Online Course

FSDS_5_Applied Statistics and Time Series Analysis

This Module Covers Applied Statistics required for Data Science and Time Series Analysis in detail

Statistics Anova Arima Sarima Df test Hypothesis testing Chi-square test Forecasting
Included in Course

  • video_library Rich Learning Content

  • assessment Quizzes & Projects

  • verified_user Taught by Industry Pros

  • forum Student Support Community

  • grade Certificate of Completion

About the Course

Section 1: Applied Statistics

  • Descriptive Statistics and Inferential Statistics Introduction
  • Analyzing the categorical & continuous Data
  • Hypothesis Testing and Business cases
  • One-Sample T-Test – Business examples
  • Two Independent Samples Tests – example and use case
  • Paired T-test
  • Python Implementation of t-tests with scipy
  • ANOVA – example and use case
  • Proportional Test
  • Chi Square Test – example and use case
  • Python implementation of tests with scipy and statsmodels

Section 2: Time Series Analysis

  • What is Time-Series data?
  • What is Time-Series Forecasting and Time-Series Analysis?
  • Predictive vs Descriptive Analytics
  • Factors to consider while forecasting.
  • Different kinds of Forecasting
    • Short-term Forecasts
    • Medium-term Forecasts
    • Long-term Forecasts
  • Forecast Horizons and Data Collection Methods
  • Types of Forecasting
    • Qualitative Forecasting
    • Quantitative Forecasting
  • Visualization of Time-Series Data
    • Time Plots
    • Stacked Line Charts
    • Seasonal Plots
    • Box Plots
    • Lag Plots
    • Autocorrelation in Data
  • Decomposing Time-Series Data
  • Time-Series Validation and Cross-Validation Strategies
  • Performance Metrics in Time-Series Analysis.
  • Algorithms
    • Simple Moving Averages
    • Simple Exponential Smoothing
    • Holts Method (Trend)
    • Holts-Winter Method (Trend and Seasonality)
    • ARIMA & SARIMA Models

Course Leads

  • Prashant Sahu
    Prashant Sahu

    Staff