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Data Science With Python

About Instructor

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:

  • Overview of Python
  • Installation of Python
  • IDLE User Interface
  • Overview of Pycharm & Jupyter
  • Installation of Pycharm & Jupyter
  • Pycharm & Jupyter User Interface
  • Python 2x Series vs Python 3x Series
  • Identifiers & Keywords
  • Comments in Python
  • Input & Output
  • Arithmetic Operations
  • Precedence and Associativity
  • Range
  • Variable
  • Number
  • String
  • List
  • Tuple
  • Dictionary
  • Membership & Boolean Operator
  • Sets
  • IF Else statement
  • For loop
  • While loop
  • Break Statement
  • Continue Statement
  • Pass Statement
  • Local & Global Variable
  • User Defined Functions
  • Functions with arguments & without arguments
  • Lambda
  • Package and module
  • Errors & Exception Handling
  • File import
  • File export
  • File Append
  • Rename file
  • Delete File
  • File Import & Export
  • Dataframe
  • Columns & Rows Operations
  • Merge Data
  • Data subset
  • Data Aggregation
  • Regular Expression
  • Matplotlib Module
  • Central Tendency (Mean, Median, Mode, Quartile)
  • Range, Variance & Standard Deviation
  • Skewness & Kurtosis
  • Sample vs Population
  • Hypothesis Testing
  • Correlation
  • Regression
  • Supervised Learning vs Unsupervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Clustering
  • Neural Network
  • Text Sentiment Analytics with Twitter Data

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Duration 1 Month (4 Weeks)
Training period 40 hours
Sessions Weekdays/ Weekends
Modules covered 13 modules
Learning method Offline/Online
Price 19,900.00 18% GST

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