Data Science with Python: Best Courses for Aspiring Data Scientists
In the ever-evolving world we are living in, you always have to stay updated, whether it’s your work or learning a new skill. Data science is getting its much-deserved popularity now more than ever. And the recent developments clearly suggest that the surge in popularity of this field is here to stay.
When we speak about data science, the first thing that comes to mind is Python. Even though Java training is always beneficial, Python is a popular language used in data science. However, since Python is more of a general-purpose language, it is used in other sectors too.
If you are an aspirant looking to pursue a career in data science with a Python course, you are at the right place. In this blog, we will take a look into the possibilities and prospects of learning Data Science with Python.
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Concepts to know before pursuing Data Science with Python
If we are to talk about many data science job positions that are offered by big companies, both R and Pytho belong to the most commonly asked skills. Before we delve into the ABCs of the courses. Let us take a look at some of the concepts that you must know before you pursue your Data Science journey:
- Strings
Strings generally carry values that are alphanumeric and are generally enclosed in double and single quoted marks in Python. There are various methods you can utilize if you wish to manipulate strings. For instance, if you wish to convert a string to lowercase from uppercase, you can simply utilize Python’s .lower() procedure.
Often, you work with the strings in Data science to develop or manipulate any textual data in your dataset.
- Integers and Floating-point numbers in Python
One of the most fundamental concepts in data science with Python is numbers. Python carries representation or data types for the distinct sorts of numbers that can exist. We can classify these numbers into two parts. The first ones are integers, which are known as whole numbers. These sorts of numbers are either positive or negative in Python. Then there are the numbers, which are known as Floating-point numbers. These numbers are decimal values, which can be negative or positive.
- Python’s arithmetic operators
You may utilize the mathematical operators to perform any kind of mathematical operations on two numerical values and operands. The following are some of them:
- The ‘+’ signifies addition.
- The ‘-‘ represents subtraction.
- The ‘*’ means multiplication.
- The ‘/’ signifies division.
- The ‘//’ means floor division.
- Boolean values
Boolean values are acknowledged as binary values. These sorts of values are represented by two numbers, 0 and 1, or true or false.
Reasons why Python is ideal for Data Science
In 2024, organizations across the world will look for the skill of Python when evaluating a candidate’s profile. And it is a no-brainer why the majority of the aspirants are looking to begin their journey in data science with Python. Here are some of the reasons that make it ideal for data science:
- First and foremost, it is a full package. In addition to being a general-purpose language, it is also tailored for any data analysis tasks. Generally, data scientists are required to utilize a lot of algorithms for which the language is perfectly developed as well.
- Libraries and scalability are one of the biggest reasons why there is a major preference for Python. Every one of the libraries that the data scientists prefer is present here. Furthermore, it comes with a distinct number of exclusive libraries. Additionally, Python has put itself in a position where it is known as the best scalable language, as someone can develop concrete language without any kind of hassle. This feature is perfect for data scientists working tirelessly to develop specific data analysis models and applications.
- It is known for easy implementation. It is good news for every aspiring data scientist. However, it is extremely vital to understand the ins and outs of the language before getting into the intricate details of machine learning or artificial language. Python is very famous as a language for beginners, and it does not at all have a steep learning curve and does not need a lot of learning time from any aspiring candidate who is looking to master the language.
- The massive community of Python also acts as one of the key reasons why it is ideal for data science. If you are looking to learn data science with Python, you would be pleased to know that a robust community accompanies it.
Top 3 Data Science courses with Python
If you are looking for an all-rounded course in data science, then check out
Python And Data Science Full Course | Data Science With Python Full Course In 12 Hours | Simplilearn.
On that note, let us take a look at the top 3 data science courses with Python:
- Data Scientist
It is one of the well-known courses throughout the community, and it is the perfect first course for beginners who are looking to dip their toes in data science. Collaborating with IBM, this data science course features many ask-me-anything sessions, masterclasses, and hackathons. You can get hands-on experience in Python, ChatGPT, SQL, and many more.
- Data science with Python course
If you are looking for a comprehensive overview of Python and understand the tools deeply, then this is the course for you. For the majority of roles in data science, learning Python has become paramount. Giving you a blended learning approach, this course will take you through from A to Z of mathematical computing and data wrangling.
- Post-graduate program in data science
A collaboration of Simplilearn, Purdue, and IBM, this data science course is ideal for every individual who is looking to supercharge their data science career with Python. Benefit from the guidance from the experts and take a deep dive into data science with Python.
Wrapping up
So there you have it! Three of the top courses you can pursue. Now that you have a better understanding of why Python is preferred for data science, you can explore different courses and find out the ones you need to learn for better knowledge of the field and the tools.
