Python for Data Science is a must-learn skill for professionals in the Data Analytics domain. With the growth in the IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language for data-driven development. Through this article, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Before we begin, let me just list out the topics I’ll be covering through the course of this article.
You can go through this Python for Data Science video lecture where our Python Training expert is discussing each & every nitty-gritty of the technology.
Python for Data Science | Data Science using Python | Hireleven
What Is Data Science?
Data Science has emerged as a very promising career path for skilled professionals. The truest essence of Data Science lies in the problem-solving capabilities to provide insights and solutions driven by data. There is a lot of misconception when it comes to Data Science, the Data Science life cycle is one way to get a clearer perspective to understand what Data Science really is.
Data Science Life Cycle
Data science takes into account the whole process starting from understanding the business requirements to preparing the data for model building and deploying the insights finally. The whole process is handle by different professionals including Data Analysts, Data Engineers, and Data Scientists. The role depends on the size of the company, sometimes all the processes are done by just one professional. Let us try to understand why python is the right programming language for Data Science.
Why Python For Data Science?
Python is no-doubt the best-suited language for a Data Scientist. I have listed down a few points which will help you understand why people go with Python for Data Science:
- Python is a free, flexible and powerful open-source language
- Python cuts development time in half with its simple and easy to read syntax
- With Python, you can perform data manipulation, analysis, and visualization
- Python provides powerful libraries for Machine learning applications and other scientific computations
Data Scientist is the hottest job profile in the market right now, with more than 250,000 to 1.7 million expect job openings in the year 2020 alone is pretty promising for any professional to learn Data Science.
A Data Scientist job profile stays open for 5 more days on any portal compared to any other job opening.
The future looks pretty promising too, according to sources, there is going to be a massive surge in the Data Science job market with an expected growth of further 500,000 to 11 million jobs by 2025.
With an increasing data flow, it is pretty evident that the market is thriving on Data. And it is going to make an impact almost everywhere, so the scope is not just related to a particular domain. Data Science is an integral part of any organization, business, etc.
Let us take a look at the fruits of hard work that a job profile related to Data Science gets in the market.
Data Science Salary Trends
The Data Science job market is fill with job profiles, so to give you a clearer perspective here are the top 3 job profiles for Data Science related jobs in the market with their average salaries in The United States and India.
Let us take a look at the company trends revolving around the Data Science Job market.
Company Trends For Data Science
Data Science is an integral part of any organization, business, etc. Some of the major players in the market are listsd down, but we have to be clear that these are only the tip of the much bigger iceberg. The amount of data flowing in the world has almost every organization buckle up for the kind of impact data-driven development makes on a business. So even the smaller businesses are thriving on the Data Science market and making their mark in the industry.
Let us take a look at the basics that must be master in order to master Data Science.
Python Basics For Data Science
Now is the time when you get your hands dirty with Python programming. But for that, you should have a basic understanding of the following topics:
- Variables: Variables refer to the reserved memory locations to store the values. In Python, you don’t need to declare variables before using them or even declare their type.
- Data Types: Python supports numerous data types, which defines the operations possible on the variables and the storage method. The list of data types includes – Numeric, Lists, Strings, tuples, Sets, and Dictionary.
- Operators: Operators helps to manipulate the value of operands. The list of operators in Python includes- Arithmetic, Comparison, Assignment, Logical, Bitwise, Membership, and Identity.
- Conditional Statements: Conditional statements help to execute a set of statements based on a condition. There are namely three conditional statements – If, Elif and Else.
- Loops: Loops are used to iterate through small pieces of code. There are three types of loops namely – While, for and nested loops.
- Functions: Functions are use to divide your code into useful blocks, allowing you to order the code, make it more readable, reuse it & save some time.
For more information and practical implementations, you can refer to this blog: Python Tutorial.
Loading The Data
The very first step, to begin with, is loading the data into your program. We can do so by using the read_csv( ) from the Python panda’s library.
Cleaning the Data
The next step is to look for irregularities in the data by doing some data exploration. Finding out the null values and replacing them with other values or dropping that row altogether is involv in this phase.
Python Libraries For Data Science
This is the part where the actual power of Python with Data Science comes into the picture. Python comes with numerous libraries for scientific computing, analysis, visualization, etc. Some of them are listed below:
NumPy is a core library of Python for Data Science which stands for ‘Numerical Python’. It is use for scientific computing, which contains a powerful n-dimensional array object and provides tools for integrating C, C++, etc. It can also be use as a multi-dimensional container for generic data where you can perform various Numpy Operations and special functions.
Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis also. It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.
Matplot-lib is a powerful library for visualization in Python. It can be used in Python scripts, shell, web application servers, and other GUI toolkits. You can use different types of plots and how multiple plots work using Matplot-lib also.
Seaborn is a statistical plotting library in Python. So whenever you’re using Python for Data Science, you will be using Matplot-lib (for 2D visualizations) and Seaborn, which has its beautiful default styles and a high-level interface to draw statistical graphics.
learn is one of the main attractions, wherein you can implement machine learning using Python. It is a free library that contains simple and efficient tools for data analysis and mining purposes also. You can implement the various algorithms, such as logistic regression, time series algorithm using learn. It is suggested that you should go through this tutorial video on learn to understand machine learning and various techniques before proceeding ahead.
Master Data Science With Use Cases
Let us go ahead and learn with the help of a few examples. These examples are driven by problem statements and we will derive our conclusions based on Data Science life cycle processes likewise.
Best Team Selection Using FIFA Data Set
We have a data set consisting of various players including stats about their skills, nationality, clubs, etc. Our goal is to come up with a team that would be the best among all the players for a particular team formation.