(), and .median() for summarizing data.

Tools & Methodologies

  • Software:

    • Python: Pandas, SQLAlchemy for databases.

    • ETL Awareness: Exposure to tools like Power Query or simple Python scripts.

  • Methodologies:

    • Validate columns (data types, missingness) before transformations.

    • Document merging logic and handle key collisions carefully.

    • Use grouping and summarising to identify anomalies or outliers.

    • Perform small-scale ETL tasks using Python (e.g., extracting data from APIs, transforming it, and loading it into local databases or BI tools).

    • Understand the broader context of ETL to ensure data readiness.