Development Programme, Internship - Data Science/Analytics at Standard Bank Group

(Focus: Data collection, analysis, visualization, predictive modeling, and business intelligence)

Data Collection & Cleaning:

Gather, clean, and preprocess structured and unstructured data from various internal and external sources for analysis.

Exploratory Data Analysis:

Perform exploratory data analysis to uncover trends, patterns, and outliers, summarizing findings through descriptive statistics.

Dashboard Development:

Develop interactive dashboards and visualizations using tools like Power BI, Tableau, or Python (Matplotlib/Seaborn) to communicate insights.

Predictive Modeling:

Assist in building and validating predictive models (e.g., regression, classification, clustering) to support business forecasting and decision-making.

Data Quality Assurance:

Implement data quality checks and validation rules, ensuring data integrity and accuracy across databases.

Reporting:

Prepare and present data-driven reports to stakeholders, translating complex analytical findings into actionable business recommendations.

Automation:

Support the automation of data pipelines, reporting processes, and recurring analytical tasks to improve efficiency.

• Gather, clean, and preprocess structured and unstructured data from various internal and external sources for analysis.
• Perform exploratory data analysis to uncover trends, patterns, and outliers, summarizing findings through descriptive statistics.
• Develop interactive dashboards and visualizations using tools like Power BI, Tableau, or Python (Matplotlib/Seaborn) to communicate insights.
• Assist in building and validating predictive models (e.g., regression, classification, clustering) to support business forecasting and decision-making.
• Implement data quality checks and validation rules, ensuring data integrity and accuracy across databases.
• Prepare and present data-driven reports to stakeholders, translating complex analytical findings into actionable business recommendations.
• Support the automation of data pipelines, reporting processes, and recurring analytical tasks to improve efficiency.

• Data collection
• Data cleaning
• Data preprocessing
• Exploratory Data Analysis
• Descriptive statistics
• Dashboard development
• Data visualization
• Power BI
• Tableau
• Python (Matplotlib/Seaborn)
• Predictive modeling
• Regression
• Classification
• Clustering
• Data quality assurance
• Data integrity
• Reporting
• Automation