Section outline

• Select section Day 1: Introduction to Data Analytics & Data Manipulation (7 hours)

  Day 1: Introduction to Data Analytics & Data Manipulation (7 hours)

  Collapse all Expand all

  Session 1: Data Collection and Preparation

  • Importing and Exporting Data
  • Reading data from CSV, Excel, SQL databases, and APIs
  • Mathematical & statistical operations
  • Reshaping and stacking arrays
  • Hands-on Exercise: Solving mathematical problems using NumPy

  Session 2: Data Manipulation and Transformation

  • Filtering and selecting data from DataFrames
  • Sorting and ranking data
  • Grouping data and applying aggregate functions (groupby(), agg())
  • Merging, joining, and concatenating datasets
  • Hands-on Exercise: Checking for missing values, modifying data types, and sorting DataFrames

  Session 3: Data Aggregation & Multi-Indexing

  • Aggregating data using groupby()
  • Multi-indexing for hierarchical data
  • Hands-on Exercise:
    • Combining two datasets using joins
    • Grouping data by categorical variables and computing statistics

  Session 4: Exploratory Data Analysis (EDA)

  • Understanding the structure of datasets
  • Displaying and summarizing data using Pandas (head(), describe(), info())
  • Handling missing data (imputation, dropping rows/columns)
  • Data Cleaning: Removing duplicates, correcting data types
  • Feature Engineering: Creating new features, scaling, encoding categorical variables
  • Hands-on Exercise: Univariate, Bivariate, and Multivariate Data Analysis Case Studies

   

   
• Select section Day 2: Data Visualization & Statistical Analysis (7 hours)

  Day 2: Data Visualization & Statistical Analysis (7 hours)

  Session 5: Data Visualization with Matplotlib & Seaborn

  • Creating basic plots: Line, bar, histogram, scatter, etc.
  • Customizing plots: Titles, labels, legends, colors
  • Creating histograms, box plots, and heatmaps
  • Hands-on Exercise:
    • Creating line and bar plots from datasets
    • Creating a heatmap to visualize correlations

  Session 6: Statistical Analysis & Hypothesis Testing

  • Descriptive statistics: Mean, median, mode, variance, standard deviation
  • Probability distributions: Normal, Binomial, Poisson distributions
  • Inferential statistics: Confidence intervals, p-values
  • Introduction to hypothesis testing
  • Types of hypothesis tests: T-test, Chi-squared test, ANOVA
  • Hands-On Exercise:
    • One-Sample t-Test, Chi-Square Test, ANOVA using SciPy
    • Performing hypothesis testing on a real-world dataset

  Session 7: Optimizing Data Frames using Vectorized Operations

  • Using apply() vs. vectorized operations
  • Performance considerations when working with large datasets
  • Hands-on Exercise: Optimizing code performance using vectorization

   

  Final Project: Real-World Data Analytics Case Study

  • Working with a real-world dataset
  • End-to-End Data Analysis Process: Cleaning, manipulation, visualization, and hypothesis testing
  • Hands-on Exercise:
    • Apply learned concepts to analyze and visualize business data