Internship in Data Science and Machine Learning

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



Data Science Internship


Data science became the most in-demand skill-set of the 21st century due to the increased amount of data generated by the online users and collecting same by most the companies, as data collected by these companies has to be utilized effectively to scale up the business, the need fora skilled data scientist is very high. The internship program in data science by cranes varsity provides the interns with a varied skill-set for one to master him/her self in the domain of data science.

During the internship program, the interns will get good exposure to Python programming concepts, Machine learning techniques and will also learn about the Project life cycle of data science. These skill-sets are learned to enable our interns to stand out during the interview process and can expect better job opportunities. Data Science is a very popular field and there are a ton of companies looking for people with this skill set. To give you just one example, we have over 700 open positions right now on our own platform, and that’s just one company! 

Data Science Course Modules:

Data Science using Python training course syllabus is classified into modules that help students better understand 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 the cranes’ campus for those who complete the training successfully.

Course Content:

  • Object Oriented Programming
  • Overloading Operator
  • Inheritance
  • Multiprocessing
  • Multi-threading
  • Creating Thread
  • Argument Passing to thread
  • Sharing data between threads
  • Race Condition
  • Regular Expression
  • Finding Patterns of Text
  • Meta characters
  • Testing Fundamentals
  • Unit Testing
  • Working with JSON

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

  • Understand what is Machine Learning
  • Regression
  • Naive Bayed Classification
  • Supervised machine learning
  • Simple linear regression  Decision tress and its types
  • Unsupervised machine learning   Multiple linear regression
  • K Nearest Neighbour Classification
  • Data Preprocessing
  • Performance measure for regression
  • Performance Measure for Classification
  • Handling missing data
  • MSE, R-Squared, MAE, SSE
  • Accuracy, Recall, Precision,
  • Fmeasure
  • Onehot Encoding
  • Feature selection for Regression
  • Clustering and types
  • Label encoding   ML Workflow for project implementation
  • Kmeans Clustering
  • Ordinal, frequency encoding
  • Classification
  • Evaluate clustering results, Elbow Plot
  • Standardization and normalization
  • Various types of classification
  • Hierarchical clustering
  • Train test split the data
  • Binomial and Bayes theorem
  • K fold cross validation
  • Logistic regression

  • What is Deep Learning
  • Performance measure for ANN
  • Building project based on CNN
  • Deep Learning Methods  Need for Hardware’s in Deep learning
  • Need for Data augmentation
  • Deep Learning Application
  • Basics of image processing
  • Batch Normalization, dropout
  • Artificial Neural Network
  • Opencv library
  • Object detection with CNN
  • Hidden Layers
  • Image reading, writing, enhancement
  • Object recognition with CNN
  • Activation Function
  • Edge detection, filtering, morphology
  • Transfer Learning
  • Forward and Backward propagation
  • CNN for computer vision
  • Restnet, ImageNet.
  • Deep Learning Libraries  CNN architecture
  • Introduction RNN, LSTM
  • Tensor flow, pytorch, Keras
  • Various types of CNN
  • Project Implementation With CNN


  • Predicting Home Prices
  • Predicting Credit Card Approvals

Placement Statistics


Yes, Cranes Varsity training is available through online


Our Online training is Instructor-Led live online sessions

Yes, we will provide training course material for each module

Yes, we offer weekend classes as well evening classes.



Duration: 5 months
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