A Visual Guide to Data Analytics
for Beginners

Sources of data (primary and secondary)
Data collection methods (surveys, web scraping, APIs)
Data cleaning techniques (handling missing values, outliers, inconsistencies)
Data wrangling and transformation

Importance of data visualization
Basic data visualization tools (Excel, Google Sheets, Tableau, Python libraries like Matplotlib, Seaborn)
Creating various types of charts (bar charts, line charts, pie charts, histograms, scatter plots)
Choosing the right visualization for different types of data

Measures of central tendency (mean, median, mode)
Measures of dispersion (variance, standard deviation, range)
Distribution of data (normal distribution, skewness, kurtosis)
Summary statistics

Basic probability concepts (probability rules, conditional probability)
Hypothesis testing (null hypothesis, alternative hypothesis, p-value, confidence intervals)
T-tests, Z-tests, ANOVA
Chi-square test

Simple linear regression
Multiple linear regression
Model evaluation (R-squared, adjusted R-squared, p-values)
Interpretation of regression coefficients

Time series components (trend, seasonality, cycle, noise)
Forecasting techniques (moving averages, exponential smoothing, ARIMA)
Evaluating forecasting models

Introduction to data mining and machine learning
Supervised learning (classification, regression)
Unsupervised learning (clustering, dimensionality reduction)
Common algorithms (decision trees, random forests, support vector machines, k-means clustering)