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- Data visualization
- Databases (SQL & NoSQL)
- Web languages and web semantics to extract data

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- Data PreProcessing
- Data Imputation
- Data Cleaning
- Data Transformation
- Data Visualization
- Data Analysis
- Data Engineering - Big Data

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- Machine Learning
- Data Mining
- Data Visualization
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- Time Series, Survival Analysis
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**Code for: **R and Linear Regression

**Solution:**

```
```
```**1.**
```{r}
linear_regression<-function(x,y){
# number of observations/points
n = length(x)
# mean of x and y vector
m_x = mean(x)
m_y = mean(y)
# calculating cross-deviation and deviation about x
SS_xy = sum(y*x) - n*m_y*m_x
SS_xx = sum(x*x) - n*m_x*m_x
# calculating regression coefficients
b_1 = SS_xy / SS_xx
b_0 = m_y - b_1*m_x
return (c(b_0,b_1))
}
```
```{r}
b=linear_regression(x,y)
b
```
**2.**
```{r}
reg_function <- function(b0,b1,x){
y = b0 + b1*x
pred = c()
pred=append(pred,y)
return(pred)
}
```
```{r}
pred = reg_function(20.65919,3.195276,x)
pred
```

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