Python programming for Data Science for Beginners

Overview

This course is for beginners who want to enter into data science world, this course starts with basics of python programming and slowly move towards data science world introducing the estimation techniques using Python libraries.

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Regular Price: ₹11,999.00

Special Price ₹10,999.00

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Regular Price: ₹11,999.00

Special Price ₹10,999.00

Upcoming Classes

31st Aug 2019
6 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 10999
30th Nov 2019
6 Days
Weekend Online (WebEx) & Offline (Bangalore) Classes - Saturday & Sunday
Time : 11 AM to 6 PM
Price : 10999

Course Description

  • We will start with set of objective questions which will give us idea of participants and also it will encourage participants to learn.
  • Every fundamental of learning is backed by objective questions and hands on excercise.
  • We will last our course with set of multiple choice questions which demonstrate the improvements in participants
  • This is  6 days or 36 hours course.

Course Instructor : Raju Kumar Mishraview more

Objective

Course Description :

 In this course, with basics of Python, participant learn many new libraries. As someone move in this course through, certainly going to gain a new confidence and feeling distinguished from others. The Python Libraries you are going to learn :

 re : Library for Regular Expression in Python

NumPy : A library for array and matrix data operations.

Pandas : A library to manipulate, aggregate and do data preprocessing level analytics.

SciPy : A library which provides to perform many scientific and statistical operations on data.

ElementTree : Deal with XML

JSON : Deal with JSON

 

BeutifulSoup : Deal with HTML

 

Matplotlib : A data visualization library.

Seaborn : A data visualization library on top of Matplotlib to provide appealing charts in less time.

Bokeh :  A web browser based data visualization Python library.

 

Detailed Course Index

 

Python Basic :

  • Introduction to Python
  • Running Python from eclipse
  • IDLE
  • Data type in Python
    • Integer
    • Long
    • Float
    • Complex
    • None
    • Typecasting data
  • Operators in python

Collections  in  python:

  • List
    • Introduction to List
    • List operations
    • List Comprehensions
  • Dictionary
    • Introduction to Dictionary
    • Dictionaries operations
  • Set
    • Introduction to Set
    • Set Operations
  • Tuples
    • Introduction to Tuples
    • Tuples Operations

Conditionals and Looping

  • Introduction to conditionals
  • if
  • if and else
  • if elif and else
  • Introduction to looping
  • for loop
  • while loop
  • break
  • continue
  • pass

Strings:

  • Introduction to Strings
  • String Operations

 

Functions modules and Package:

  • Inbuilt Functions
  • User Defined Functions and its definition
  • return statement
  • Local and global variables
  • Default argument
  • Variable length arguments
  • Anonymous functions
  • Modules in Python
  • Packages in python
  • Writing packages
  • importing packages

 

Exception handling in python :

  • Introduction to exception
  • Raising Exception
  • try and except
  • Different type of exception
  • Multiple except
  •  Role of finally
  • try, except and finally together
  • assert

IO in python

.Reading from and writing to console.

.Reading from a file

.Difference between read() and readLine() function.

.Writing to a file

.Reading and Writing csv (Comma Separated Files)

.Reading and Writing JSON files.

.Reading pdf files

.Reading and writing excel files.

 

Some useful packages in Python

  • os
    • Joining path
    • Creating new directory
    • Absolute and relative path
    • File size
    • Getting content of a folder
  • shutil
    • Copying files and directories
    • Deleting files and directories
    • Renaming files and directories

Class and Objects :

  • Class in Python
  • Constructors
  • Objects
  • Inheritance
  • Namespace

Debugging in Python

 NumPy :

 NumPy is very old data science library, which internals have been written using C for mathematical computation efficiency. This deals with linear algebra problems. 

  • Introduction to NumPy.
  • NumPy data type
  • NumPy ndarray
  • NumPy array and its Creation
  • Merging and stacking , and reshaping of array
  • Splitting array.
  • Indexing slicing
  • Mathematical operations on array.
  • Data aggregation
  • Matrices and its operation in NumPy
  • Linear systems and NumPy
  • File IO in NumPy

 Pandas :

 Data manipulation is inevitable part of data preprocessing. Similarly, data aggregation provides us many insight about the data and helps a lot in model selection. Pandas is a python library which deals with structured and semi-structured data manipulation and aggregation. 

  • Introduction to Pandas.
  • Pandas data structures.
  • Introduction to Pandas series.
  • Operation on Pandas Series.
  • Introduction to Pandas DataFrame.
  • Merging and Joining DataFrame.
  • Operation on DataFrames
    • Indexing and selection.
    • Data filtering
    • Data Mutation
    • Data Aggregation
    • Descriptive Statistics
    • Missing Data Handling
    • Operations on Data structures
  • IO in Pandas
    • CSV files.
    • JSON files
    • Excel Files
    • HTML files
    • Stata
    • SAS
    • Pickle
  • Pandas and Databases.
  • Time series and Pandas

 Python Web Scrapping and  Markup language :

Markup language are prevalent. We are going to discuss three markup language XML, HTML and JSON.  We will discuss three Python libraries ElemntTree, BeutifulSoup and json to deal with these three markup language.

 Python and XML

  • Introduction to XML
  • XML using DOM
  • XML as tree
  • Introduction to ElementTree.
  • Parsing a XML
  • Data  extraction from XML
  • Creation of an XML

Python and HTML

  • Introduction to HTML
  • Element of HTML
  • Introduction to BeutifulSoup.
  • Structure of BeutifulSoup.
  • Parsing a HTML.
  • Element extraction from HTML.
  • HTML creation using BeutifulSoup.

 Python and JSON :

  • Introduction to JSON.
  • Library json.
  • Parsing JSON
  • Creating a JSON structure.

 Data Visualization in Python :

 For Data visualization we are going to use three libraries . The library Seaborn is defined over Matplotlib and remove some boiler plate code and provide more appealing charts.  The third one is Bokeh.  Bokeh results can be embedded in  HTML, Django and Flask. Let me start with course topics of Matplotlib, and thereafter I will enlist the topics of Seaborn and Bokeh.

 Matplotlib :

  • Introduction to Matplotlib.
  • Line plot multiple line plot
  • Scatter plot
  • Bar plot
  • Histograms
  • Box plot
  • Error bars 
  • Contour plot
  • Piplot
  • Different aspects of coloring
  • Violin plots
  • Text plot
  • Word Cloud

 Seaborn :

  • Introduction to Seaborn.
  • Seaborn. Is this a better Matplotlib?
  • Different type of plots in Seaborn.
    • Linear relationship plot.
    • Bar plot
    • Histograms.
    • Density plot.
    • Heatmap
    • Jointplot
    • Factor plot

 Bokeh : 

  • Introduction to Bokeh.
  • Requirement of Bokeh.
  • Architecture of Bokeh and Bokeh Server.
  • Fundamental of Glyphs.
  • Different type of charts in Bokeh :
    • Line Charts
    • Multiline
    • Scatter plots.
    • Bar
    • Histogram
    • Box
    • Function
    • 3D

Story telling with data visualization.

 Probability and Statistics with SciPy :

 Without knowledge of probability and statistics, data science is like food without nutritions. 

  • Randomness and Life.
  • Concept of Random Numbers.
  • Concept of chaos.
  • Introduction to Probability.
  • Introduction to Probability Distributions.
  • Law of Probability
  • Law of Independence.
  • Discrete and Continuous Distributions.
  • Some popular discrete distributions
    • Bernoulli Distribution
    • Geometric Distribution
    • Uniform Distribution
    • Binomial Distribution
    • Poisson Distribution
  • Some common continuous Distributions
    • Uniform
    • Exponential
    • Normal
    • t-Disribution
    • Chi-square Distribution
    • F Distribution
  • Concept of Expectation
  • Concept of Variance
  • Bayes theorem
  • Concept of Population and Samples
  • Parameter estimation
  • Estimation of mean and Variance
  • Introduction to Confidence interval and Hypothesis Testing 
    • z-test
    • t-test
    • chi-square test
    • test for variance
  • Introduction to Non Parametric Tests

Customer Reviews

Shruti

Best course for Python

This is one of the best course of python ,I have come across.It gives in-depth insight on python libraries.The explanation was clear and to the point.The instructor Mr Raju Mishra is very experienced and is top of his subject.
Would definitely recommend to anyone looking to learn Python.

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