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Data Science Masters Program

data science

data scienceData Science Master program at Besant Technologies provided by experienced Data Scientists. Our Data Science Course module is completely designed about how to analyze Data Science with R programming and Data Science with Python programming. Data Science course certification will help you be a professional Data Scientist. If you really Interested to Learn Data Science, then Besant Technologies is the Right place.

About the Program

This course prepares you for the role of Data Scientist by making you an expert in Statistics, Data Science, Big Data, R Programming, Python. There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience.

Learning Path Curriculum 

The term “data scientist” is an industry recognized designation for a professional with deep analytics experience, industry knowledge, and skills. Our Data Science Masters Training will give hands-on experience to you to meet the demands of industry needs.

Statistics Essentials for Analytics

All the topics in the following section will explain the basis of what it is, which scenario you want to use, What math behind it, How to implement with an analytic tool, what inferences you are getting from the final result.

  • Understanding the Data
  • Probability and its Uses
  • Statistical Inference
  • Data Clustering
  • Testing the Data
  • Regression Modelling
Introduction

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R console and Editor
  • Packages in R
  • CRAN
  • How to check package by date
  • Variables
  • Data Types
  • Data structure
  • Factors
  • Converting variable types
  • Missing values

Importing and Exporting in R

  • Loading data from file(Text,CSV,Excel)
  • Loading data from the clipboard
  • Connecting MySQL in R
  • How to remove lines while importing
  • Saving R data format
  • Exporting in R(Excel,Text,PDF,JSON)

Data Cleaning Process

  • Concentrating strings
  • Find and replace
  • How to split the string
  • Position based splitting
  • Semi matching condition
  • Condition-based row/column selection
  • Renaming column names

Data Manipulation

  • Data sorting
  • Find and remove duplicates record
  • Recoding data
  • Merging data
  • Data aggregation
  • User-defined functions
  • Local and global variables
  • Date and Time format in R

Loops

  • For
  • If else
  • While
  • Break
  • Next
  • Return

 Visualization in R

  • Bar, stacked bar chart
  • Pie chart
  • Line chart
  • Scatter plot
  • Histogram
  • Column chart
  • Doughnut chart
  • Trending visualization charts in R
Introduction

  • Why do we need Python?
  • Program structure

Execution steps

  • Interactive Shell
  • Executable or script files
  • User Interface or IDE

Memory management and Garbage collections

  • Object creation and deletion
  • Object properties

Data Types and Operations

  • Numbers
  • Strings
  • List
  • Tuple
  • Dictionary
  • Other Core Types

Statements and Syntax

  • Assignments, Expressions and prints
  • If tests and Syntax Rules
  • While and For Loops

File Operations

  • Opening a file
  • Using Files – txt ,Csv, Xlsx
  • How to connect MySQL
  • Find and replace
  • Appending to file
  • Exporting file

Functions

  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions
  • Packaging Importing

Oops

  • Function definition and call
  • Function Scope
  • Arguments
  • Function Objects
  • Anonymous Functions
  • Packaging Importing

Pandas Section

  • Defining Panda
  • Pandas – Creating and Manipulating Data
  • How to Create Data Frames?
  • Working with dates
  • How to slice and condition based selection
  • Importance of Grouping and Sorting
  • Comparison with MySQL
Introduction and Overview

  • Why Tableau? Why Visualization?
  • Level Setting – Terminology
  • Getting Started – creating some powerful visualizations quickly
  • The Tableau Product Line
  • Things you should know about Tableau

Getting Started

  • Connecting to Data and introduction to data source concept
  • Working with data files versus database server
  • Understanding the Tableau workspace
  • Dimensions and Measures
  • Using Show Me!
  • Tour of Shelves (How shelves and marks work)
  • Building Basic Views
  • Help Menu and Samples
  • Saving and sharing your work

Analysis
Creating Views

  • Marks
  • Size and Transparency
  • Highlighting
  • Working with Dates
  • Date aggregations and date parts
  • Discrete versus Continuous
  • Dual Axis / Multiple Measures
  • Combo Charts with different mark types
  • Geographic Map Page Trails
  • Heat Map
  • Density Chart
  • Scatter Plots
  • Pie Charts and Bar Charts
  • Small Multiples
  • Working with aggregate versus disaggregate data
  • Analyzing
  • Sorting & Grouping
  • Aliases
  • Filtering and Quick Filters
  • Cross-Tabs (Pivot Tables)
  • Totals and Subtotals Drilling and Drill Through
  • Aggregation and Disaggregation
  • Percent of Total
  • Working with Statistics and Trend lines

Getting Started with Calculated Fields

  • Working with String Functions
  • Basic Arithmetic Calculations
  • Date Math
  • Working with Totals
  • Custom Aggregations
  • Logic Statements

Formatting

  • Options in Formatting your Visualization
  • Working with Labels and Annotations
  • Effective Use of Titles and Captions
  • Introduction to Visual Best Practices

Building Interactive Dashboard

  • Combining multiple visualizations into a dashboard
  • Making your worksheet interactive by using actions and filters
  • An Introduction to Best Practices in Visualization

Sharing Workbooks

  • Publish to Reader
  • Packaged Workbooks
  • Publish to Office
  • Publish to PDF
  • Publish to Tableau Server and Sharing over the Web

Putting it all together

  • Scenario-based Review Exercises
  • Best Practices
Data Science Jobs Out Look

Data Science Careers Outlook. A shortage of data scientists means the employment outlook for professionals with the required knowledge and technical skills is extremely positive. It predicts that between now and 2020, demand for data scientists and data engineers will grow by 39 percent.

data science

Frequently Asked Questions

Why data cleaning plays a vital role in analysis?

Cleaning data from multiple sources to transform it into a format that data analysts or data scientists can work with is a cumbersome process because – as the number of data sources increases, the time take to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.

What is logistic regression? Or State an example when you have used logistic regression recently?

Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

What are Recommender Systems?

A subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product. Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

What is Linear Regression?

Linear regression is a statistical technique where the score of a variable Y is predicted from the score of a second variable X. X is referred to as the predictor variable and Y as the criterion variable.

What is Interpolation and Extrapolation?

Estimating a value from 2 known values from a list of values is Interpolation. Extrapolation is approximating a value by extending a known set of values or facts.

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