Published by Kirandeep Kaur | 26:05:2020

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 enroll in Data Science field
- Candidates who are currently or
planning to enroll in Machine Learning industry
- Candidates who are currently or planning to
enroll 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 center, 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 dataframes. 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
- Scilkit-Learn and PyBrain are machine learning
libraries that provide modules for building neural networks and data
preprocessing

And here are the peoples’ favorites:

- 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, scikit-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 favored 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 modeling.

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 scikit-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 dataframe and dataframe 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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Data Science Certification

189 Learners

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