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COMPUTERS-DATA-ANAYTICS

A data analyst is not into coding as much as a data scientist does, but he must have programming language knowledge but knowledge of statistical programming languages like R/ Python is expected. He must also have knowledge of data bases like SQL (structured query language) to perform data analysis. Though excel is not as powerful as R or Python, a knowledge on excel will help to work on smaller data sets which is the best option for start-ups. Data visualization and presentation is an integral part of the data analyst job, hence knowledge of tableau and business presentation tools like MS Excel, MS PowerPoint is also essential.

The data analyst course topics are:

Statistics

Data analytics with R

SAS

Tableau

The data analyst course syllabus in general would be:

Data and overview of data types

Introduction to Data Types

Numerical parameters to represent data

          a. Mean

          b. Mode

          c. Median

          d. Sensitivity

          e. Information Gain

          f.  Entropy

Statistical parameters to represent data

Probability

Uses of probability

Need of probability

Bayesian Inference

Density Concepts

Normal Distribution Curve

Statistics

Point Estimation

Confidence Margin

Hypothesis Testing

Levels of Hypothesis Testing

Data clustering

Association and Dependence

Causation and Correlation

Covariance

Simpson’s Paradox

Clustering Techniques

Data testing techniques

Parametric Test

Parametric Test Types

Non- Parametric Test

Experimental Designing

A/B testing

Regression modelling and Distributions

Probability distributions and regression analysis

Linear regression analysis and model

Normal distribution and binomial distribution

Logistic and Regression Techniques

Problem of Collinearity

WOE and IV

Residual Analysis

Heteroscedasticity

Homoscedasticity

Introduction to R programming

Various data types in R

Built-in functions in R

Subsetting methods

Data cleansing

Data inspection

Data import from excel and other spreadsheets and text files into R

Data import from SPSS, sas7bdat

Installation of packages for database import

Fundamentals of Web scrapping

Summarize data

Exploratory data analysis

Overview of EDA

Understand and implement EDA on various data sets

Understand the EDA functions

Work with box plots, segment plots etc.

Data visualization in R

Data visualization and graphical functions and representations in R

Plotting various graphs like tableplot, histogram, boxplot

Understand the working of Deducer, and R commander

Understand the graphical functions in R

Customize graphical parameters to improvise the plots

Overview to spatial plots

Data mining and clustering techniques

Introduction to data mining

Overview of machine learning

Machine learning algorithms – supervised and unsupervised

K-Means clustering technique

Association rule

Sentiment analysis

Predictive analysis techniques

Annova technique

Decision Trees and Random forest techniques

Concept of decision trees

Classification rules of decision trees

Understand the algorithms for creating decision trees

Create perfect decision trees

Concepts and features of decision trees

Working on Random forest

Entropy and gaining information

SAS

Understand the implementation of SAS and the use cases

Understand the different data types in SAS

Understand data step and procedural step

Understand the basic procedural steps

Overview of SAS GUI

Introduction to SAS Window and its contents

Use of formats and informats in SAS

Create instream SAS Data set

Process and Integrate with data        

Data import in SAS

Work with permanent and temporary data sets

Set and merge statements

Manipulate influx of data sets into SAS

Customize dataset

Program with SAS data sets

Simplify processing with SAS using conditional and iterative processing

Date and time functions

Numeric and character functions

SAS Arrays

SAS statistical procedures

Advanced statistical procedures with SAS

Overview of clustering

Concepts of hierarchical clustering

Concepts of Non-Hierarchical clustering(K-clustering)

Simple, multiple and logistic regression

Data optimization variables

Concepts of data optimization and optimization models

Introduction to ODS and its benefits

Generate rtf, pdf, html, and doc files

SQL

Work with select statements and other select statement clauses

Work with JOINS and UNIONS

Create new tables

CASE expression

SAS Macros

Overview and benefits of using SAS Macros

Macro variables

Macro code constituents

Positional parameters to Macros

Data visualization

Introduction to data visualization

Introduction to Tableau 10

Data blending and establishing a connection

Visual analytics

Manage metadata and extracts

Data granularity using marks card

Work with sort, filter and grouping (static and dynamic)

Graphic visualization

Dashboard, stories and Mapping

Introduction and creation of a dashboard layout

Introduction to story point

Introduction to maps and web mapping services

Design dashboard for devices

Interact using action

Create background images

Create polygon maps

Work with custom geocoding

Charts in Tableau

Work with Gantt, waterfall, pareto charts

Work with control charts, funnel charts, box and whisker’s plots

Integration of Tableau, R and Hadoop

Introduction to big data, R and Hadoop concepts

Integrate R with Hadoop

Integrate Tableau with R

Integrate visualization using Tableau