# Statistics for Data Science

Guys, I have listed down all the statistics topics needed to quick start Data Science and Machine Learning. I am in the process of writing articles on each of these topics with examples. So stay tuned to get latest updates on those topics. You can also bookmark this page and follow the blog to get automatic notification on each article completion.

### 3. Probability that is enough to get you started

• Probability Basics
• Conditional Probability
• Monty Hall Problem
• Bayes’ Theorem
• Probability Distributions
• Why we need the Probability Distribution
• Discrete Distribution – Probability Mass Function (PMF)
• Continuous Distribution – Probability Density function (PDF)
• Representation through Histogram
• Shape of PDF
• Negative Skewed
• Normal Distribution
• Positive Skewed
• Kurtosis
• Some Common Probability Distributions
• Bernoulli
• Geometric
• Binomial
• Poisson
• Exponential

### 4. Central Limit Theorem

• What is Central Limit Theorem
• Expectation and Variance
• Sampling Distribution

### 5. Hypothesis Testing

• Confidence Interval
• Confidence Level
• Null Hypothesis (H_0)
• Alternate Hypothesis (H_1)
• Significance Level
• Critical Region
• P-value
• Q-value
• One tailed test
• Two tailed test
• Errors
• Type I
• Type II

• z test
• t test
• chi-squared
• F test

### 7. Analysis of Variance ANOVA

• Test for One Way ANOVA
• Two Way ANOVA
• Multiple Comparisons

### 9. Time Series Analysis

• What is Time Series Data
• Time Series Equation
• Components of Time Series
• Trend
• Seasonality
• Multiplicative
• Random stationary
• Auto Regressive Method
• Auto correlation Factor (ACF)
• Partial Auto correlation (PACF)
• Stationary Model
• Moving Averages
• Simple Moving Averages (SMA)
• Weighted Moving Averages (WMA)
• Exponential Smoothing
• Adding Trend And Seasonality to Moving Averages Process
• Holt Winters Method
• AR, MA and ARIMA Models

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