Python Certification Training for Data Science

Transform yourself as a Python Maven in the field of Data Science

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Key Highlights

  • 42 hours of Instructor-led Live Sessions
  • Complimentary Self-paced course of ?Python Statistics for Data Science?
  • 14 Weekend sessions, each of 3 hours duration
  • 21 Weekdays sessions, each of 2 hours duration
  • Case Studies on Real-life Scenarios
  • Lifetime Access to Learning Management System
  • Practice Assignments
  • 24X7 Expert Support
  • Online Forum for Discussions

Course Price

$499.00 $900.00

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Available Courses Delivery

This course is available in the following formats:

Course Overview

Python certification training provides a 360-degree overview of Python programming concepts, such as, file operations, data operations, and object-oriented programming. It acquaints learners with Python coding and its application in Data Analytics. It trains learners in Data Manipulation, Machine Learning using Python, Time Series Analysis, Association Rules Mining, Model Selecting and Boosting. The course introduces learners to NumPy, Pandas, and Matplotlib.

Course Objectives

  • Acquaint learners with various tools and technologies for research and visualization
  • Teach learners about various methods to handle different types of data
  • Train about the roles played by a Machine Learning Engineer
  • Make learners adept at Machine Learning Algorithms and their implementations
  • Equip learners with advanced and predictive modeling
  • Acquaint learners with Data Visualization using Python
  • Train about time series, text mining, and sentimental analysis

Career Benefits

  • Multi-industry invitations as a Machine Learning Engineer
  • Opportunity to write Python scripts
  • Options to carry out hands-on data analysis using Python
  • Scope of developing and using Machine Learning Applications
  • Considered as an expert in Data Science industry
  • Higher paycheck
  • Opportunity to work with big brands


  • Basic knowledge of computer programming languages
  • Fundamental understanding of any data analysis tools will be an added advantage
  • Specific knowledge of Python is not compulsory

Who should take up?

  • Big Data Professionals
  • BI Managers
  • Project Managers
  • Software Developers
  • ETL Professionals
  • Analytics Managers
  • Business Analysts
  • Information Architects
  • Python Professionals who want to learn Automatic Predictive Models

Course Content

  • Overview of Python
  • The Companies using Python
  • Different Applications where Python is used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the screen
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda Functions
  • Object-Oriented Concepts
  • Standard Libraries
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways
  • Errors and Exception Handling
  • Handling Multiple Exceptions
  • Basic Functionalities of a data object
  • Merging of Data objects
  • Concatenation of data objects
  • Types of Joins on data objects
  • Exploring a Dataset
  • Analyzing a Dataset
  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent
  • What are Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA
  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization
  • Grid Search vs Random Search
  • Implementation of Support Vector Machine for Classification
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How does K-means algorithm work?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering works?
  • Implementing K-means Clustering
  • Implementing Hierarchical Clustering
  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How does Recommendation Engines work?
  • Collaborative Filtering
  • Content-Based Filtering
  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q ? Learning
  • ? values
  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF
  • What is Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting
  • Python files I/O Functions
  • Numbers
  • Strings and related operations
  • Tuples and related operations
  • Lists and related operations
  • Dictionaries and related operations
  • Sets and related operations
  • NumPy - arrays
  • Operations on arrays
  • Indexing slicing and iterating
  • Reading and writing arrays on files
  • Pandas - data structures & index operations
  • Reading and Writing data from Excel/CSV formats into Pandas
  • matplotlib library
  • Grids, axes, plots
  • Markers, colours, fonts and styling
  • Types of plots - bar graphs, pie charts, histograms
  • Contour plots


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