Data Science is a broad subject. If you want to start your career in Data Science,the first thing you need to do is pick the right programming language to easily work with Data Science. Out of all other programming languages like R, Julia, C++; Python is the topmost preferable language for Data Science. It is because Python is easy to write and understand. It also offers numerous libraries used in Data Science. Data Science Training with Python with an online course that will make you master Data Science. If you are wondering how to learn Python for Data Science, this blog will guide you through. Continue reading.
Step 1: Concentrate on Basics of Python
If you want to learn Data Science using Python. Then first you need to concentrate on the basics of Python. Understanding concepts like data types, loops, strings, classes, expectations, Control statements, and File I/O will be very helpful for you to brush up on your basic Python skills. You can opt for an online course to help you guide you through the concepts of Python. Or it will be helpful for you if you join the Python community where many professionals share their coding problems and solutions. It will be much helpful for you to adapt to such a programming mindset by joining the community.
If you are strong enough in the basics of Python programming. Then you should continue with the next step.
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Step 2: Learn Python Libraries Used for Data Science
By having basic route knowledge of Python you will be much confident to learn more about Python such as libraries. In this phase, you will learn Python libraries, which are why it is used for Data Science. Python has various libraries that are very helpful for Data Science. So if you want to learn Data Science using Python, you must learn what the Python libraries are and how they are used. You should also get practice using them. The main Python libraries used for Data Science are mentioned below:
- The Python libraries used for data mining and Machine Learning are mentioned below:
- Scrapy: Scrapy is one of the most popular Python libraries used for web scraping and retrieving structured data. It is also used for a variety of beneficial applications such as data mining, information processing, and historical archiving.
- Scikit-Learn: Scikit-Learn is an open-source library mainly used for machine learning algorithms. It makes data mining and data analysis processes easy, fast, and more effective. It is created by using NumPy, SciPy, and Matplotlib.
- For Data Analysis and Modelling, the following Python libraries are used in Data Science.
- Numpy: Numpy has Many useful features that are used when executing operations on n-arrays and matrices in Python. It facilitates the processing of arrays containing values of the same type of data and makes mathematical calculations on arrays easier. It improves efficiency and shortens time complexity.
- Pandas: it is one of the Python libraries which is mainly used for data analysis and data modeling. It can read data from various file formats. Pandas offer a variety of data structures that are high-performance and user-friendly in operations for data manipulation.
- SciPy: Working with the SciPy library is easy because of its rich documentation. It provides efficient mathematical procedures that are excellent for all methods of academic programming languages.
- In Data Science, for Data Visualization the Python libraries used are as follows:
- Matplotlib: Matplotlib is an open, strong, and simple Data visualization library of Python. It is a 2-D and 3-D plotting toolkit. It creates graphs and visualization that are mostly used for the visualization of the data. It is also used for data manipulation and data management.
- Seaborn: Seaborn is also a Data Visualization library of Python. It is free and open-source and designed based on Matplotlib. It can be used for data analysis and data visualization. The graphs implemented by using Seaborn can assist us in identifying data patterns.
These are the Python libraries you must learn to improve your expertise in Python for Data Science.
Step 3: Gain In-Depth Knowledge in Data Science Concepts
Once you are good with the Python libraries required for Data Science then you can start to learn Data Science concepts. Data Science is a vast subject. So take your time to understand and gain in-depth knowledge of Data Science. However, Data Science can be learned by self-studying. But with proper guidance, your knowledge will not only understand how Data Science works but also can know how it is applied in the real world. Data Science is a combination of various concepts used to read, write, analyze and visualize the data. Concepts such as Statistics, Data Discovery, Deep Learning, Machine Learning, Visualization, Data Preparation, Data Analysis are some of the important Data Science concepts. If you are a beginner looking for the right place to learn Data Science then Data Science for beginners tutorial is for you. Check out.
Step 4: Practice Implementing Data Science and Machine Learning Models using Python
Just knowing things is not enough. Data Science is a very complicated subject and it is a place where you can learn many new techniques each day. So, for constant learning and becoming an expert in Data Science, Practice is the Key. After learning each concept in the theory you must try to implement it. You should make your hands dirty. You may have good knowledge of the theory but remember that does not help you land a job or help you lead in this career. You must know how things work. And how to implement theoretical concepts to real-time solutions.
Step 5: Acquire Hands-on with Data Science Projects using Python
Last, the most crucial thing you need to know (especially if you are a fresher) is that working on projects is the only thing that will help you cross the line called Interview. Most companies or interviewers recruit a person because of his/her projects. They look for a candidate who has knowledge of the industry and how it works. So having an industry-based project in the resume will help you stand out.
Industry projects give a hands-on experience that gives you the confidence to crack the interview. You can work on data analysis, Data Visualization, and data preparation using Machine Learning and Python. You can either work on self-designed projects or Public projects.
Thus, these are the basic five steps for beginners who want to learn Python for Data Science. We hope it helps you.