PG Diploma in Data Science using Python

We Deliver both Classroom & Online Sessions

Eligibility: BE, B.Tech, ME, M.Tech, MCA

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PG Diploma in Data Science using Python is a 4 months job oriented professional course, providing strong knowledge & proper understanding on Data Science.

We, at Cranes, provide training in all the relevant disciplines to enable the engineers to develop Java based application that meets industry standards.

At Cranes, we provide the students with an organized framework to enhance their technical skills & knowledge curve. The sessions are well planned and delivered with examples to make the lectures more interesting and understandable. Our aim is to create more robust knowledge representations in the students' minds.

PG Diploma in Data Science using Python modules:

Data Science using Python training course syllabus structured in-terms of modules to help students for better understanding of the subject. Which are listed below.

  • Python Programming
  • Advanced Python and Unit Testing
  • Data Analysis & Visualization
  • Machine Learning using SKlearn
  • Tableau
  • Cloud Computing

Placement Support:

Guaranteed Placement is available at cranes campus for those who complete the training successfully.

Python Programming

  • Introduction to Python
  • Defining Functions
  • Set and Dictionary
  • Python Data types and Conditions
  • List and Tuple
  • Exception Handling
  • Control Statements
  • List Comprehension
  • File Handling

Advanced Python and Unit Testing

  • Object Oriented Programming
  • Multiprocessing
  • SQLite
  • Overloading Operator
  • Multi threading
  • Testing Fundamentals
  • Inheritance
  • Regular Expression
  • Unit Testing using PyTest

Data Analysis and Visualization

  • NumPy
  • Slicing of Matrices Filtering
  • NumPy Functions across axis
  • Vectorization
  • Array Creation
  • Stacking of arrays
  • Broadcasting
  • Functions
  • Matrix Calculation
  • Pandas Series
  • Pandas Dataframe
  • Working with Categorical Data
  • Sorting DataFrames
  • Creating graphs using Matplotlib
  • Scatter Plot, Line Graph
  • Data Cleaning
  • Selection Data (loc, iloc)
  • Grouping & Aggregation
  • Importing csv files
  • Customizing Plots
  • Bar Graph, Histogram
  • Handling Missing Data
  • Filtering DataFrames
  • Merging DataFrame(concat, merge)
  • Importing Excel Files
  • Saving Plots
  • Subplots

Machine Learning

  • Understand what is Machine Learning
  • Regression
  • Training and Testing Data
  • Classification
  • Decision Tree Classifier
  • Supervised Learning
  • Linear Regression with Single Variable
  • Handling Categorical Data
  • Logistic Regression – Binary classification
  • Support Vector Machine
  • Unsupervised Learning
  • Multiple Linear Regression
  • K-Fold Cross Validation
  • Logistic Regression – Multiclass classification
  • KNN Classifier


  • Tableau Introduction
  • Connect Tableau with Different Data Sources
  • Tableau Calculations using Functions
  • Traditional Visualization vs Tableau
  • Visual Analytics
  • Building Predictive Models
  • Tableau Architecture
  • Parameter Filters
  • Dynamic Dashboards and Stories

Cloud Computing

  • Introduction To Cloud Computing
  • Management Console, Amazon Web Services Or AWS
  • EBS(Elastic Block Storage),VPC
  • Types of Cloud
  • Card Configuration
  • EBS volumes and Snapshots
  • Virtualization
  • Creating S3 Bucket
  • RDS


  • Anaconda Distribtion (Jupyter Notebook)
  • Tableau
  • SQLite
  • Google Colab
  • AWS