DataScience Classroomnotes 11/Jan/2022

Data Science

  • This is a domain where we work with data to generate some form of value (Meaningful insights, predictions etc..)
  • We use scientific techniques to extract this value
  • For Data Science Project we need Data
  • Collecting Data:
  • Build Systems for data intake (Webpages/ Surveys)
  • Scraping the internet
  • From different existing databases
  • Organizing Data:
  • Data Cleaning is the process of correcting misspellings, fixing errors, identifying duplicates & parsing out missing values
  • Explore Data:
  • Exploratory Data Analysis is process where you find the trends & patterns in your existing data using Statistical Models. You would be using Visualizations to show the trends/patterns
  • Building Models To predict
  • From past data, we can build models that predict future outcomes better than a Random Chance
  • Deploy or Productionize the Model
  • Data Science Life Cycle
  • Data Science Job Roles
  • Data Engineer
  • Data Analyst
  • Machine Learning Engineer
  • Data Scientist
  • Data Engineer
  • Skills:
    • SQL (Primary)
    • Any Cloud (Google, AWS, Azure)
    • ETL (Extract, Tranform and Load)
    • Python (Secondary)
    • Big Data (HDFS, Spark) (Secondary)
    • Admin Skills of Databases (Good to Have)
  • Data Analyst
  • SQL
  • Statistics
  • BI Tools
    • Tableau
    • Power BI
    • Excel
  • Good to Have:
    • Python & R (Matplotlib, Plotly, ggplot)
  • Machine Learning Engineer:
  • Machine Learning Models
    • Supervised ML Models
    • Un Supervised ML
    • Deep Learning
    • Reinforcement
    • ….
  • Programming Languages: Python (Primary), R
  • Integrating Models with Existing Applications i.e. building APIs around ML Models (REST APIs)
  • SQL

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