Experience Of My First JOB
•Python and SQL
After almost a year at my first job I must say this is the most critical skill a Data Science Aspirant should have.
Without Python I can’t imagine surviving in my current job.
Python is one of the most powerful languages I ever come across. It is everywhere. Building MVP is very fast even for most complex Projects.
SQL is the basis of the RDBMS system. Most Structured Data resides in a Data warehouse which can be extracted by SQL. I used PostgreSQL in my job, which is the most popular and open source RDBMS present till date.
•This is one of the most required skills in the Data Science field.
•While Communicating with stakeholders to note down business needs become a more critical part, on which failure or success of a project is dependent.
• Sometimes the right business understanding can save your hours of Re-Doing Work.
•Good Communication skills leads to better planning of Work and getting help from your colleagues when you are stuck in any issue.
3 Phases of My first JOB
•I have engaged in 3 projects and some additional works, all those works gave me 3 different tests of Data Science Field.
•These 3 Fields are Data Analyst, Data scientist, Data Engineer.
•All Roles are different from each other but at the same time very interesting.
•Let me try to Explain My All experience one by one.
•Procurement Analytics This is my first project where I have to work on SAP data to make an Analytics Report on Power BI.
•It requires understanding the procurement process and Business needs.
•Microsoft Power BI is one of the best BI tools for creating Reports and sharing to business users.
•I have to deal with integration of Data with Power BI and Preprocessing of Data as a prerequisite before creating reports on Power BI.
•Video Analytics with Deep Learning and OpenCV is one of my projects which requires a lot of research and persistence.
•After being involved in this project I got real world Data Science Experience.
•Data Science is 80% Preparing Datasets and 20% building models.
•Always be ready for new scenarios and challenges since data will change as we go ahead. Building a successful Machine Learning Solution requires more understanding of data than having a good grasp on tools.
•Creating a Pipeline for different Data Sources (API).
•Built basic ETL and scheduled using Airflow that ingested every minute and hourly Weather data from API and stored in Postgres to analyze and increase Solar and Wind Power Plant Production.
•Utilized spark in python to distribute data processing on large streaming dataset to improve ingestion and processing speed of the application.
Mathematical Model for Optimization
•Order Management where we have to predict or give a list of items to choose in the bag of list to reduce the cost.
•Pyomo gives a template where we can build dynamic input of data to solve.
•There are n-number of Solvers which can optimize our input data based on some constraints.
•This is one of the critical tools since all the data I got exist in Excel format only.
•A good knowledge of excel could save lots of time.
•Excel has a lot of features similar to Power BI.
•Advanced Excel is a must add to python and SQL.
•Preprocessing of data and initial stage of EDA also can be performed by Excel.
•Python and countless number of Libraries.
•SQL i.e. PostgreSQL
•Deep Learning/ Machine Learning/AI
Data Science is not about tools so never ever attached to any tool.
Always have a learning habit and ready for new challenges. I can assure you will be successful in this field
Thanks & Regards