Data Science Blog

How To Makes Use Of Domain Knowledge In Data Science: Examples From Finance And Health Care

Feb 8, 2023

The domains of finance and health care don’t have much in common except for one thing - the involvement of data scientists and machine learning experts, who are changing the way both these domains work. From helping them collect, organize, and process a massive volume of data and making sense of it to letting them make efficient and faster data-driven decisions, a lot is happening to disrupt both these domains. Let’s consider some examples from both the finance and healthcare sectors to understand how the application of data science is helping them.

How Many And Which Programs Should I Learn For Being A Skilled Data Scientist?

Feb 1, 2023

To become a data scientist, you should have knowledge of a variety of programming languages, which include Python, R, Java, SQL, JavaScript, C/C++, and Scala, to name a few. But why do you need to learn these programming languages? Let’s find out the answers by taking a look at the top programs you should learn to make your career path in data science a smooth-sailing one

Navigating An Economic Downturn: How To Plan A Career Switch To Data Science

Jan 31, 2023

Recently, there has been a massive layoff spree by top companies such as Google, Microsoft, and Meta. As difficult as it gets for those who are going through the layoffs, it is a challenge for those who are planning to switch careers. If you are one of those who is planning to switch to a career in data science, it is important to be strategic during these times.

Comparing The Top Three RDBMS For Data Science: Microsoft SQL, MySQL, And PostgreSQL

Jan 30, 2023

According to the Stackoverflow community survey in 2022, the respondents were asked which database environments they have done extensive development work in over the past year, and which they want to work in over the next year. Even though below answers have a mingle of relational database management systems with the others, in this article, we will compare the top three RDBMS: Microsoft SQL, MySQL, and PostgreSQL.

Ten Things That You Need To Know In Statistics: The Fundamentals of Statistics

Jan 26, 2023

In this post, we’re going to discuss ten essential things that you must understand to excel in statistics. These include concepts, equations, and theorems that will not only greatly help you pursue data science but prove your understanding of statistics as well.

Unlocking The Power Of Soft Skills In Data Science

Jan 25, 2023

Data science is a rapidly growing field that combines statistics, computer science, and domain knowledge to extract insights and predictions from data. While technical skills such as programming and machine learning are important for data scientists, soft skills -personal attributes and interpersonal abilities- are also crucial for success in this field.

The Most Likely Problems In Data Analysis?

Jan 13, 2023

As a growing number of businesses and organizations rush to unlock the value of massive amounts of data to derive high-value, actionable business insights via data analysis, they are also facing certain problems. Here are the most common problems that you’re likely to face when performing data analysis:

$Being Good At Math To Be A Good Data Scientist?$

Being Good At Math To Be A Good Data Scientist?

Jan 6, 2023

Being good at math is an important skill for a data scientist to have, as data science involves the use of mathematical and statistical concepts and techniques to analyze and interpret data. However, the level of math proficiency required can vary depending on the specific role and responsibilities of a data scientist.

Maximizing Your Data Science Career Potential: Professional Development And Advancement

Jan 2, 2023

Development and Advancement” is a comprehensive guide to help data scientists and aspiring data scientists take their careers to the next level. Whether you are just starting out in data science or looking to move up in your current role, this guide will help you achieve your career goals and maximize your potential as a data scientist.