POP in Data Science and Machine Learning

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



Data Science Course with Placement Guarantee


Cranes Varsity offers a Data Science Course With Placement Guarantee. You will learn how to use Python, SQL, and R for data science. This course will teach you how to analyze and interpret data, create models and develop machine learning algorithms for big data.

POP in Data Science is designed to cater to graduate engineering students, students in their final year, and also to the Working professionals from any domain.

Data Science course is split into several modules, learners will go through these modules stage by stage with regular assessments. Modules covered include basic and advanced, Python programming, Database management with SQL, statistics and mathematics, Data analysis using TABLEAU, Machine Learning, Deep learning, and very popular Natural Language processing, and also the course includes several capstone projects.

Candidates completing the data science course will get placement opportunities from various industries, such as Information Technology, Automotive companies, Banking, Professional consulting firms, healthcare companies, and many more.

Our Data Science Course is designed by industry experts who have helped thousands of people like you land the job they deserve. You won’t find a more effective or affordable way to start your career in data science than with us!

Course Modules


  • RDBMS using MySQL
  • Python for Data Science
  • Advanced Python (Testing and Web Scraping)
  • Exploratory Data Analysis using Pandas

Data Science Specialization

  • Mathematics and Statistics for Data Science
  • Machine Learning using sklearn
  • Machine Learning model Improvement
  • Deep Learning using Tensor Flow
  • Data Analysis and Visualization using Tableau
  • Natural Language Processing


  • Sales forecasting with Machine Learning
  • Stock market predictions with LSTM
  • Automatic Number plate recognition using opencv
  • Image deblurring using Deep Learning


  • Anaconda Distribution Jupyter, Spyder
  • Tableau, Google Colab

Course Content


  • Relational Operators
  • if…else statement
  • if…elif…else statement
  • Logical operators
  • While Loops
  • break and continue statement
  • Loops with else statement
  • pass statement
  • Python for loop
  • Range Function

  • Creating List
  • Accessing elements from List
  • Inserting and Deleting Elements from List
  • List Slicing
  • Joining two lists
  • Repeating sequence
  • Nested List
  • Built-in List Methods and Functions
  • Searching elements in List
  • Sorting elements of List
  • Implementing Stack using List
  • Implementing Queue using List
  • Shallow and Deep copy
  • List Comprehensions
  • Conditionals on Comprehensions

  • Defining Functions in Python
  • Function Argument
  • Single Parameter Functions
  • Function Returning single Values
  • Functions with multiple parameter
  • Function that return Multiple Values
  • Functions with Default arguments
  • Named arguments
  • Scope and Lifetime of Variables
  • global specifier
  • Functional programming    tools:     map(), reduce() and filter()
  • Lambda: short Anonymous functions
  • Creating and importing modules
  • Programming Examples & Assignments
  • Recursion

  • Python Set
  • Creating Set
  • Adding/Removing elements to/from set
  • Python Set Operations : Union, Intersection, Difference and Symmetric Difference
  • Python Tuple
  • Creating Tuple
  • Understanding Difference between Tuple and List
  • Accessing Elements in Tuple
  • Python Dictionary
  • Creating Dictionary
  • Accessing / Changing / Deleting Elements in Dictionary
  • Built-in Dictionary Methods and Functions

  • Understand Exception
  • Handling exception
  • try and except blocks
  • multiple except blocks for a single try block
  • finally block
  • Raising exceptions using raise

  • Introduction to File handling
  • File opening modes
  • Reading data from file
  • Writing data to file

  • Creating Class
  • Creating Objects
  • Method Invocation
  • Understanding special methods
  •    init     method
  •    del     method
  •    str     method
  • Operator Overloading
  • Overloading arithmetic operators
  • Overloading relational operators
  • Inheritance

  • NumPy
  • Vectorized
  • Operation
  • Subsetting
  • Matrix Calculation

  • Pandas Series
  • Pandas Dataframe
  • Importing Data

  • Data Cleaning
  • Handling Missing Data

  • Creating graphs using Matplotlib
  • Customizing Plots
  • Saving Plots

  • Understand what is Machine Learning
  • Supervised Learning
  • Unsupervised Learning

  • Regression
  • Linear Regression with Single Variable
  • Multiple Linear Regression

  • Training and Testing Data
  • Handling Categorical Data
  • K-Fold Cross Validation

  • Classification
  • Logistic Regression – Binary classification
  • Logistic Regression – Multiclass classification

  • Decision Tree Classifier
  • Support Vector Machine
  • KNN Classifier

Placement Statistics


The course is split into sub-modules, students have to complete each module test, and also mock and placement tests will be conducted frequently.

Data Science and Machine Learning Course Program are available in both Online and offline modes, students can choose depending on their convenience.

Yes, Machine Learning engineers are in huge demand in almost every industry, having certification will get you higher opportunities in this highly in-demand and high-paying domain.



Duration: 5 months (At Cranes Varsity) 240hrs (At College Premises)
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