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Last updated on Nov. 16, 2020, 1:43 p.m. 768 Views



A Master in Computational Intelligence and also Data Science Consultant for Andhra Pradesh State Skill Development Corporation(APSSDC) blended with a passion for nurturing the meaning of education with technology. With expertise in Machine Learning, Data Analytics, Natural Language Processing, and Cloud Computing, he believes in teaching all functionalities to the core making his life's motto to train students to be a Data Scientist rather than a Data Engineer. With a vision of building a better society using technological innovation, he joined Codegnan to bring out and enrich the essence of Technical Education with industrial excellence. His ambition is to make Codegnan the Centre of Excellence towards LeadIndia 2020.


Last updated on Nov. 16, 2020, 1:43 p.m. 768 Views


Python is a flexible programming language that is primarily favoured by software engineers and tech organizations around the globe, from new businesses to the bigger ones. Data Scientists use it widely for data analysis and knowledge generation, while many organizations pick it for its convenience, extensibility, coherence, transparency, and the completeness of its standard library. 

Python programming skills are in great demand and learning it can open ways to endless opportunities in Data Science, Machine Learning, Artificial Intelligence, web development, and much more. The Python for Data Science is applied to-

  • Candidates with no background of coding or want to explore their career in Data Science 
  • Candidates who are currently or planning to enrol in Data Science field
  • Candidates who are currently or planning to enrol in Machine Learning industry
  • Candidates who are currently or planning to enrol in Artificial Intelligence industry

Python for Data Science is used to build strong skills in the basic concepts that are mandatory for Data Science. It includes data handling, feature engineering, statistical analysis, and Python programming. When you start building for the foundation in Python for Data Science, you build and implement predictive analytics algorithms (regression and forecasting), and classification and segmentation Machine Learning algorithms (K Means and Random Forest) using Python. Python with Data Science allows flexibility and makes integration of programming easy with complex systems. This is all because of the Python programming which is very dynamic and portable and due to this, the use of Python is wide in the industries.

Understanding the use of Python in Data Science

Consistently, around the United States, in excess of 36,000 climate estimates are given covering 800 unique locales and urban areas. If it starts raining in the middle of your outing but it was assumed to be a sunny day! Such forecasts around the country predict the usage of forecast models. The people at Forecastwatch.com did! Consistently, they accumulate every one of the 36,000 estimates, put them in a database, and compare them with the actual conditions experienced in that area on that day. Forecasters around the country at that point utilize the outcomes to improve their forecast models for the next round. All this collection of data, analysis, and reporting takes a lot of time but Forecastwatch.com does it with the use of a single programming language: Python.

Not only Forecastwatch.com but according to the industry analyst survey of O’ Reilly, 40% of the data scientists make use of Python for their day-to-day work. Trusted organizations such as Google, NASA, and CERN use Python for almost every programming purpose under the sun… including, in increasing measures of data science.

Python: the new life of Data Science

Algorithms or predictions made in Data Science are very tricky and mind-boggling. If the programming language will also be tricky then a data scientist would use his maximum time in coding and syntax only. Even Java can be used for Data Science. But if the comparison between two programming Languages is done then Python will be a winner. Because of the small code snippets, Python is the most loved language for Data Science and this is the primary reason for utilizing Python for Data Science. See the example below:

Hello, world program in Java:

class Prog 

		public static void main(String args[])

                System.out.println(“Hello World, Java!”):



Hello world program in Python:

print(“Hello World, Python!”)

Python’s inherent readability and simplicity make it relatively easier to pick up. The number of dedicated analytical libraries available in Python means that data scientists in every sector will find packages already tailored to their needs freely available for download. Due to Python's extensibility and universally useful nature, it is unavoidable as somebody would inevitably begin utilizing it for data analysis. As a jack of all business sectors, many organizations as of now intensely invest in the language they saw preferences to normalizing on it and extending it to that reason.

Python: a bank of libraries for Data Science

There are a lot of programming languages available in the market. But Python has grown up with almost 72,000 Python Package Index (PyPi) and it is kept on growing constantly. With Python explicitly intended to have a lightweight and stripped-down centre, the standard library has been developed with tools for each kind of programming task- a "batteries included" philosophy that permits language clients to rapidly get down to the stray pieces of tackling issues without filtering through and choose between competing function libraries.

Moreover, the Pandas Python library is used for the data analysis that is, doing everything from importing data from Excel spreadsheets to processing sets for time-series analysis. Pandas put basically every normal data munging tool readily available. This implies fundamental cleanup and some advanced manipulation can be performed with Pandas amazing data frames. Pandas is based on NumPy, perhaps the earliest library behind Python's data science success story. NumPy's capacities are uncovered in Pandas for advanced numerical analysis.

If you are looking for advanced, then-

  • SciPy is the scientific equivalent of NumPy, offering tools and techniques for scientific data analysis
  • Statsmodels focuses on tools for statistical analysis
  • Sci-kit-Learn and PyBrain are machine learning libraries that provide modules for building neural networks and data preprocessing

And here are the peoples’ favourites:

  • SymPy – for statistical applications
  • Shogun, PyLearn2 and PyMC – for machine learning
  • Bokeh, d3py, ggplot, matplotlib, Plotly, prettyplotlib, and seaborn – for plotting and visualization
  • csvkit, PyTables, SQLite3 – for storage and data formatting

It is called ‘Pythonic’ when the code is written in a fluent and natural style. Apart from that, Python is also known for other features that have captured the imaginations of the data science community.

How Python is the perfect fit

There are customized circumstances where it is the best data science tool for the activity. It is impeccable when data analytics undertakings include integration with web applications or when there is a need to incorporate statistical code into the production database. The full-fledged programming nature of Python makes it an ideal fit for implementing algorithms. Python’s packages rooted in explicit data science occupations. Packages like NumPy, SciPy, and pandas produce great outcomes for data analysis employments. While there is a requirement for graphics, Python's matplotlib emerges as a decent package, and for Machine Learning undertakings, sci-kit-learn turns into the perfect substitute.

Is Python used for Machine Learning?

With regard to data science, machine learning is one of the noteworthy components used to expand the value of data. With Python as the data science tool, investigating the essentials of ML turns out to be simple and powerful. More or less, ML is progressively about statistical, mathematical optimization, and probability. It has become the most favoured ML tool in the manner it permits competitors to 'do maths' without any problem. 

There is Numpy for numerical straight linear algebra, CVXOPT for convex optimization, Scipy for general scientific computing, SymPy for symbolic algebra, PYMC3, and Statsmodel for statistical modelling. 

With the grasp on the nuts and bolts of ML algorithms including logistic regression and linear regression, it makes it simple to actualize ML frameworks for forecasts by the method of its sci-kit-learn library. It's easy to customize for neural systems and deep learning with libraries including Keras, Theano, and TensorFlow.

Career path for the use of Python in Data Science

As per the research, the journey to learn Python for Data Science will be a 4-week journey. You don’t need to understand Python like your own kid, simple basics will be sufficient. If you follow the below career path then you will attain a core level understanding of Python for Data Science.

Week 1:

  • Basic of Python Spyder
  • Introduction Spyder
  • Setting working Directory
  • Creating and saving a script file
  • File execution, clearing console, removing variables from environment, clearing environment
  •  Commenting script files
  • Variable creation
  • Arithmetic and logical operators
  • Data types and associated operations

Week 2:

  • Data Structures
  • List
  • Tuples
  • Dictionary
  • Sets
  • Numpy
  • Array
  • Matrix and associated operations
  • Linear algebra and related operations

Week 3:

  • Pandas data frame and data frame related operations on Toyota Corolla dataset
  • Reading files
  • Exploratory data analysis
  • Data preparation and preprocessing
  • Data visualization on Toyota Corolla dataset using matplotlib and seaborn libraries
  • Scatter plot
  • Line plot
  • Bar plot
  • Histogram
  • Box plot
  • Pair plot
  • Control structures using Toyota Corolla dataset
  • if-else family
  • for loop
  • for loop with if break
  • while loop
  • Functions

Week 4: 

  • Case Study
  • Regression
  • Predicting the price of pre-owned cars
  • Classification
  • Classifying personal income

Projects and Further Learning 

To truly become acquainted with technology and to learn Python for Data Science, you should create something in it. Odds are, you will stall out on your way, and each time you stall out, you will discover out all alone. Start with issues accessible on the Internet, and build your skills. At that point, think of your own issues, characterize and illuminate them. We additionally recommend that you take a glance at deep learning. It is a subfield of machine learning utilized for algorithms roused by the structure and function of the mind called artificial neural networks.

How long will it take to learn Python for Data Science?

There are a ton of evaluations for the time it takes to learn Python. For data science explicitly, estimates a range from 3 months to a time of predictable practice. We've watched individuals travel through our courses at lightning velocity and other people who have taken it much slower. Truly, everything relies upon your ideal timeline and free time that you can commit to learning Python programming and the pace at which you learn. Codegnan’s courses are made for you to go at your own speed. Every way is loaded with missions, hands-on learning, and chances to pose inquiries so you can get a top to bottom dominance of data science essentials. 

Additionally, Python is not the single thing you need to learn for Data Science, but there are the following couple of things that are required to learn:

  • Data visualization using Matplotlib
  • SQL with Python
  • Statistics with Python

Final thoughts

The width and height of Data Science are growing constantly and the tools used for value extraction from Data Science have also increased in numbers. With the advancement of technologies such as artificial intelligence, machine learning, and predictive analytics, the demand for experts with Python skills is rising significantly. It is widely used in web development, scientific computing, data mining, and others. Learning Python programming will give you versatility and competence as a data scientist. Good luck readers!

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