Description

Are you aware of the buzz words that belong to the field of data science? Here are few of them; business analytics, big data, machine learning, business intelligence & artificial intelligence. If you are aspiring to be a data scientist, you should have a comprehensive knowledge about each of these tech concepts, also about how & where they fit in the realm of data science so that you can identify the appropriate approach to solve a problem.  

This particular course, Data Science with R and Python, is an instructor-led course with a mean batch size of ten students. Within the 90 hours of online-Live training, students will obtain the theoretical & practical knowledge/information in order to acquire the required skills. The trainer’s holistic approach is stemmed to satisfy the long-run wants of the scholar.  They facilitate 100% job/placement post the successful completion of the course & also provide the students with an option to take a demo class before enrolling for the course

What will you learn?

  • This course will help learners gain expertise in skills required to be a Data Scientist
  • Training on programming tools such as R and Python along with real-time hands-on projects.
  • This course would also help to create dashboards and storytelling with Tableau. 

Specifications

  • Free Demo
  • 100% Placement Assistance
  • Learn from Experts
  • Interactive Learning
  • Certification by Institute
  • Instalment Facility
  • Interview Training

Data Science with R and Python

  • Statistical Learning 
  • Measures of central tendency, Measures of dispersion, Probability theory, Hypothesis testing, ANOVA, Types of graphs and plots. 
  • Python Environment Setup and Essentials 
  • Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac. 
  • R Environment Setup and Essentials 
  • Hands-on Exercise – Installing R for the Windows, Linux and Mac, Exploratory data analysis, Basic operators in R, Data Manipulation, Data visualisation. 
  • Python language Basic Constructs 
  • OOP concepts in Python 
  • Hands-on Exercise – important concepts in OOP like polymorphism, inheritance, encapsulation, Python functions, return types, and parameters, Lambda expressions, 
  • NumPy for mathematical computing 
  • Hands-on Exercise – How to import NumPy module, creating array using ND-array, calculating standard deviation on array of numbers, calculating correlation between two variables. 
  • SciPy for scientific computing 
  • Hands-on Exercise – Importing of SciPy, applying the Bayes theorem on the given dataset. 
  • Matplotlib for data visualization 
  • Hands-on Exercise – deploying MatPlotLib for creating Pie, Scatter, Line, Histogram. 
  • Pandas for data analysis and machine learning 
  • Hands-on Exercise – working on importing data files, selecting record by a group, applying filter on top, viewing records, analyzing with linear regression, and creation of time series. 
  • Introduction to Machine Learning with R and Python 
  • The need for Machine Learning, Introduction to Machine Learning, types of Machine Learning, such as supervised, unsupervised and reinforcement learning, why Machine Learning with Python, R and applications of Machine Learning. 
  • Supervised Learning and Linear Regression 
  • Hands-on Exercise – Implementing linear regression from scratch with R and Python, Using Python library Scikit-learn to perform simple linear regression and multiple linear regression, Implementing train–test split and predicting the values on the test set. 
  • Classification and Logistic Regression 
  • Hands-on Exercise – Implementing logistic regression from scratch with R and Python, Using Python library Scikit-learn to perform simple logistic regression and multiple logistic regression, Building a confusion matrix to find out the accuracy, true positive rate, and false-positive rate. 
  • Decision Tree and Random Forest 
  • Hands-on Exercise – Implementing a decision tree from scratch in R and Python, Using Python library Scikit-learn to build a decision tree and a random forest, Visualizing the tree and changing the hyperparameters in the random forest. 
  • Naïve Bayes and Support Vector Machine 
  • Hands-on Exercise – Using Python library Scikit-learn to build a Naïve Bayes classifier and a support vector classifier. 
  • Unsupervised Learning 
  • Hands-on Exercise – Using Python library Scikit-learn to implement K-means clustering, Implementing PCA (principal component analysis) on top of a dataset. 
  • Natural Language Processing and Text Mining 
  • Project 
  • Time Series Analysis 
  • Hands-on Exercise – Analyzing time series data, the sequence of measurements that follow a non-random order to recognize the nature of the phenomenon, and forecasting the future values in the series. 

 

Certification Project

  • The learner will have to submit a certification project and will be rewarded with a certificate once this project is completed. 
  • Multiple Choice Questions & Answers: 
  • Learners will be asked with multiple choices Q&A during the training sessions and points will be provided. 
  • Scenario-based Questions & Answers: 
  • Learners will have to submit scenario-based Q&A and points will be provided. 
  • Sample Project: 
  • A Sample Project will be discussed and shown to the learners that will help learners to start working in a project.

The trainer - Data Science

The trainer with 4+ years of experience in technical training and 2 years in Data Science training. The trainer is predominantly working on Data; be it data analyzing or visualizing.

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Description

Are you aware of the buzz words that belong to the field of data science? Here are few of them; business analytics, big data, machine learning, business intelligence & artificial intelligence. If you are aspiring to be a data scientist, you should have a comprehensive knowledge about each of these tech concepts, also about how & where they fit in the realm of data science so that you can identify the appropriate approach to solve a problem.  

This particular course, Data Science with R and Python, is an instructor-led course with a mean batch size of ten students. Within the 90 hours of online-Live training, students will obtain the theoretical & practical knowledge/information in order to acquire the required skills. The trainer’s holistic approach is stemmed to satisfy the long-run wants of the scholar.  They facilitate 100% job/placement post the successful completion of the course & also provide the students with an option to take a demo class before enrolling for the course

What will you learn?

  • This course will help learners gain expertise in skills required to be a Data Scientist
  • Training on programming tools such as R and Python along with real-time hands-on projects.
  • This course would also help to create dashboards and storytelling with Tableau. 

Specifications

  • Free Demo
  • 100% Placement Assistance
  • Learn from Experts
  • Interactive Learning
  • Certification by Institute
  • Instalment Facility
  • Interview Training
₹45,499 ₹ 92,855

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