Data Science Online Training

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About the Data Science Online Training:

A crucial technology in the data market is data science. The generated data needs to be archived and examined. The information produced reports on the company’s and its business’s current state. These reports are used to inform the judgements. Both structured and unstructured data are kept there.
You will learn how to use R to analyse data in a data science course. Real-world situations and projects will be used to teach this course. It will be possible for you to create a report using the evaluated data. Any company that has the ability to make decisions about its business and clients should take this crucial step.
Due to a lack of data scientists, there is a great need in the IT industry and among multinational corporations. The

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Objectives of Data Science Online Training

  • Discover data acquisition.
  • Master R Language.
  • Recognize complex statistical concepts.
  • machine learning algorithms expertise.
  • Implement data mining and data collection.
  • The Project Deployment Tools: Master them.
  • Study segmentation for analysis and prediction.

KEY FEATURES

100% Job Support
100% Practical Training
Live Project Experience

DATA SCIENCE COURSE CURRICULUM

  • Data Science Overview
  • Data Science
  • Data Scientists
  • Examples of Data Science in day to day life
  • Python for Data Science
  • Introduction to Data Visualization
  • Processes in Data Science
  • Data Wrangling, Data Exploration, and Model Selection
  • Exploratory Data Analysis or EDA
  • Data Visualization
  • Plotting
  • Hypothesis Building and Testing
  •  
  • Introduction to Statistics
  • Statistical and Non-Statistical Analysis
  • Some Common Terms Used in Statistics
  • Data Distribution
  • Methods of Central Tendency
  • Mean, Median, Mode
  • Methods of Dispersion
  • Percentiles, Dispersion
  • Histogram
  • Bell Curve
  • Hypothesis Testing
  • Chi-Square Test
  • Correlation Matrix
  • Inferential Statistics
  • Introduction to Anaconda
  • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
  • Jupyter Notebook Installation
  • Jupyter Notebook Introduction
  • Variable Assignment
  • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
  • Creating, accessing, and slicing tuples
  • Creating, accessing, and slicing lists
  • Creating, viewing, accessing, and modifying dicts
  • Creating and using operations on sets
  • Basic Operators: ‘in’, ‘+’, ‘*’
  • Logical operators
  • Functions
  • Use of break and continue keywords
  • Control Flow
  • Classes
  • Objects
  • Object oriented programming in python (encapsulation, abstraction, inheritance & polymorphism)
  •  
  • NumPy Overview
  • Properties, Purpose, and Types of ndarray
  • Class and Attributes of ndarray Object
  • Basic Operations: Concept and Examples
  • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
  • Copy and Views
  • Universal Functions (ufunc)
  • Shape Manipulation
  • Broadcasting
  • Linear Algebra
  • SciPy and its Characteristics
  • SciPy sub-packages
  • SciPy sub-packages –Integration Optimize
  • Linear Algebra
  • SciPy sub-packages – Statistics
  • SciPy sub-packages – Weave
  • SciPy sub-packages – I O
  • Introduction to Pandas
  • Data Structures
  • Series
  • DataFrame
  • Missing Values
  • Data Operations
  • Data Standardization
  • Pandas File Read and Write Support
  • SQL Operation
  • Introduction to Machine Learning
  • Machine Learning Approach
  • How Supervised and Unsupervised Learning Models Work
  • Scikit-Learn
  • Supervised Learning Models
  • Linear Regression
  • Supervised Learning Models
  • Logistic Regression
  • K Nearest Neighbours (K-NN) Model
  • Unsupervised Learning Models – Clustering
  • Unsupervised Learning Models – Dimensionality Reduction
  • Pipeline
  • Model Persistence
  • Model Evaluation – Metric Functions
  • NLP Overview
  • NLP Approach for Text Data & Environment Setup
  • NLP Sentence analysis & Applications
  • Major NLP Libraries
  • Scikit-Learn Approach
  • Scikit – Learn Approach Built – in Modules
  • Scikit – Learn Approach Feature Extraction
  • Bag of Words
  • Extraction Considerations
  • Scikit – Learn Approach Model Training
  • Scikit – Learn Grid Search and Multiple Parameters
  • Pipeline
  • Introduction to Data Visualization
  • Python Libraries
  • Plots
  • Matplotlib Features:
  • –Line Properties Plot with (x, y)
  • –Controlling Line Patterns and Colors
  • –Set Axis, Labels, and Legend Properties
  • –Alpha and Annotation
  • –Multiple Plots
  • –Subplots
  • Types of Plots and Seaborn
  • Web Scraping
  • Common Data/Page Formats on The Web
  • The Parser
  • Importance of Objects
  • Understanding the Tree
  • Searching the Tree
  • Navigating options
  • Modifying the Tree
  • Parsing Only Part of the Document
  • Printing and Formatting
  • Encoding
  • Need for Integrating Python with Hadoop
  • Big Data Hadoop Architecture
  • MapReduce
  • Apache Spark
  • Resilient Distributed Systems (RDD)
  • PySpark
  • Spark Tools
  • PySpark Integration with Jupyter Notebook
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