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Demonstrating my Data Analyst Skills

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Contents

Introduction Python data analysis in healthcare(Viewed)

Course Project Objective The goal of this project is to apply everything we have learned in this course to build an end-to-end data pipeline.

Problem statement Develop a dashboard with two tiles by:

Selecting a dataset of interest (see Datasets) Creating a pipeline for processing this dataset and putting it to a datalake Creating a pipeline for moving the data from the lake to a data warehouse Transforming the data in the data warehouse: prepare it for the dashboard Building a dashboard to visualize the data Data Pipeline The pipeline could be stream or batch: this is the first thing you'll need to decide

Stream: If you want to consume data in real-time and put them to data lake Batch: If you want to run things periodically (e.g. hourly/daily) Technologies You don't have to limit yourself to technologies covered in the course. You can use alternatives as well:

Cloud: AWS, GCP, Azure, ... Infrastructure as code (IaC): Terraform, Pulumi, Cloud Formation, ... Workflow orchestration: Airflow, Prefect, Luigi, ... Data Warehouse: BigQuery, Snowflake, Redshift, ... Batch processing: Spark, Flink, AWS Batch, ... Stream processing: Kafka, Pulsar, Kinesis, ... If you use a tool that wasn't covered in the course, be sure to explain what that tool does.

If you're not certain about some tools, ask in Slack.

Dashboard You can use any of the tools shown in the course (Data Studio or Metabase) or any other BI tool of your choice to build a dashboard. If you do use another tool, please specify and make sure that the dashboard is somehow accessible to your peers.

Your dashboard should contain at least two tiles, we suggest you include:

1 graph that shows the distribution of some categorical data 1 graph that shows the distribution of the data across a temporal line Ensure that your graph is easy to understand by adding references and titles.

Data Analysis for Healthcare

Data analysis in healthcare(Viewed) Types of data in healthcare(In progress) Sources of healthcare data Databases and storage for healthcare data Data analysis tools and technologies for healthcare data Healthcare data analytics use cases

1. Visit Data from an Outpatient Clinic Using Python

Explore patient visit demo dataset Save and load the patient visit demo dataset (different format) Demography analysis of the patient visit demo dataset: Part 1 Demography analysis of the patient visit demo dataset: Part 2 Trend analysis of patient visit demo dataset: Part 1 Trend analysis of the patient visit demo dataset: Part 2 Wait time analysis of the patient visit demo dataset: Part 1 Wait time analysis of the patient visit demo dataset: Part 2 Correlation analysis of the patient visit demo dataset Cost and insurance analysis of the patient visit demo dataset: Part 1 Cost and insurance analysis of the patient visit demo dataset: Part 2

2. Medication Demand Forecasting Using Python

Explore the pharmacy sales demo dataset Medication class analysis: Part 1 Medication class analysis: Part 2 Medication demand forecast using Prophet: Part 1 Medication demand forecast using Prophet: Part 2

3. Patient Experience Analytics Using Python

Explore laboratory patient experience demo dataset with Plotly Sentiment analysis of patient experience reviews using TextBlob

4. Public Health Facilities GeoSpatial Analysis Using Python

Explore the public health facilities geolocation demo dataset using GeoPandas: Part 1 Explore the public health facilities geolocation demo dataset using GeoPandas: Part 2 Interactive map for public health facilities geolocation demo dataset using folium: Part 1 Interactive map for public health facilities geolocation demo dataset using folium: Part 2 Interactive map for public health facilities geolocation demo dataset using folium: Part 3

5. Healthcare Data Wrangling Using Python

Practical data manipulation and wrangling using pandas: Part 1 Practical data manipulation and wrangling using pandas: Part 2