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Data Science with Python Course Syllabus

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Our Data Science with Python Course Syllabus gives students an enriching learning experience. By teaching from fundamental to advanced concepts, our Data Science with Python Syllabus with 100% placement support will be able to make students understand Data Science with Python easily. The syllabus covers data manipulation and cleaning using NumPy and Pandas, exploratory data analysis (EDA) techniques, and data visualization with Matplotlib and Seaborn. So, enroll in our  Data Science with Python Training Institute in Chennai to master Data Science with Python and get placed easily with the help of our lifelong placement support.

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Breakdown of Data Science with Python Course Fee and Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
July 2024
Week days
(Mon-Fri)
Online/Offline

2 Hours Real Time Interactive Technical Training 

1 Hour Aptitude 

1 Hour Communication & Soft Skills

(Suitable for Fresh Jobseekers / Non IT to IT transition)

Course Fee
July 2024
Week ends
(Sat-Sun)
Online/Offline

4 Hours Real Time Interactive Technical Training

(Suitable for working IT Professionals)

Course Fee

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Syllabus for The Data Science with Python Course

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Python – Overview

1

  • A brief history of python
  • Application and trends in python
  • Available python versions
Python – Environment Setup

2

  • Getting and installing python
  • Environmental variables and idle
  • Executing python from command line
Fundamentals

3

  • I/o
  • Naming conventions
  • Datatypes:
  • Numbers
  • String
  • List
  • Tuple
  • Dictionary
  • Set
Python Operators

4

  • List, Tuple, Dictionary, Set Methods
  • Statements: If, elif, Break, Continue
  • Loops: For loop, while loop
  • Functions
Oops Concepts:

5

  • Class and objects
  • Getters and setters
  • Properties
  • Inheritance
  • Polymorphism
  • Special Functions of Python: Lambda, Map, Reduce, Filter
Modules in Python:

6

  • Math
  • Arrow
  • Geopy
  • Beautiful soup
  • Numpy
  • Sys
  • Os
Multithreading:

7

  • Introducing threads and life cycles
  • Priorities
  • Dead Locks
Exceptional Handling

8

  • Errors
  • Runtime errors
  • Exceptional model
  • Exceptional hierarchy
  • Handling multiple exception
  • Raise exceptions
File Handling

9

  • Text files
  • Csv files
Regular Expressions

10

  • Simple character matches
  • Flags, quantifers, greedy matches
  • Grouping and matching objects
  • Matching at beginning or end
  • Substituting and splitting a string
  • Compiling regular expressions
Gui Interfacing: Tkinter

11

  • Widgets
  • Integrated application
  • Mysql/with application
  • Converting .exe
Datascience Modules:

12

  • pandas
  • numpy
  • scipy
  • matplotlib
Python Data Processing

13

  • Python Data Operations
  • Python Data cleansing
  • Python Processing CSV Data
  • Python Processing JSON Data
  • Python Processing XLS Data
  • Python Relational databases
  • Python NoSQL Databases
  • Python Date and Time
  • Python Data Wrangling
  • Python Data Aggregation
  • Python Reading HTML Pages
  • Python Processing Unstructured Data
  • Python word tokenization
  • Python Stemming and Lemmatization
Python Data Visualization

14

  • Python Chart Properties
  • Python Chart Styling
  • Python Box Plots
  • Python Heat Maps
  • Python Scatter Plots
  • Python Bubble Charts
  • Python 3D Charts
  • Python Time Series
  • Python Geographical Data
  • Python Graph Data
Statistical Data Analysis

15

  • Python Measuring Central Tendency
  • Python Measuring Variance
  • Python Normal Distribution
  • Python Binomial Distribution
  • Python Poisson Distribution
  • Python Bernoulli Distribution
  • Python P-Value
  • Python Correlation
  • Python Chi-square Test
  • Python Linear Regression

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