Data Science

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Course Description

Data Science

What is data science? Data science is the field of study on the data available on domain expertise, programming skills and knowledge of mathematics and statistics to draw meaningful and useful insights from the data. Data Science is a mix of various tools, algorithms and machine learning principles with an objective to find hidden patterns from the raw data. 

As the era of big data has emerged and demand has gone up for its storage. It became a major challenge and concern for the industry till a decade ago. A focus is laid on building a framework and finding solutions to store data. With the emergence of a key tool like Hadoop and other frameworks, the storage of data has been resolved to the maximum extent and now the focus of the industry migrated into the processing of the data. Undoubtedly, Data Science is the future of Artificial Intelligence (AI). Hence, it attained significance to understand what Data Science is. The industry thinks how the data can be used and it can be made a value-added service to the enterprise.

The course provides fundamentals of data preparation, predictive modelling, data science and the development. Various models of maintenance will be introduced in a corporate environment following a tested project methodology. The candidates will be trained in fundamental challenges, understanding the business problem and identifying the appropriate analytical approach to tackle the issues. The training includes data preparation, selecting variables and data encoding. A wide range of algorithms includes decision trees, regression, neuro networks, basket analysis and simulation.  

Version IT offers best training in Data Science 

Data science training course involves ‘learning by doing’ at Version IT. We ensure that the candidates can accelerate their career with Data Science certification and the course is offered with a perfect mix of theory, case studies and final touch projects. Version IT has designed the course curriculum in such a way that as it will transform even naive students as corporate professionals. Version IT training institute is considered to be the best Data Science training at Ameerpet in Hyderabad. With extensive hands-on experience in Data Science training in Hyderabad is given to the classroom learners as well as online training. We follow 3P formula where the trainees will be assured Placement, Preparation and Process. Version IT has gain popularity as the best Data Science institute in Ameerpet of Hyderabad as we closely follow and monitor the growth of individual students as well as corporate trainees and assist them during the training period to excel in the subject and improve thorough knowledge. 

Introduction to Recommender Systems 

  • Technical requirements 
  • What is a recommender system? 
  • The prediction problem 
  • The ranking problem 
  • Types of recommender systems 
  • Collaborative filtering 
  • User-based filtering 
  • Item-based filtering 
  • Shortcomings 
  • Content-based systems 
  • Knowledge-based recommenders 
  • Hybrid recommenders 
  • Summary

Manipulating Data with the Pandas Library 

  • Technical requirements 
  • Setting up the environment 
  • The Pandas library 
  • The Pandas DataFrame 
  • The Pandas Series 
  • Summary 

Building an IMDB Top 250 Clone with Pandas 

  • Technical requirements 
  • The simple recommender 
  • The metric 
  • The prerequisties 
  • Calculating the score 
  • Sorting and output 
  • The knowledge-based recommender 
  • Genres 
  • The build_chart function 
  • Summary 

Building Content-Based Recommenders 

  • Technical requirements 
  • Exporting the clean DataFrame 
  • Document vectors 
  • CountVectorizer 
  • TF-IDF Vectorizer 
  • The cosine similarity score 
  • Plot description-based recommender 
  • Preparing the data 
  • Creating the TF-IDF matrix 
  • Computing the cosine similarity score 
  • Building the recommender function 
  • Metadata-based recommender 
  • Preparing the data 
  • The keywords and credits datasets 
  • Wrangling keywords, cast, and crew 
  • Creating the metadata soup 
  • Generating the recommendations 
  • Suggestions for improvements 
  • Summary 

Data Mining Techniques 

  • Problem statement 
  • Similarity measures 
  • Euclidean distance 
  • Pearson correlation 
  • Cosine similarity 
  • Clustering 
  • k-means clustering 
  • Choosing k 
  • Other clustering algorithms 
  • Dimensionality reduction 
  • Principal component analysis 
  • Other dimensionality reduction techniques 
  • Linear-discriminant analysis 
  • Singular value decomposition 
  • Supervised learning 
  • k-nearest neighbors 
  • Classification 
  • Regression 
  • Support vector machines 
  • Decision trees 
  • Ensembling 
  • Bagging and random forests 
  • Boosting 
  • Evaluation metrics 
  • Accuracy 
  • Root mean square error 
  • Binary classification metrics 
  • Precision 
  • Recall 
  • F1 score 
  • Summary 

Building Collaborative Filters 

  • Technical requirements 
  • The framework 
  • The MovieLens dataset 
  • Downloading the dataset 
  • Exploring the data 
  • Training and test data 
  • Evaluation 
  • User-based collaborative filtering 
  • Mean 
  • Weighted mean 
  • User demographics 
  • Item-based collaborative filtering 
  • Model-based approaches 
  • Clustering 
  • Supervised learning and dimensionality reduction 
  • Singular-value decomposition 
  • Summary 

Hybrid Recommenders 

  • Technical requirements 
  • Introduction 
  • Case study – Building a hybrid model 
  • Summary 

About Instructor

  • admin


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