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Day 1 of learning Data Science as a beginner. Topic: data science life cycle and reading a json file data dump. What is data science life cycle? The data science lifecycle is the structured process of extracting useful actionable insights from raw data (which we refer to as data dump). Data science life cycle has the following steps: 1. Problem Solving: understand the problem you want to solve. 2. Data Collection: gathering relevant data from multiple sources is a crucial step in data science we can collect data using APIs, web scraping or from any third party datasets. 3. Data Cleaning (Data Preprocessing): here we prepare the raw data (data dump) which we collected in step 2. 4. Data Exploration: here we understand and analyse data to find patterns and relationships. 5. Model Building: here we create and train machine learning models and use algorithms to predict outcome or classify data. 6. Model Evaluation: here we measure how our model is performing and its accuracy. 7. Deployment: integrating our model into production system. 8. Communicating and Reporting: now that we have deployed our model it is important to communicate and report it's analysis and results with relevant people. 9. Maintenance & Iteration: keeping our model upto date and accurate is crucial for better results. As a part of my data science learning journey I decided to start with trying to read a data dump (obviously a dummy one) from a .json file using pure python my goal is to understand why we need so many libraries to analyse and clean the data why can't we do it in just pure python script? the obvious answer can be to save time however I feel like I first need to feel the problem in order to understand its solution better. So first I dumped my raw data into a data.json file and then I used json's load method in a function to read my data dump from data.json file. Then I used f string and for loop to analyse each line and print the data in a more readable format. Here's my code and its result.