Overview
Statistics you need at the Project : Descriptive and Inferential statistics, Hypothesis testing, Regression analysis
Syllabus
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- Introduction to Statistics
-- Importance of statistics in AI and business analysis
-- Basic terminology and concepts
- Descriptive Statistics
-- Measures of central tendency (mean, median, mode)
-- Measures of variability (range, variance, standard deviation)
-- Data visualization techniques (histograms, box plots, scatter plots)
- Probability Theory
-- Basic probability concepts
-- Conditional probability and Bayes' theorem
-- Probability distributions (normal, binomial, Poisson)
- Inferential Statistics
-- Sampling methods and sampling distributions
-- Hypothesis testing
-- Confidence intervals
- Regression Analysis
-- Simple linear regression
-- Multiple linear regression
-- Model evaluation metrics (R-squared, adjusted R-squared)
- Time Series Analysis
-- Understanding time series data
-- Trend and seasonality
-- Autoregressive and moving average models
- Statistical Software and Tools
-- Introduction to statistical programming languages (Python/R)
-- Using libraries for data manipulation and analysis (pandas, NumPy, SciPy)
-- Data visualization libraries (Matplotlib, Seaborn)
- Application of Statistics in Business
-- Market basket analysis
-- Customer segmentation
-- Sales forecasting
- Ethics and Best Practices in Data Analysis
-- Data privacy and ethical considerations
-- Ensuring bias-free analysis
-- Communicating statistical findings effectively
Taught by
Tags
On-Demand
