• I do like the story telling aspect of the data analysis process. In the making of a good data story, Exploratory Data Analysis with solid data visualizatons is very important. While conducting the EDA, aspects of data integrity and data quality must be kept in check in the process of data cleansing.
• I believe a good data scientist should know his data's story in order to be able to build strong and resilient Machine Learning applications with high accuracy and low error rate.
• I think productionalizing Machine Learning algorithms is the most important aspect of the Data Science process since even the best performing ML Models in a vacuum can't really offer business value without being deployed.
• In order to keep deployed Machine Learning applications resillient and strong auiditing and monitoring processes must be taken very seriously since Data Drift can rot deployed ML Models as time goes. Proper monitoring and maintenance of deployed ML Models are only possible with Continuous Integration and Continuous Delivery (CI/CD).

• become a better Linux user since it is the OS of choice for all Cloud Platforms.
• improve on common Data Science languages such as Python, R and SQL also get proficient at pipeline tools such as Apache Beam and Apache Spark.
• forge ahead my Cloud Computing skills since I believe the best way to do Data Science is through cloud native applications. Currently I am making progress on Google Cloud Platform.
• develop and hone my Machine Learning Operations (MLOps) abilities on cloud platforms by utilizing tools such as Docker Containers, Kubernetes(KubeFlow), GitHub Actions, BigQuery ML and Vertex AI.

Since I consider myself as a data person I do enjoy learning new tools and tackling new challenges on Data Science related subjects such programming, statistics, mathematics or another Data/ML framework such as MLFlow. Though learning process could be hard, time taking and painful at times😓 I believe it's also quite rewarding in an intellectual sense.

You can find my personal projects here on Github or on my Kaggle.

If you wish to contact me my e-mail is [email protected]. I am always open to have a chitchat about Data Science 😊.