Basics of Data Analysis Notes.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyN8Hr+5w7gbJo7b5vupLeB7"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["**Data collection and Preprocessing**\n","\n","\n","*Data Collection*\n","* process of gathering information (survey responses, observations, measurements, interviews, etc.)\n","\n","*Data Preprocessing*\n","* process of getting your data ready for analysis or machine learning by cleaning, organizing, and formatting it.\n","* involves tasks like removing errors. dealing with missing information, and making sure he data is in usable form.\n","\n","**Exploratory Data Analysis**\n","\n","**Data Preprocessing Techniques**\n","\n","* *Understand Data*\n","* *Identify Patterns*\n","* *Detect Anomalies*\n","* *Formulate Hypotheses*\n","* *Guide Decision-making*\n","* *Communicate Insights*\n","\n","**Data Visualizaation Tools**\n","* Matplotlib\n","* Seaborn\n","\n","**Types of Charts**\n","* Line Charts - trends\n","* Bar Graph - comparison\n","* Scatter Plots - linear regression"],"metadata":{"id":"NNff5rDFbLfT"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"TqtwztBcbLBK"},"outputs":[],"source":[]}]}