Don’t let them slow you down

I have been using Python since I took my first step into data science. Although I excessively focus on the third party libraries such as Pandas and Scikit-learn, it is of vital importance to learn base Python as well.

Python is a great language for various reasons. It is easy to learn and understand. You can read Python code as you are reading plain English. Python also leads to quick and easy development processes which I think is a motivation booster.

On the other hand, Python has some features that we need to pay extra attention to. Otherwise, we might…

Hands-on SQL tutorial

I have come across a great dataset on Kaggle. It was shared under creative commons license so we can use and share it publicly. I thought it would be exciting to learn about the history of olympics.

There are many alternative tools for performing an analysis on this dataset. I have chosen SQL for no particular reason. In this article, we will be learning about the Olympics while practicing SQL.

Note: SQL is used by many relational database management systems such as MySQL, SQL Server, PostgreSQL, and so on. Although they mostly adopt the same SQL syntax, there might be…

We all learn from mistakes

My passion for data science started about 3 years ago. After a long and exciting learning journey, I was able to land my first job as a data scientist. I have written several articles to share my learning experience and the process that leads to me getting that dream job.

In this article, I will write about my experience after starting my first job as a data scientist. It has been thrilling and fun so far and I’m glad I made a career change into data science.

However, I have also faced some challenges and made some mistakes. It is…

and how to overcome them

Approximately 3 years ago, I watched a video on YouTube that lit a spark in my mind. That spark has grown and enlightened my path to become a data scientist.

It was a big challenge for me to make a career change after the age of 30. I had to spare time for learning data science in the evenings or on the weekends. Nevertheless, it was fun and I’m glad I made a career change.

It took me about 2 years to land my first job as a data scientist. During this journey, there were times I questioned my decision…

Practical data visualization guide.

Data visualization is an integral part of data science. It helps us explore a dataset and the relationships between variables to create proper data visualizations. They are also highly efficient tools to deliver results and findings.

There are several different kinds of data visualizations. One of the most commonly used ones is line plot which is used for analyzing the relationships between two continuous variables.

In this article, we will go over 7 examples to explain in detail how to create line plots with the Seaborn library of Python.

The main use case for line plots is time series analysis…

…That make the groupby function even more useful.

The groupby is one of the most frequently used Pandas functions for data analysis. It first divides the data points (i.e. rows in a data frame) into groups based on the distinct values in a column. Then, it calculates aggregated values for each group.

Consider we have a dataset that contains brands and prices of cars. In order to calculate the average price for each branch, we group the rows based on the brand column and then apply the mean function on the price column.

Pandas provides several aggregate functions that can be used along with the groupby function such…

It is not always about numbers.

Python is a great language. It is relatively easy to learn and has an intuitive syntax. The rich selection of libraries also contribute to the popularity and success of Python.

However, it is not just about the third party libraries. Base Python also provides numerous methods and functions to expedite and ease the typical tasks in data science.

In this article, we will go over 15 built-in string methods in Python. You might already be familiar with some of them but we will also see some of the rare ones.

The methods are quite self-explanatory so I will focus more…

We sometimes need to go the other way around.

String interpolation is a process of modifying strings by using placeholders. The resulting strings include the values of the placeholders. In Python, the format method and f-strings are two commonly used string interpolation methods.

Before jumping on the parse library, it will be helpful to do a few examples to demonstrate what a string interpolation is.

folder = "notebook"
subfolder = "parse"
file_path = f"documents/{folder}/{subfolder}"print(file_path)

The placeholders are represented with curly braces and the values of them are included in the output.

Here is another example.

name = "John"
age = "23"
print(f"{name} is {age} years old.") …

Machine learning, Python, and SQL

The popularity of data science attracts a lot of people from a wide range of professions to make a career change with the goal of becoming a data scientist.

Despite the high demand for data scientists, it is a highly challenging task to find your first job. Unless you have a solid prior job experience, interviews are where you can show you skills and impress your potential employer.

Data science is an interdisciplinary field which covers a broad range of topics and concepts. Thus, the number of questions that you might be asked at an interview is very high.


A smart way for simplifying tasks

SQL is a must-have skill for data scientists. Since a lot of companies store their data in relational databases, we need to use SQL to access, query, and analyze that data.

SQL is capable of performing more advanced tasks than basic queries. It provides several functions that make SQL a highly efficient data analysis tool.

The following might be the most simple SQL query. It selects all rows and columns in a table.

SELECT * FROM table_name;

As the complexity of a task increases, the syntax of the query gets more complicated. Besides, you may have to query multiple tables…

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Writing about Data Science, AI, ML, DL, Python, SQL, Stats, Math | |

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