Python is an interpreted, object-oriented, high-level programming language. Python’s simple, easy to learn syntax assures readability and therefore reduces the cost of program maintenance at a greater extent. Python supports modules and packages, which encourages program modularity and code reuse.

The three important libraries used for Data Science are are NumPy, Pandas, and Matplotlib.

A person is proficient in these libraries with Graduate Degree having mathematics background is more prone to get hired in Data Science field.

## Version

I recommend using the Python 3. x version is ideal for data science.The most popular and recent frameworks and libraries like Tensorflow are also supported in Python 3 well.

- Step 1: Get comfortable with Python
- Step 2: Learn data analysis, manipulation, and visualization with pandas and Numpy
- Step 3 Learn Machine Learning
- Step 4: Keep learning and practicing

Step 0: Figure out what you need to learn

We can see that Mathematics and Computer science are closely related to Data Science in general.

Many people will tell you that you can’t become a data scientist until you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more.

It does require knowledge of a programming language and the ability to work with data in that language in order to become a Data Scientist ?

. Indeed You need mathematical proficiency to become really good at data science, but you only need a basic understanding of mathematics to get started.

It’s true that the other specialized skills listed above may one day help you to solve data science problems. However, you don’t need to master all of those skills to begin your career in data science. You can begin today, and we are here to help you!

## Get Proficiency in Python

Python and R are two various choices as programming languages for data science. R tends to be more popular in academics where Python tends to be more popular in industry, but both languages have a lot of of packages that support the data science workflow

Anaconda distribution is most comfortable package when you learn Python because it simplifies the process of package installation and management on various platforms.

You also don’t need to become a Python expert to move on to step 2. Instead, you should focus on mastering the following: data types, data structures, imports, functions, conditional statements, comparisons, loops, and comprehensions.

All these are the basic building blocks of any computer languages.

If you want to know how python affects career in Data Science please go through my article https://cibersindu.com/career-opportunities-in-data-science/

## Learn Python Libraries

Learn data analysis, manipulation, and visualization with Pandas and Numpy

For working with data in Python, you should learn how to use the Numpy and

Pandas Libraries. It consists of a excellent performance data structure (called “DataFrame”) that is suitable for tabular data with columns of different types, similar to an Excel spreadsheet or SQL table. It faciluity for reading and writing data, handling missing data, filtering data, cleaning messy data, merging datasets, visualizing data, and so much more. In short, learning pandas will significantly increase your efficiency when working with data.

However, pandas includes an overwhelming amount of functionality, and provides too many ways to accomplish the same task. Those characteristics can make it challenging to learn pandas and to discover best practices.

Learn machine learning with scikit-learn

Building “machine learning models” to predict the future or automatically extract insights from data is the most attractive part of data science. scikit-learn is the most popular library for machine learning in Python, and for good reason:

- It gives an interface to different models.

- It offers many parameters for each model, but also chooses sensible defaults.
- Its documentation is exceptional, and it helps you to understand the models as well as how to use them properly.

## Learn machine learning in more depth

Machine learning is a complex field. Although scikit-learn provides the tools you need to do effective machine learning, it doesn’t directly answer many important questions:

- How do I know which machine learning model will work “best” with my dataset?
- How do I interpret the results of my model?
- How do I evaluate whether my model will generalize to future data?
- How do I select which features should be included in my model?

## Consistent learning and practising

Here is my best advice for improving your data science skills: Find “the thing” that motivates you to practice what you learned and to learn more, and then do that thing.

- Learn to Grow vendors are a great way to practice data science without coming up with the problem yourself. Don’t worry about how high you place, just focus on learning something new with every small project.

- Contributing to open source projects will help you to practice collaborating with others.
- If you create your own data science projects, you should share them on your testimonials. That will help to show others that you know how to do reproducible data science.
- I also have a few other tips for staying up-to-date as a data scientist.

Your data science journey has only begun! There is so much to learn in the field of data science that it would take more than a lifetime to master. Just remember: You don’t have to master it all to launch your data science career, you just have to get

started!

## Questions and Answers

What is a data scientist salary?

The average data scientist salary is $100,560, according to the U.S. Bureau of Labor Statistics. The driving factor behind high data science salaries is that organizations are realizing the power of big data and want to use it to drive smart business decisions

Should I learn Python before data science?

Before we explore how to learn Python for data science, we should briefly answer why you should learn Python in the first place. In short, understanding Python is one of the valuable skills needed for a data science career. Though it hasn’t always been, Python is the programming language of choice for data science.

Is data science a stressful job?

First, data scientists typically work in stressful environments. They may be part of a team, but it’s more frequent that they spend time working alone. Long hours are frequent, especially when you’re pushing to solve a big problem or finish a project, and expectations for your performance are high.

Which degree is best for data scientist?

With 18.3%, Computer Science is the most well-represented degree among data scientists. This isn’t a complete shock, since good programming skills are essential for a successful career in the field. It’s not all that surprising that a degree in Statistics or Mathematics is among the top of the list (16.3%).

Is data science require coding?

You need to have knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java, with Python being the most common coding language required in data science roles. These programming languages help data scientists organize unstructured data sets

When you Google for the math requirements for data science, the three topics that consistently come up are calculus, linear algebra, and statistics. The good news is that — for most data science positions — the only kind of math you need to become intimately familiar with is statistics.

Can a math student become a data scientist?

To become a data scientist, you could earn a Bachelor’s degree in Computer science, Social sciences, Physical sciences, and Statistics. Therefore, you can enroll for a master’s degree program in the field of Data science, Mathematics, Astrophysics or any other related field.

Can I become data scientist after BSC maths?

You can definitely become a data scientist. Your background in Statistics comes handy in handling data. However, you should do a course related to data analytics to enter the field. You can do an MBA in Data Analytics to get into data management as well or go for industry-led courses in Big Data.

Can I become data scientist after BCA?

You can be a data scientist or analyst after BCA or MCA. But you need to proper degree for this. You can go for PGDM in data analytics after completing your graduation in BCA. Then you can learn more about data analyst and become a data analyst or scientist

## Summary

Python 3.x version is ideal for Data Science

Steps to get hired in Data Science are in 4 steps :

- Get comfortable with Python
- Proficiency in Data Analysis, visualization and Cleaning
- Learn Machine Learning
- Keep Learning and Practising

Learn 3 Python Libraries for Data Science are :

- Numpy
- Pandas
- Matplotlib

I have written some blogs in Data Science. You can **Read **it here

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