Python (Data analytics)

Objective: Goal is to learn python for data analytics in 6 weeks

• Basic principles of data analytics 
• Analytics life-cycle 
• Loading and working with data from variety of data sources 
• Python functionalities, tooling, and frameworks for preparing and working with data

( Thursday 2hrs, Saturday 2hrs, Sunday 2hrs)

Included in the package:

✅ Expert-led training from seasoned professionals
✅ Hands-on projects and real-world simulations
✅ Intensive learning environment for rapid skill acquisition
✅ State-of-the-art lab facilities and resources
✅ Capstone Project

What you would learn:

• Introduction to python (Extra classes on the first 2 Thursdays)
• Introduction to Data Analytics
  • What is Data Analytics
  • Types of Analytics 
    • Descriptive, Diagnostic, Predictive, Prescriptive
  • Analytics life-cycle
    • Define objective > Data collection > Data Prep > Analysis and Visualization > Reporting > Refine objective
  • Overview of tooling 
    • Development environment: Jupyter Notebooks, Google Colab
    • Data wrangling: Pandas
    • Mathematical/statistical analysis: Numpy, scipy, statsmodel
    • BigData: PySpark
    • Visualization: matplotlib, seaborn, plotly, etc
    • Reporting: Dash, Streamlit, BI tools (PowerBI, Tableau)
  • Walkthrough of Notebooks environment with Google Colab

   

• Data Processing (Python, Pandas)
  • Understand data terminologies
    • Types of data Qualitative (Nominal vs Ordinal) vs Quantitative (Discrete vs Continuous)
    • Structure of tabular data: rows, columns, cell, index, header, data frame, axis,
    • Cross-sectional data vs Time series data
  • Data collection
    • APIs
    • Web scrapping
  • Loading data and processing
    • Reading flat files: CSV, excel files, parquet
    • Sourcing data from relational databases
  • Data wrangling
    • Exploring data frames
      • data dimension (shape, columns, info, describe, head, tails)
      • selecting columns and rows
      • converting types
    • Creating data frames from scratch
    • Aggregating data (summary statistics)
      • unique, counting, mean, min, max, quartile/percentile
    • Filtering data: subsetting, indexing (.loc, .iloc), slicing

  Data transformation, cleaning and visualization (Python, Pandas, Seaborn)

  • Data Transformation 
    • Sorting, grouping, insertion, deletion
    • Renaming, reindexing, merging dataframes
    • Pivot table
  • Data Cleaning
    • Finding and removing duplicates
    • Finding and replacing null or missing values
    • Finding outliers or extreme values
  • EDA & Data Visualization
    • Matplotlib vs Seaborn
    • Basic plotting: line plots, bar charts, pie charts
    • Visualizing data distribution
      • Histogram, boxplots, density plots
    • Exploring data relationships
      • Scattered plots 
      • Correlation plots
      • Pairplot 

  Advanced analytics (Python, Pandas, Seaborn)

  • Working with time series data 
    • Visualizing
    • Smoothening 
    • Resampling
  • Small vs Big data analytics
  • Beyond descriptive analytics