Python is no-doubt the best-suited language for a Data Scientist. In this ppt different python libraries for data science are covered.

2. Outline
 What is Data Science?
 Why Data Science is required?
 Why Python for Data Science?
 Advantages & Disadvantages of Python
 Python Libraries for Data Science

3. WHAT IS DATA SCIENCE ?
Data Science
Computer Science
+
Mathematics/statistics
+
Visualization

4. WHY DATA SCIENCE ?
Data is generated from different
sources like :-
• Financial logs
• Text files
• Multimedia forms
 Audio file
 Video file
• Sensors
• Instruments
Total
Data
Stored

5. WHY DATA SCIENCE ?

6. DATA SCIENCE PROCESS

7. WHO IS A DATA SCIENTIST ?

8. WHY PYTHON FOR DATA SCIENCE ?
 Interpreted
 Intuitive and minimalistic code
 Expressive language
 Dynamically typed
 Automatic memory management

9. WHY PYTHON FOR DATA SCIENCE ?
 Advantages
 Ease of programming
 Minimizes the time to develop and maintain code
 Modular and object-oriented
 Large community of users
 A large standard and user-contributed library
 Disadvantages
 Interpreted and therefore slower than compiled languages
 Decentralized with packages

10. CODE PERFORMANCE VS DEVELOPMENT TIME

11. PYTHON LIBRARIES FOR DATA SCIENCE
Some Popular Python Libraries are : -
• NumPy
• SciPy
• Pandas
• Scikit-Learn
Visualization Libraries
• Matplotlib
• Seaborn
All these libraries are
installed on the SCC

12. PYTHON LIBRARIES FOR DATA SCIENCE
NumPy :
 Introduces objects for multidimensional arrays and matrices, as well as functions that
allow to easily perform advanced mathematical and statistical operations on those
objects.
 Provides vectorization of mathematical operations on arrays and matrices which
significantly improves the performance.
 Many other python libraries are built on NumPy.
Link: http://www.numpy.org/

13. PYTHON LIBRARIES FOR DATA SCIENCE
SciPy :
 Collection of algorithms for linear algebra, differential equations, numerical integration,
optimization, statistics and more
 Part of SciPy Stack
 Built on NumPy
Link: https://www.scipy.org/scipylib/

14. PYTHON LIBRARIES FOR DATA SCIENCE
PANDAS :
 Panel Data System
 Pandas is an open source, BSD-licensed library.
 High-performance, easy-to-use data structures.
 Provides data analysis and data manipulation tools ( reshaping, merging, sorting, slicing,
aggregation etc.)
 Allows handling missing data.
Link: http://pandas.pydata.org/

15. PYTHON LIBRARIES FOR DATA SCIENCE
SciKit-Learn :
 Provides machine learning algorithms: classification, regression, clustering, model validation
etc.
 Built on NumPy, SciPy and matplotlib
Link: http://skikit-learn.org/

16. PYTHON LIBRARIES FOR DATA SCIENCE
 Python 2D plotting library which produces publication quality figures in a variety of
hardcopy formats
 A set of functionalities similar to those of MATLAB
 Line plots, scatter plots, BarCharts, histograms, pie charts etc.
 Relatively low-level; some effort needed to create advanced visualization
MATPLOTLIB :
Link: https://matplotlib.org/

17. PYTHON LIBRARIES FOR DATA SCIENCE
 Based on matplotlib
 Provides high level interface for drawing attractive statistical graphics
 Similar (in style) to the popular ggplot2 library in R
SEABORN :
Link: https://seaborn.pydata.org/

18. LOADING PYTHON LIBRARIES
In [ ]:
#Import Python Libraries
• import numpy as np
• import scipy as sp
• import pandas as pd
• import matplotlib as mpl
• import seaborn as sns

19. Thank You