This 4-week course on "Python for Data Science" taught the basics of Python programming and libraries for data science. It covered topics like data types, sequence data, Pandas dataframes, data visualization with Matplotlib and Seaborn. Technologies taught included Spyder IDE, NumPy, Jupyter Notebook, Pandas and visualization libraries. The course aimed to equip participants with Python skills for solving data science problems. It examined applications of data science in domains like e-commerce, machine learning, medical diagnosis and more.

1. PYTHON FOR DATA
SCIENCE
• PRESENTED T0: DR. AMITA MALIK
• PRESENTED BY: ABHISHEK

2. INTRODUCTION
• The course ‘Python for Data Science’ was organised by Indian Institute of
Technology, Madras
on NPTEL website.
• This ‘Python for Data Science’ course will help you learn the basics of Python
along with
different steps of data science such as data preprocessing, data visualization,
statistics,
data visualization, data manipulation, etc.
• The course main objective at equipping participants to be able to use python
programming &
python libraries for solving data science problems.
• The Course was started in the first week of August 2022, it was a 4-week
course with certificate
examination which will be organize on 25th Sept. 2022.

3. COURSE OVERVIEW
The course was divided into 4 weeks:-
• Week 1: Basics of Python Spyder Tool
o Introduction to Spyder.
o Setting working Directory
o Variable Creation
o Data Types And Associated Operations
• Week 2: Sequence Data Types And Associated Operations
o Strings
o Lists
o Arrays
o Tuples
o Dictionary
o Range
o Sets

4. • Week 3: Pandas Data frame & Data frame Related Operations On Toyota
Corolla Dataset
o Reading Files
o Exploratory Data Analysis
o Data Pre processing And Preparation
• Week 4: Data Visualization Using Matplotlib & Seaborn Libraries
o Scatter Plot
o Bar Plot
o Histogram
o Box Plot
COURSE OVERVIEW

5. Technologies Learnt
• Spyder IDE
• NumPy
• Jupyter Notebook
• Pandas Dataframes
• Matplotlib
• Seaborn

6. DATA SCIENCE
• Data Science is the field of applying advanced analytics techniques and
scientific principles to extract valuable information from data for business
decision-making, strategic planning and other uses like data visualization,
data manipulation, etc.
• It is the analysis and extracting of meaningful insights from raw data.
• Data science is used in many industries to make better decisions like, better
hiring decisions, better purchasing decisions.
• Need of Data Science:
o Data Analysis
o Data Mining
o E-commerce
o Machine Learning
o Pattern Recognition
o Logistics
o Sports
o Medical Diagnosis

7. SPYDER (Integrated Development
Environment)
• Spyder is an open-source cross-platform integrated development
environment (IDE) for scientific programming in the Python language.
• Spyder integrates with a number of prominent packages in the scientific
Python stack, including NumPy, SciPy, Matplotlib, pandas, IPython, SymPy
and Cython, as well as other open-source software.
• Powerful Python IDE with advanced editing, interactive testing, debugging
& introspection features.

8. JupyterLab: A Next-Generation Notebook
Interface
• JupyterLab is the latest web-based interactive development environment
for notebooks, code, and data.
• Web-based, interactive computing notebook environment.
• Edit & run human-readable docs while describing the data analysis.
• Its flexible interface allows users to configure and arrange workflows in
data science, scientific computing, computational journalism, and
machine learning

9. SEQUENCE DATA TYPES
• Sequences Allows you to store multiple values in an organised &
efficient manner.
• These offer unique functionalities for the variables to contain &
handle more than one data type at a time.

10. PANDAS DATAFRAME
• Pandas is a library which is used to deals with the Dataframes and to
manipulate data.
• It provides high performance, easy to use data structure and analysis
tools for python programming language.

11. Data
Visualization
• Data Visualization is the graphical representation
of information and data.
• It allows us to quickly interpret the data and adjust
different variables to their affect.
Why Visualize Data ?
• Recognize emerging trends.
• Better understanding.
• Easy Interpretation.
• Helps in Decision Making.
• Observe The Patterns.

12. Matplotlib
• Matplotlib is a 2D plotting Library which produces good quality
figures.
• A cross-platform, data visualization and graphical plotting
library for python.
• Matplotlib is built on the top of NumPy arrays & consist of
several plots like,
Histograms, Bar chart, Line chart.
Plots Used in Matplotlib:
• Scatter Plot.
• Histogram.
• Bar Plot.
• Image Plot.
• 3-D Plot.
• Line Plot.
• Polar Plot..

13. Seaborn
• Seaborn is a python data visualization library based on matplotlib.
• Provides high-level interface for drawing attractive and statistical graphics.
• Provides beautiful default styles and colour palettes to make statistical plots more
attractive.
• Seaborn is built on the top of the matplotlib library and closely integrated to the data
structures from pandas.
Plots Used in Seaborn:
• Scatter Plot.
• Histogram.
• Bar Plot.
• Box & Whiskers Plot.

14. Thank You
!