Statistical Techniques for Data Analysts

1. Descriptive Statistics

Measures of Central Tendency:
- Mean: The average value.
- Median: The middle value.
- Mode: The most frequent value.
Measures of Dispersion:
- Standard deviation: Measures the spread of data around the mean.
- Variance: The squared standard deviation.
- Range: The difference between the maximum and minimum values.
- Percentiles and Quartiles: Understanding the data distributions.

Hypothesis Testing:
- T-tests: Comparing means of two groups.
- ANOVA (Analysis of Variance): Comparing means of multiple groups.
- Chi-square tests: Analyzing categorical data.
- Understanding p-values, and statistical significance.
Confidence Intervals:
- Estimating a range of values for a population parameter.
Regression Analysis:
- Linear regression: Modeling the relationship between a dependent variable and one or more independent variables.
- Logistic regression: Modeling the probability of a categorical outcome.
- Understanding correlation.

Time Series Analysis:
- Analyzing data points collected over time to identify trends, seasonality, and other patterns.
- Forecasting.
Cluster Analysis:
- Grouping similar data points together.
Factor Analysis:
- Reducing the dimensionality of data by identifying underlying factors.
Cohort Analysis:
- Analyzing groups of users with shared characteristics over time.
Monte Carlo Simulation:
- Using random sampling to model and analyze complex systems.

Understanding Distributions: Recognizing common distributions like normal, binomial, and Poisson.
Data Visualization: Effectively presenting statistical findings through charts and graphs.
Statistical Software: Proficiency in tools like Python (with libraries like NumPy, Pandas, and SciPy), R, or SQL.
Data Cleaning: Knowing how to handle missing data, outliers, and inconsistencies.
Ethical Considerations: Understanding the potential biases in data and how to avoid misinterpretations.