Understanding your Data:The Essentials of Explanatory Data Analysis

Carol Ajando — Wed, 21 Aug 2024 18:30:19 +0000

Originally developed in the 1970s by John Turkey, Explanatory Data Analysis (EDA), continues to be widely used in data science till date.
Explanatory Data Analysis (EDA), is used by data scientists to get a better look and understanding of data sets by utilizing data visualization methods.
EDA allows data scientists to analyze, investigate and identify main characteristics of data sets.
EDA is used to see what data can reveal beyond the formal modeling or hypothesis testing hence providing a better understanding of data set variables and the relationships between them.

## Why is EDA in data science important?

The main purpose of EDA is to get a better understanding of data before making any assumptions.
EDA facilitates the process of data cleaning where it makes it easy to identify outliers, duplicates and any errors within the data set.
EDA enables data scientists to produce effective, error-free & Valid results that can be used for decision-making.
EDA is used to identify patterns and features within the data set such as categorical variables, mean, mode, standard deviation and confidence intervals.
After completion of EDA and insights drawn the features identified can be used for more sophisticated data analysis or modelling such as machine learning.

## Tpes of EDA
EDA can be categorized into Univariate, Bivariate and multivariate.
The categories can be identified further into graphical and non-graphical
Non-graphical methods are used mostly for statistical deductions.
Graphical methods are used to get a full picture of the data sets.

Univariate: This is the most simple form of data analysis which is used where the data sets consists of one variable/column. This includes; boxplots, histogram.
Bivariate: Is used to decribe data and find patterns that exist within data sets that contain two variables/columns. The most commonly used graphical presentation include; scatter plots, bar plots.
Multivariate: Is used for EDA of data sets that contain multiple variables/columns. This included; scatter plots, heatmaps,Runchart, bubble charts.

Python and R programming languages are the mostly used tools that are used to perform EDA.
Common libraries used in python for EDA: Numpy, Pandas, Matplotlib, seaborn, and Ploty.
There is no single method in performing EDA, the decision varies depending on the data set that you have.

Expert advice on how to build a sucessful career in data science

Carol Ajando — Wed, 07 Aug 2024 19:52:08 +0000

In today's data-driven world,Data science has become one of the fastest rising and most sought-after field.Data science involves extraction,analysis and interpretation of data to develop valuable insight and inform strategic decisions.Diving into the data science space requires a lot of hard work and dedication.
Here are some of the tips and pre-requisites you need to build a sucessful career in data science.

1.Education
Data science involves a lot of calculations therefore Math is key.
A career in data science is built on a strong mathematical foundation that includes; Linear algebra, matrix theory,calculation,statistics and probability.
While many data scientists are self-taught,strong IT background in fields such as Computer Science, Statistics or Engineering may be required but not necessary.
Many people are making use of online resources which are mostly free such as courses offered on google, IBM,freecodecamp.org,W3 schools etc.

2.Skills
A combination of technical,analytical and soft skills are required in the field of data science.
These skills include;
1.Data visualization: Transforming data and findings into understandable and visually appealing formats using tools like Tableau,PowerBi and libraries in python such as (Matplotlib &seaborn)
2.Programming:
This is a non-negotiable skill in data science.One should be proficient in Python and R languages which are esswntial for data manupulation,statistical analysis and machine learning.
3.Machine Learning and AI:
This includes understanding and implementing machine learning such as Scikit-learn,Tensor flow and keras.
4.Data Wrangling:
This is the ability to handle missing values,outliers,merging datasets indo desired formats for analysis.
Some of the soft skills include:Good communication,Business Acumen and curiosity and open to learning.
These are just but a few of the skills required in the field of data science.

3.Job Searching
This is the secind last steps in building a career in data science and probably one of the steps that would take a longer time depending on luck, network and many more.
Networking can significantly improve your job search and imorove your chances to land a job.This includes connecting with professionals and data scientists as yourself.Tools such as Linkedin have proven to be very sucessful as a networking tool to land jobs and connect.
Customize CV and cover letter according to each application and highlighting relevant skills while also using key-words.
Lastly but most importantly create and document your projects.This helps to highlight your skills to any potential emoloyer.Add a link or attach samples in any Job application that you do.

The future is built on data and the demand for data scientists keeps growing each day.The field is ever-evolving so, keep learning to keep-up with emerging technologies to prevent being obsolete in this field.

Data science is an exiting field ,brace yourself for a fullfilling Journey ahead!

Forem: Carol Ajando

Understanding your Data:The Essentials of Explanatory Data Analysis

Expert advice on how to build a sucessful career in data science