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In this article, we are going to analyze an E-commerce dataset that contains transactions occurring over two years (2010 and 2011). The dataset was picked from Kaggle in excel format and is related to a UK-based company that operates on an E-commerce market.

Using Python 3 and Jupyter Notebook, we will perform some tasks to prepare our dataset so that we can perform exploration and analysis of the sales data of the fictitious company at a business level. The task to prepare the dataset will be:


Analysing web traffic from e-commerce

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Conversion rate optimization is a process to understand what drives, stops, and persuades users to a website. The objective is to give users the best user experience possible and to increase website sales or minimize acquisition costs.

The main focus of this blog is to analyse traffic sources and marketing channels that lead to conversion rates. To do that we’ll use MySQL Workbench.

Let’s see a list of common digital marketing channels:

  • Organic traffic includes traffic that comes from search engines. In this field, examples are Google, Bing, Yahoo etc. …


Understanding Business requirements by using SQL Inner Join, Venn Diagram and Tableau

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This blog is related to part 3 of 4. In the previous article, we’ve focused on a basic query involving a single table. We know that a relational database consists of multiple related tables linked together using common columns and the process behind it is called Normalization.

As we discuss in Understanding Relational Database article part 1, the Normalization process makes data storage more efficient and avoids problems such as data redundancy.

However, to answer business questions, we need to recombine the data from the tables. This means that we must write JOIN queries.

The general syntax of a Join…


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This blog is related to part 2 of 4 (see part 1 — Understanding Relational Database ). We will focus on querying and reading data from an existing database, as part of my learning path in understanding SQL. We will be using the MySQL Workbench server and a sample bank database which will be available on my Github repository.

The main focus will be to formulate queries using SELECT, FROM, WHERE, GROUP BY, HAVING and ORDER BY clauses to retrieve data from a single table using MySQL connected to DBeaver.

Introducing the SELECT statement

The command most used in SQL statement is the SELECT


Understanding concepts of database Relational Model.

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In this article part 1 of 4, we will take steps through a process of understanding the foundations of a Relational Database Model. Part 2 is dedicated to query a database using MySQL as part of my learning path in understanding SQL.

In school, we often work using tools such EXCEL as the main source of data (spreadsheet downloaded from government data portals or academic sources) and SPSS or STATA for data analysis. Over the years I’ve been self-learning data science and working with data stored in a single spreadsheet. However, from the perspective of business analytics, we get into…


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Hi everyone, my name is Kueila Cristina and I am going to make a quick intro about myself and my career path so far and my growing interest in data science.

Born in Cape Verde in 1992, where I finished my 12th grade on Science and Technology area in Sal island, then I wanted to try something new and gain new perspectives and opportunities, so I moved to Portugal in 2011 to get my degree. In this journey, I have always brought homesickness, carried by the sense of responsibility and learning that the new life would bring me, the same fundamental aspects that have made me grow as a person.

I chose to graduate in Business Science because it combines two areas that I always liked: math and business since I…


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Hy everyone, this is my first starting point in the field of Data Science. In this article, I am going to share my experience on a simple data cleaning on Python.

Introduction

Whenever we start doing analysis on Python, usually the first step after importing the necessary packages is to load the data into a Pandas DataFrame using for example read_csv method, read_excel, read_table or read_sql depending on the extension of the file. However, in some cases, if we want to work with a small amount of data, it is also helpful to know how DataFrame works.

For the purpose of this article, I am using Jupyter Notebook as Integrated Development Environment (IDE) and I am going to use sales data to perform data cleaning, but before this, I am quickly going…

Kueila Ramos

Tracing my own path to become a Data Scientist in the business field

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