Data Science with Python

Python is a powerful high-level, object-oriented programming language. It has wide range of applications from Web development...


Content Provider

40 hrs



Mode Of Delivery

Valid for 6 months post activation

Course Validity




Certification By

This is a paid course.

Course Fee

  • 1,800/-/- 599/-/-


Python is a powerful high-level, object-oriented programming language. It has wide range of applications from Web development, scientific and mathematical computing. The syntax of the language is clean and length of the code is relatively short. It allows you to think about the problem rather than focusing on the syntax. SQLite is one free lightweight database commonly used by Python programmers to store data. Many highly trafficked websites, such as YouTube, are created using Python.


Module 1: 

Basic concepts in python, Calculations in python, Variable assignment, Function, Conditions, Data structures - List, Dictionaries, Numpy array, Slicing, Splicing, Subsetting, Functions, Conditions, Loops, Keys, Values, Datatypes.

Module 2: 

Statistics / Plotting - Seaborn vs Matplotlib, Univariate analysis - Import from csv, Plot histograms, Distribution, Mean, Data with same mean but different standard deviation, Data with same mean and standard deviation but different kurtosis, Bootstrapping and subsetting - making samples, Mean of sample, Central limit theorem, Plotting, Hypothesis testing, Bivariate analysis- correlation, Scatter plots, Stratified samples, Categorical , Class variable.

Module 3: 

Series - Datatypes, Index, Data frame - series to data frame, Reindex, Grouping, Pandas shortcuts, Reading from different sources, Missing data treatment, Merge, Join, Writing to file, Database operations.

Module 4: 

Regression- Data Aggregation, Filtering, Lamda functions, Map, Filter, Visualization, Matplotlib, Pyplot, Scatterplot, Histogram, Heatmaps. 

Regression – Linear, Lasso, Ridge, Variable selection, Forward & Backward regression, Polynomial regression.

Module 5:

 Logistics regression, Naïve Bayes.


Unsupervised learning, Distance concepts, Classification, k-nearest, Clustering, k-means, Multidimensional scaling.


Decision Trees, Random Forest, Boosted Trees, Gradient Boosting. 

Learning Outcomes

  • Understand the core programming concepts of Python Programming Language.
  • Know the Looping and condition statements in Python Programming Language
  • Understand the different options in Data Management in Python Programming Language.
  • Understand the importance of data transformation and its need in Python Programming Language
  • Know elementary to advanced statistical methods in Python Programming environment.

Who Should Attend?

  • Engineering and IT students
  • Graduates with a programming background

Job Prospects

  • Software Engineer
  • Data Science Analyst
  • Python Developer
  • Spark Programming
  • Application Engineer
  • Data Science 


After completing this course and successfully passing the certification examination, the student will be awarded the “Data Science with Python” certification.

If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'.