PG Diploma in Data Science and Machine Learning

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

Intermediate

Overview

Data Science and Machine Learning Course

Description

The PG Diploma in Data Science Online Course and Machine Learning is a five-month professional program that provides in-depth data science knowledge and expertise.

Cranes mentors engineers in all critical disciplines to assist them in excelling at designing Data Science based applications that meet industry standards.

Cranes provide students with a structured framework to help them develop technical skills and knowledge. The lectures are well-planned and delivered with examples to make them more interesting and understandable. We want to help students develop a much broader range of mental representations of knowledge.

Cranes Varsity is considered as the best Data Science Course (Available Online) which offers services from training to placement as part of the Data Science training program with over 400+ participants placed in various multinational companies including Genpact, Ernst & Young, Capgemini, Vodafone, CGI, Wipro, Tata Elxsi, IBM, Lumen Technologies, Tech Mahindra, Birla Soft, HTC, Happiest Minds, Western Digital, Mearsk Global, Koireader, K7 Computing, Mphasis, Atos, Latent View, etc.

We offer the highest quality teaching, assessment and placement support through our Data Science course. The course is designed to make a novice into an expert from developing Python programming, writing queries on SQL to building Machine learning & Deep Learning Models and Cloud computing. Our Lead mentors are industry experts and have been associated with us for decades.

If you’re looking toward building your career in Data Science and are interested in getting the Data Science Training with Certification & Placement, then Cranes Varsity is the right destination for realizing your aspirations and growing on your Career ladder.

Data Science using Python training course syllabus is classified into modules that help students better understand the subject. Which are listed below:

Course Modules

Generic
  • 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
Projects
  • Apply statistical methods to make decisions in various business problems, including bank, stock markets, etc.
  • Apply regression to predict future flight price
  • Apply classification to classify customer
  • Use clustering to cluster banking customers
  • Computer vision projects like Face recognition, Image Quality Improvement, etc.
Platform
  • Anaconda Distribution Jupyter, Spyder
  • Tableau
  • Google Colab

Course Content

Generic:

  • Introduction to Python
  • Python Functions
  • Scope of Variables
  • List and Tuple
  • Map and filter functions
  • Set and Dictionary
  • Python Data types and Conditions
  • Default arguments
  • Global specifier
  • List Methods
  • String
  • Exception Handling
  • Control Statements
  • Functions with variable number of  args
  • Working with multiple files
  • List Comprehension
  • List comprehension with conditionals
  • File Handling

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

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

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

Data Science Specialization:

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

  • Optimizing regression models with forward elimination, grid search cv
  • Regularization L1 and L2 regularization
  • Improving classification models with Ensemble modeling
  • Random Forest, Bagging
  • Boosting techniques,: ADA boost, Gradient Boost, XG boost
  • Dimensionality Reduction with PCA

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

  • Tableau Introduction
  • Working with sets
  • Connect Tableau with Different Data Sources
  • Cards in Tableau
  • Tableau Calculations using Functions
  • Traditional Visualization vs Tableau
  • Creating Groups
  • Visual Analytics
  • Charts, Dash-board
  • Building Predictive Models
  • Tableau Architecture
  • Data types in Tableau
  • Parameter Filters
  • Joins and Data Blending
  • Dynamic Dashboards and Stories

  • Text cleaning, tokenization, lemmatization
  • Word2Vec, Genism
  • Understanding of Artificial Neural Network
  • Transformers
  • Sentimental Analysis
  • Solve machine learning use case
  • word embedding’s
  • BERT
  • bag of words, TF-IDF, unigrams, bigrams
  • Understanding of Artificial Neural Network
  • Bidirectional LSTM – Encoders and Decoders

Placement Statistics

FAQs

Yes, Certification of completion will be awarded after successful completion of all modules and clearing modules tests.

Yes, Data science requires coding, required coding concepts will be covered during the course time, Python programming is used for the same.

Yes, Data Science is in very high demand with an increase in data collection by every organization, and it is also one of the highest-paying jobs you can expect.

Graduates from Science, all Engineering streams, Mtech, and MCA can apply for this course.

Cranes curriculum provides Masters equivalent teaching content along with the projects. But it is not a master’s degree certificate.

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Duration: 5 months
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