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Data Science is changing the way Business Intelligence works and is taking it one step further. Learn Data Science Online Training and it will teach you the important tasks such as **Data Analytics, Data Acquisition, Data Analysis, R Language etc**.

Data Science is an important technology in the field of data market. The data created must be stored and analyzed. The data created reports on the current situation of the company and its business. The decisions are made based on these reports. It stores both structured and unstructured data.

Data Science Course will train you to analyze the data using the R. This training will be taught with real-time cases and projects. You will be able to develop a report based on the evaluated data. This is an important step for any companies where they can make business and customer-related decisions.

There is a huge demand in the data market in the IT companies, Multinational Companies due to the shortage of the Data Scientists. The future is great for Data Scientists due to the hike in the salaries.

- The demand for Data Scientists will grow tremendously by 28% to 2020.
- Data Scientist jobs will increase by 59% in Finance, Health and Insurance and IT.
- The national average salary of a Data Scientist in India is INR/- 6, 20, 24.

This training can be taken by anyone looking forward to building a firm career in top MNCs as a Data Scientist. This can be learned from the following professionals.

- Analytics Managers.
- Business Analysts.
- Information Architectures.
- Business Intelligence Professionals.
- IT Professionals.
- Software Developers.

### Prerequisites to Learn the Data Science Online Training:

The Data Scientist Training can be taken by anyone with basic knowledge of IT infrastructure, data analysis. Anyone working on data and business analytics can also take this course. Also, this course can also be taken by anyone who has been exposed to science in their career fields.

- Learn Data Acquisition.
- Master R Language.
- Understand Advanced Statistical Concepts.
- Expertise Machine Learning Algorithms.
- Implement Data Collection And Data Mining.
- Master The Project Deployment Tools.
- Learn Prediction and Analysis Segmentation.

- 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

### Information will be Available soon…