Data science has tremendous growth opportunities and is one of the hot careers in the current world. Many businesses are thriving for skilled data scientists. Data science requires many skills to become an expert – One of the essential skills in Python programming.

Python is a programming language widely used in many fields. It is considered as the king of the coding world. Data scientists extensively use this language, and even beginners find it easy to learn the Python language. There are many Python data science courses that guide and effectively train you to determine the python language.

1. What is Python?

Python is an interpreted and object-oriented programming language. It is an easily understandable language whose syntaxes can be grasped by a beginner quickly. Guido found it in 1991.

It is supported in operating systems like Linux, Windows, macOS, and a lot more. The Python is developed and managed by the Python software foundation.

The second version of Python was released in 2000. It features list comprehension and reference counting. This version was officially stopped functioning in 2020. Currently, only the Python version 3.5x and later versions are supported.

2. Why is Python used in data science?

Python is the most preferred programming language by the data scientists as it effectively resolves tasks.

It is one of the top data science tools used in various industries. It is an ideal language to implement algorithms. Python’s sci-kit-learn is a vital tool that the data scientist finds useful while solving many machine learning tasks. Data science uses Python libraries to solve a task.

Python is perfect when it comes to scalability. It gives you flexibility and multiple solutions for different problems. It is faster than Matlab. The main reason why YouTube started working in Python is because of its exceptional scalability.

a) Features of Python language

  • Python has a syntax that can be understood easily.
  • It has a vast library and community support.
  • We can quickly test codes as it has interactive modes.
  • The errors that arise can be easily understood and cleared quickly.
  • It is free software, and it can be downloaded online. Even there are free online Python compilers available.
  • The code can be extended by adding modules. These modules can also be implemented in other languages like C, C++, etc.
  • It offers a programmable interface as it is expressive.
  • We can code Python anywhere.
  • The access to this language is simple. So we can easily make the program working.

3. The different types of Python libraries used for data science

a) Matplotlib

Matplotlib is used for effective data visualization. It is used to develop line graphs, pie charts, histograms efficiently. It has interactive features like zooming and planning the data in graphics format. The analysis and visualization of data are vital for a company. This library helps to complete the work efficiently.

b) NumPy

NumPy is a library that stands for Numerical Python. As the name suggests, it does statistical and mathematical functions that effectively handles a large n-array. This helps in improving the data and execution rate.

c) Scikit-learn

Scikit- learn is a data science tool used for machine learning. It provides many algorithms and functions that help the user through a consistent interface. Therefore, it offers active data sets and capable of solving real-time problems more efficiently.

d) Pandas

Pandas is a library that is used for data analysis and manipulation. Even though the data to be manipulated is large, it does the manipulation job easily and quickly. It is an absolute best tool for data wrangling.

It has two types of data structures .i.e. series, and data frame. Series takes care of one-dimensional data, and the data frame takes care of two-dimensional data.

e) Scipy

Scipy is a popular library majorly used in the data science field. It does scientific computation. It contains many sub-modules used primarily in science and engineering fields for FFT, signal, image processing, optimization, integration, interpolation, linear algebra, ODE solvers, etc.

4. Importance of data science

Data scientists are becoming more critical for a company in the 21st century. They are becoming a significant factor in public agencies, private companies, trades, products, and non-profit organizations. A data scientist plays as a curator, software programmer, computer scientist, etc.

They are the central part of managing the collection of digital data. According to our analysis, we have listed below the significant reasons why data science is essential in developing the world’s economy.

  • Data science helps to create a relationship between the company and the client. This connection helps to know the customer’s requirements and work accordingly.
  • Data scientists are the base for the functioning and the growth of any product. Thus they become an essential part of doing significant tasks .i.e. data analysis and problem-solving.
  • There is a vast amount of data traveling around the world, and if it is used efficiently, it results in the successful growth of the product.
  • The resulting products have a storytelling capability that creates a reliable connection among the customers.
  • This is one of the reasons why data science is popular.
  • It can be applied to various industries like health-care, travel, software companies, etc.

  • Big data analytics is majorly used to solve the complexities and find a solution for IT companies’ problems, resource management, and human resource.
  • It greatly influences the retail or local sellers. Currently, due to the emergence of many supermarkets and shops, the customers approaching the retail sellers are drastically decreased. Thus data analytics helps to build a connection between the customers and local sellers.

Are you finding it difficult to answer the questions in an interview? Here are some frequently asked data science interview questions on basic concepts

a) Q. How to maintain a deployed model?

To maintain a deployed model, we have to

  • Monitor
  • Evaluate
  • Compare
  • Rebuild

b) Q. What is the random forest model?

The random forest model consists of several decision trees. If you split the data into different sections and assign each group of data a decision tree, the random forest models combine all the trees.

c) Q. What are recommendation systems?

A recommendation system recommends the products to the users based on their previous purchases or preferences. There are mainly two areas .i.e. collaborative filtering and content-based filtering.

d) Q. Explain the significance of p-value?

  • P-value <= 0.5 : rejects the null-hypothesis
  • P-value > 0.5 : accepts null-hypothesis
  • P-value = 0.5 : it will either except or deny the null-hypothesis

Q. What is logistic regression?

Logistic regression is a method to obtain a binary result from a linear combination of predictor variables.

Q. What are the steps in building a decision tree?

  1. Take the full data as the input.
  2. Split the dataset in such a way that the separation of the class is maximum.
  3. Split the input.
  4. Follow steps 1 and 2 to the separated data again.
  5. Stop this process after the complete data is separated.

5. Best Python data science courses

Many websites provide Data Science online courses. Here are the best sites that offer data science training based on Python.

  • GreatLearning
  • Coursera
  • EdX
  • Alison
  • Udacity
  • Skillathon
  • Konvinity
  • Simplilearn

6. How do data science courses help in a successful career, post-COVID-19 pandemic?

The economic downfall due to COVID-19 impacts has led to upskill oneself as the world scenarios are changing drastically. Adding skills to your resume gives an added advantage of getting a job quickly.

The businesses will invest mainly in two domains .i.e. data analysis of customer’s demand and understanding the business numbers. It is nearly impossible to master data science, but this lockdown may help you become a professional by indulging in data science programs.

Firstly, start searching for the best data science course on the internet. Secondly, make a master plan in such a way that you complete all the courses successfully. Many short-term courses are there online that are similar to the regular courses, but you can complete it within a few days.

For example, Analytics Labs are providing these kinds of courses to upskill yourself. So this is the right time where you are free without any work and passing time. You can use this time efficiently by enrolling in these courses and become more skilled in data science than before.

These course providers also give a data science certification for the course you did; this will help build your resume.

Data science is a versatile field that has a broad scope in the current world. These data scientists are the ones who are the pillars of businesses.

They use various factors like programming languages, machine learning, and statistics in solving a real-world problem. When it comes to programming languages, it is best to learn Python as it is easy to understand and has an interactive interface. Make efficient use of time in COVID-19 lockdown to upskill and build yourself.