Top Python Concepts To Understand Before Learning Data Science

Introduction

The ecosystem for data science is dominated by Python. The ease of learning and the wide variety of data science resources are the main drivers of such domination. Python may be used for a variety of things and is not  for data science. Python's applications include the creation of websites, mobile applications, and video games. You may want to become a data scientist or you may be one and want to increase the number of available tools. The purpose of this blog is to give a thorough learning route to people who are unfamiliar with Python for data science. This route offers a breakdown of the concepts you must learn to use Python for data science.  Continue reading to explore your possibilities to become a data scientist with skills like Python. 

Top Python Concepts For Data Science And Why They Are Important To Understand

The field of data science can be intimidating  for newcomers. Many individuals will tell you that you need to master some skills before you can become a data scientist. For example statistics, linear algebra, calculus, programming, databases, distributed computing, is a must. You must know machine learning, visualisation, experimental design, clustering, deep learning, and natural language processing. 

What precisely is data science then? It entails raising challenging issues and then using data to offer solutions. The general data science workflow is as follows:

• Ask a query
• A massive amount of information that could assist you in resolving the query.
• Clean the data
• Investigate, evaluate, and display the data
• Build a machine learning model, then test it.
• Forward results

If you want to use Python as your data science language, you will need to understand these fundamentals to get started on your data science journey. As a data scientist, you will interact with them, so it is wise to understand how they operate.

To  learn data science with Python, follow these gradual stages:

1. Set Up Your Development Environment

Jupyter Notebook is a potent programming environment for creating and presenting data science projects. Installing Anaconda is an easy approach for you to set up a Jupyter Notebook on your PC. The most popular Python distribution for data science, Anaconda, includes all the most used libraries by default.

2. Only The Fundamentals Of Python

Python training is available at Code Academy and takes about 20 hours to finish. Since your only aim is to become familiar with the fundamentals of the Python programming language, you don't need to upgrade to the Pro Version.

3. An Excellent Resource For Learning Them Is Numpy And Pandas

Python is sluggish when handling huge volumes of data and methods that need a lot of maths. Then, why is Python the most widely used programming language for data science, you might wonder?The answer is that using a C or Fortran extension, it is simple to offload number-crunching operations from Python to the lower layer. That is what Pandas and Numpy do. You should first learn Numpy. The most basic Python module for scientific computing is this one. Since multidimensional arrays are the most fundamental data structure used by most machine learning methods, Numpy supports them.

4. Use Matplotlib To Visualise Data

The core Python library for making simple visualisations is called Matplotlib. You must learn how to use Matplotlib to make some of the most popular charts. For instance Line charts, Bar charts, Scatter plots, Histograms, and Box plots. Seaborn is a top-notch charting library that is based on matplotlib and  integrated with Pandas. 

5. Learn About Machine Learning And Scikit-Learn

The core of this entire procedure is now at hand. The best Python Library for machine learning is scikit-learn. This works as a quick summary provider of the library. You will learn about machine learning in general, as well as supervised and unsupervised learning techniques including clustering, decision trees, and ensemble modelling. Complete the assignments from particular lectures  after those lectures. To give yourself a major boost in your search for a data scientist position, you should also have a look at the "Introduction to Data Science" course.

6. Practice

You now own all the technical abilities you require. Practice makes perfect, and what better way to perfect your skills than to compete against other Data Scientists? Go ahead and participate in one of the live events that are now taking place on DataHack and Kaggle to put all you have learned to use!

7. Deep Learning

It's time to try out deep learning now that you have learned the majority of machine learning techniques. Although there is a strong chance you already know what deep learning is, in case you do not, here is a quick introduction.

Our Learners Also Read: Top Python Commands List

How To Use Python With SQL

Organisations store data in databases. You must understand how to use Python to analyse Jupyter Notebook and SQL to retrieve data. Data scientists modify data using both Pandas and SQL. There are many data manipulation tasks that you can complete using SQL. But other activities may need Pandas for effective completion. You can retrieve data using SQL and manipulate it using Pandas.

Be Comfortable With Python

R and Python are both powerful programming languages for data science. Although Python has a wide use in business, R is more common in academics. Several tools are available in both languages that support the data science workflow. To get started, it is not essential to master both Python and R. Instead, you should focus on knowing a single language and the data science tools that are built around it. To start using Python, you might need to download the Anaconda distribution. On Windows, OS X, and Linux, it facilitates package management and installation.

Conclusion

We have gone through some of Python's most important ideas and concepts. The majority of data science-related tasks are carried out using third-party libraries and frameworks like Pandas, Matplotlib, Scikit-learn, and TensorFlow. Python is a useful tool for data analysts since it is designed for repeated activities and data processing, and anyone who has worked with a lot of data understands how repetition occurs. Data analysts can focus on the more fascinating and satisfying aspects of their jobs because they have a tool to handle tedious tasks. But, to  use such libraries, you should have a thorough understanding of Python's fundamentals and concepts. They presumptively know the fundamentals of Python. Join The IoT Academy to start learning Python!

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