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Data Science with Python


Data Science with Python Course

Data Science deals with structured and unstructured data. In principle, everything that relates to data cleansing, preparation and analysis lies within the scope of Data Science.

Using Python for Data Science

Python is a general use, high level programming language which is considered to be powerful, fast, friendly, open and ease to learn. Python was initiated in 1980s and was named on the comedy group, Monty Python. Only in last few years, Python is extensively being used for Data Science.

Python is a general purpose language that is easy and intuitive. This gives it a relatively flat learning curve, and it increases the speed at which you can write a program. In short, you need less time to code and you have more time to play around with it!

Python testing framework is a built-in, low-barrier-to-entry testing framework that encourages good test coverage. This guarantees your code is reusable and dependable. Bank of America and Facebook are some of the renowned names that use Python for Data Science.


Following forms part of our curriculum for the course, Data Science with Python:

Introduction of Python-Module 1

  • Overview of Python
  • Installation of Python
  • IDLE User Interface

Introduction of Python’s IDE [Pycharm & Jupyter]-Module 2

  • Overview of Pycharm & Jupyter
  • Installation of Pycharm & Jupyter
  • Pycharm & Jupyter User Interface

Basic of Python-Module 3

  • Python 2x Series vs Python 3x Series
  • Identifiers & Keywords
  • Comments in Python
  • Input & Output
  • Arithmetic Operations
  • Precedence and Associativity
  • Range

Datatypes & Variable-Module 4

  • Variable
  • Number
  • String
  • List
  • Tuple
  • Dictionary
  • Membership & Boolean Operator
  • Sets

Control Structure-Module 5

  • IF Else statement
  • For loop
  • While loop
  • Break Statement
  • Continue Statement
  • Pass Statement

Functions-Module 6

  • Local & Global Variable
  • User Defined Functions
  • Functions with arguments & without arguments
  • Lambda
  • Package and module
  • Errors & Exception Handling

File Handling-Module 7

  • File import
  • File export
  • File Append
  • Rename file
  • Delete File

Pandas Module-Module 8

  • File Import & Export
  • Dataframe
  • Columns & Rows Operations
  • Merge Data
  • Data subset
  • Data Aggregation

Text Manipulation-Module 9

  • Regular Expression

Array Manipulation-Module 10

  • Numpy Module

Data Visualization-Module 11

  • Matplotlib Module

Statistics-Module 12

  • Central Tendency (Mean, Median, Mode, Quartile)
  • Range, Variance & Standard Deviation
  • Skewness & Kurtosis
  • Sample vs Population
  • Hypothesis Testing
  • Correlation
  • Regression
  • Supervised Learning vs Unsupervised Learning

Machine Learning Algorithm-Module 13

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Clustering
  • Neural Network
  • Text Sentiment Analytics with Twitter Data


  • Course Duration: : 1 Month (4 Weeks)
  • Approximate training period: 40 hours
  • Fees: INR 19,900
  • Sessions: Weekdays/ Weekends
  • Number of modules covered: 13 modules
  • Learning method: Offline/Online

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