** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka Session on Data Science Tools will help you understand the best tools to get you started with Data Science. Here’s a list of topics that are covered in this session:
Introduction To Data Science
Data Science Tools
Data Science Tools For Data Storage
Data Science Tools For Data Manipulation
Data Science Tools For EDA
Data Science Tools For Data Visualization
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Top 8 Data Science Tools | Open Source Tools for Data Scientists | Edureka
1.
2. INTRODUCTION TO DATA SCIENCE
DATA SCIENCE TOOLS
DATA SCIENCE TOOLS FOR DATA MANIPULATION
DATA SCIENCE TOOLS FOR EDA
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DATA SCIENCE TOOLS FOR DATA STORAGE
DATA SCIENCE TOOLS FOR DATA VISUALIZATION
4. Introduction To Data Science
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Data Science is the process of extracting knowledge and insights from data by
using scientific methods.
Data Science involves collecting, analysing and modelling data to solve real-world problems. It is
used for fraud detection, disease detection, recommendation engines and so on.
6. Data Science Tools come with pre-defined functions, algorithms, and a very user-friendly GUI.
Hence, they can be used to build convoluted Machine Learning models without the use of a
programming language.
DATA SCIENCE TOOLS
Data Science
Data Collection
Exploratory Data Analysis
Data Modelling
Data Visualization
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8. Scale and manage massive
amounts of data
Hadoop Distributed File System
(HDFS) for data storage
Integrate with , Hadoop
MapReduce, Hadoop YARN
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9. Data processing via Apache
Hadoop and Spark clusters
The default storage system is
Windows Azure Blob
Provides Microsoft R Server
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11. Data Integration tool based on
Extract Transform Load architecture
Extract Transform Load tool
to manage data
Support for distributed processing, grid
computing, adaptive load balancing.
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12. Data processing, building
Machine Learning models, etc
Support for integrating Hadoop
framework
Generate predictive models
through automated modelling
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14. Easy to apply Machine Learning
Supports GLM, Boosting ML models
& Deep Learning
Support to integrate with Apache
Hadoop
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15. Supports parallel programming to
perform data analysis, data
modelling, etc
Tests and trains Machine Learning
models at lightning fast speed
Makes model evaluation much
easier.
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17. Can visualize massive data sets to find
correlations and patterns
Create customized reports and
dashboards
Support to integrate with Apache
Hadoop
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18. Clear & concise visualizations
Supports in-memory data
processing
Automatically generates data
associations
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