Data science has become essential for businesses to analyze large amounts of data. While machine learning seems promising, there are no shortcuts - data exploration is needed to improve model accuracy. The key steps of data exploration are identifying variables, univariate and bivariate analysis, and treating missing values and outliers to clean the data before building predictive models. Understanding the data generation process is important for leaders to evaluate the quality of analytics.

2. Data science has become an essential business tool. With access to
incredible amounts of data—thanks to advanced computing and the
“Internet of things”—companies are now able to measure every aspect
of their operations in granular detail.

3. Introduction
There are no shortcuts for data exploration. If you are in a state of
mind, that machine learning can sail you away from every data storm,
trust me, it won’t. After some point of time, you’ll realize that you are
struggling at improving model’s accuracy. In such situation, data
exploration techniques will come to your rescue.

4. Steps of Data Exploration and
Preparation
Below are the steps involved to understand, clean and prepare your
data for building your predictive model:-
Variable Identification
Univariate Analysis
Bi-variate Analysis
Missing values treatment
Outlier treatment
Variable transformation
Variable creation

5. Variable Identification
First, identify Predictor (Input) and Target (output) variables. Next,
identify the data type and category of the variables.

6. Univariate Analysis
At this stage, we explore variables one by one. Method to perform uni-
variate analysis will depend on whether the variable type is categorical
or continuous.

7. Bi-variate Analysis
Bi-variate Analysis finds out the relationship between two variables.
Here, we look for association and disassociation between variables at a
pre-defined significance level.

8. Missing Value Treatment
Missing data in the training data set can reduce the power / fit of a
model or can lead to a biased model because we have not analysed the
behavior and relationship with other variables correctly. It can lead to
wrong prediction or classification.

9. We looked at the importance of treatment of missing values in a
dataset. Now, let’s identify the reasons for occurrence of these missing
values. They may occur at two stages:
Data Extraction
Data Collection

10. Outlier treatment
Outlier is a commonly used terminology by analysts and data scientists
as it needs close attention else it can result in wildly wrong estimations.
Outlier can be of two types: Univariate and Multivariate.

11. Outliers can drastically change the results of the data analysis and
statistical modeling.
It increases the error variance and reduces the power of statistical
tests.
If the outliers are non-randomly distributed, they can decrease
normality.
They can bias or influence estimates that may be of substantive
interest.

12. Working of Data Analysis
A working knowledge of data
science can help leaders turn
analytics into genuine insight. It
can also save them from making
decisions based on faulty
assumptions. “When analytics
goes bad,”

13. How can leaders learn to distinguish
between good and bad analytics?
It all starts with understanding the data-generation process.You cannot
judge the quality of the analytics if you don’t have a very clear idea of
where the data came from.