POP in Data Science and Machine Learning

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

Intermediate

Overview

Data Science Course with Placement Guarantee

Description

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!

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

Data Science Course With 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
  • Regular Expression
  • Multiprocessing
  • Multithreading
  • Testing Fundamentals
  • Unit Testing

  • NumPy
  • Slicing of Matrices Filtering
  • NumPy Functions across the 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

  • 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
  • Multiple Linear Regression
  • K-Fold Cross-Validation
  • Logistic Regression – Multiclass classification
  • KNN Classifier
  • Unsupervised Learning
  • K-Means Clustering

  • 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

  • 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 Distribution (Jupyter Notebook)
  • Tableau
  • Google Colab
  • AWS

Projects

  • Predicting Home Prices
  • Predicting Credit Card Approvals

Placement Statistics

FAQs

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.

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Duration: 5 months
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