Data Science With Python Course
From this guide, you will come to know how to learn Data Science with the Python course, including the favorite curriculum for self-study. As you know, the Data Science course is all about problem-solving, exploration, and gathering valuable information from the data.
For this, you need to dispute different datasets, train the machine learning models, analyze the results, and much more. Forbes ranked Python in the Top 10 Technical Skills concerning job demand growth. Let us now discuss why it is so important?
Why Learn Python for Data Science?
Python is one of the essential skills required for a data science career. Moreover, in recent years, Python has become one of the most preferred programming language choices for data science.
Here are some of the significant impacts of Python-
It overtook the R language on Kaggle in 2016, which is the biggest platform for data science fields.
It also overtook R on KDNuggets in 2017, which was the most important tool for all data scientists.
In 2018, it was estimated that around 66% of all data scientists were using Python daily. Therefore, it is considered the number one language for all analytics professionals.
The professional data scientists believe that this trend will continue to grow more with the increase in the Python ecosystem. If your journey in Python programming is just starting, you must be happy to know that there are an ample number of opportunities in this field which is continuously growing.
If we go according to the report of Indeed, an average salary for a Data Scientist is $127,918. And the good news is, this number is only believed to increase more. The IBM experts believe a 28% increase in the demand for data scientists in the industry by the end of 2020.
Hence, the future for data science is bright, and Python will act as an enhanced additional skill to solve different outcomes. Understanding Python or other programming languages will help in achieving more results in the industry.
Python can be considered best because of its precise and efficient syntax. It can accomplish the same task with less code if compared to other languages. This may help in solving the problems efficiently faster. When combined with Data Science, Python can provide plenty of tutorials, code snippets, and fix the common bugs.
Python is equipped with an all-star lineup of different libraries for data analysis and machine learning. It results in reducing time and producing efficient outcomes.
How To Learn Python Efficiently?
Before we tell you how to learn Python efficiently for Data Science, let us understand first what all things you don’t require.
You do not require a Computer Science degree
There is no demand for a computer science degree to become a data scientist. You will not face any problem till you can go through a clean and logical code in scripting languages like Python or R.
You do not require a complete Python course
As we know, Python and Data Science are not precisely similar to each other. Python is a very vast programming language used in many different fields like Artificial Intelligence, Machine Learning, Data Science, etc. Therefore, it is recommended to learn only a specific part that will be used in this field.
You do not require to learn all the syntax
There is no need to learn all the syntax hand-by-hand as everything is easily available on the Internet. Instead, you should focus on grasping the technique used, such as when and where to use any specific function or how different conditional statements work.
How To Start Learning
You must cover several points step by step to achieve the desired results and then solidify the concepts.
These steps are as follows-
Initially, you have to start by learning some core programming concepts.
Then you have to command over essential data science libraries.
At last, you need to practice and refine your skills by working on actual projects.
This approach will let you reach your goal and master you with time. However, apart from having a command on Python language for a data scientist, you also need to work upon your soft skills. It is recommended to add some complimentary technical skills also in this journey.
Learn Python Fundamentals
The primary step to start is to grab some knowledge on Python Programming Fundamentals. In addition to this, you also work upon the Data Science introduction. In the beginning, you need to start with one of the most important tools known as the Jupyter Notebook. It comes with pre-installed Python libraries to help those who are new to the programming language.
Begin your learning by joining a community. When you join a community, it allows you to be surrounded by the same-minded people and increases your chances for employment. According to the Human Resource Society and Management reports, joining a community has around a 30% impact on employment.
You can join easily by creating a Kaggle account and joining a meetup group. Then you can participate in the various discussions of Dataquest’s Slack discussions with the live students and other experts. Initially, when starting, you should begin with the Command Line Interface. It helps run the scripts more efficiently and faster, allowing you to test programs with a vast amount of data.
Practice Mini Python Projects
It is evident that hands-on learning will provide you with a better experience in any field. Having an adequate amount of knowledge and sufficient practice will make you ready to build small Python projects.
You can start with basic programming questions like calculators for an online game, or the weather predicting program in your city. Practicing these types of mini-projects will help you in learning Python.
These projects work similarly in all the languages and offer the best method to solidify your basic understanding.
You can start by building APIs and web scraping. Apart from programming, web scraping will help you gather all the data later.
Try to enhance your learning by finding answers to all the Python programming challenges you face.
You can go through different guidebooks or blog posts or even some source code while learning Python and data science concepts.
The basic skill you need to work upon different projects is handling the database using SQL. SQL helps you to work with databases to alter, edit, and collect various information. SQL acts as a chief principle in the data science community since 40% of data scientists use this technology.
Learn More from Python Training in Vizag
Learn Python Data Science Libraries
Like other programming languages, some libraries are very essential in commanding yourself in Python. The most important ones that are mainly used in the Data Science field are Numpy, Pandas, and Matplotlib. Numpy and Pandas are used mainly for exploring the data, whereas Matplotlib is a data visualization library used in making various graphs.
The community of Python has many experts to help you to start with learning. The online forum platforms like Quora, Stack Overflow, or Dataquest’s Slack have many experts who can share their knowledge to overcome your doubts.
You can take the help of Git, a professional tool to help you track the changes you make into your code. It reduces your effort and minimizes your mistakes and helps you in collaborating with others.
Build your Data Science Portfolio
A good portfolio is a must for data science aspirants. It should include several datasets you have worked on and tell some insights you have learned. There is no need to maintain any particular theme for the portfolio. You can find the datasets that suit you and showcase it all together.
There is a massive benefit for showing a good portfolio, as it may leave a good impression on your fellow data scientists. Also, it justifies that you have spent time on learning Python and other skills. One more advantage is, in the field of data science, a portfolio is like two times more than a resume highlighting your programming skills.
You can start by learning the ability to communicate and collaborating on various technical skills. In the meantime, you have to ensure developing soft skills to work with others. As a data scientist, make sure to have a good background in statistics. It helps you in developing the mindset to focus on the right things rather than merely executing the code.
Apply Advanced Data Science Techniques
You should try to increase your skills whenever you get time. Your whole journey will be of constant learning, but you need to cover some advanced courses to make your basics strong.
You need to be familiar with regression, classification, and k-means clustering. You can also get in touch with bootstrapping models of machine learning and learn to create neural networks using scikit-learn.
Data Science will continuously grow in the upcoming years with numerous more industries. The rate of demand is continuously increasing with exponential more opportunities.
Therefore, data science training is really essential for all those who are developing their interest in Python. Your outcome is entirely dependent on the time you spend on learning.