Data Analytics

Gathering data from various sources, including databases, APIs and web scraping. Ensuring data quality and integrity by cleaning and preprocessing raw data. Employing techniques to handle missing values, outliers and inconsistencies.

Conducting initial investigations to discover patterns, anomalies and relationships. Using statistical techniques and visualisation tools like histograms, scatter plots and box plots. Summarising main characteristics of the data to guide further analysis.

Applying statistical tests and models to analyse data and draw meaningful conclusions. Utilising regression, clustering and classification methods to understand data trends. Building predictive models to forecast outcomes and support decision-making.

Developing and implementing machine learning algorithms to automate data analysis. Training models on large datasets to predict and classify future data points. Evaluating model performance using metrics like accuracy, precision and recall.

Creating visual representations of data to communicate insights effectively. Using tools like Tableau, Power BI, and Matplotlib to develop dashboards and reports. Ensuring visualisations are clear, accurate and tailored to the audience's needs.

Translating complex data findings into actionable insights for stakeholders. Collaborating with cross-functional teams to align data strategies with business goals. Presenting results through written reports, presentations and interactive visualisations