Statistics for AI Data Science and Business Analysis - 2025

Overview

Are you interested in pursuing a career as a Marketing Analyst, Business Intelligence Analyst, Data Analyst, or Data Scientist, and are eager to develop the essential quantitative skills required for these roles? Look no further!

Syllabus

• 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

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Taught by

Manifold AI Learning ®

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Subjects

Data Science