Data Analytics Masters Program

Data Science
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Enroll now to become a Certified Data Analytics expert with CertZip Data Analytics Masters Program and upgrade your skills.

Course Description

Data Analyst Course makes you experienced in tools and systems utilized by Data Analytics Professionals. It contains in-depth training in Statistics, Data Analytics with R and Tableau.

Data analytics certifications are prepared to demonstrate you know data management and analytics concepts and are proficient in data analysis skills. On the other hand, data analytics certificates and certificate programs are created to help you know more about the field.

A data science master's program helps you acquire skills to collect, manage and interpret data, its types, trends, and deliver the results accordingly. This developed skillset is extended out throughout the M.

There are no prerequisites for enrollment in the Data Analytics Course Masters Program.

experienced professional working in the IT industry, a candidate preparing to enter the world of Data Analyst

Data analysts work with enormous volumes of data, turning them into insights businesses can leverage to create better decisions. They work across various industries—from healthcare and finance to retail and technology.

Data analytics assists individuals and organizations make sense of data. Data analysts generally examine raw data for insights and trends. They utilize different tools and techniques to help organizations make decisions and succeed.

Skilled data analysts are some of the most sought-after experts in the world. Because the demand is so intense and the supply of people who can do this job well is restricted, data analysts control huge pay and excellent perks, even at the entry-level.

Data analytics is the science of examining basic data to make conclusions about that Information. The methods and procedures of data analytics have been automated into mechanical methods and algorithms that work over raw Data for human consumption. Data analytics assist a business in optimizing its performance.

What you'll learn

  • In this course, you will learn: Statistics, Data Analytics with R Tableau and more


  • There are requirements for learning this course.


Understand various data types, Various variable types, List the uses of varying types, Explain Population and Sample, Discuss sampling techniques, Understand Data representation

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

Understand rules of probability, dependent and independent events, Implement conditional, marginal, and joint probability using Bayes Theorem, Discuss probability distribution, Explain Central Limit Theorem.

Uses of probability,
Need of probability,
Bayesian Inference,
Density Concepts,
Normal Distribution Curve

Understand the concept of point estimation using confidence margin, Draw meaningful inferences using a margin of error, Explore hypothesis testing and its different levels

Point Estimation,
Confidence Margin,
Hypothesis Testing,
Levels of Hypothesis Testing

Understand the concept of association and dependence, Explain causation and correlation, Learn the idea of covariance, Discuss Simpson's paradox, Illustrate Clustering Techniques

Association and Dependence,
Causation and Correlation,
Simpson's Paradox,
Clustering Techniques

Understand Parametric and Non-parametric Testing, various types of parametric testing, Discuss experimental designing, Explain a/b testing

Parametric Test,
Parametric Test Types,
Non- Parametric Test,
Experimental Designing,
A/B testing

Understand the concept of Linear Regression, Explain Logistic Regression, Implement WOE, Differentiate between heteroscedasticity and homoscedasticity, Learn the idea of residual analysis

Logistic and Regression Techniques,
Problem of Collinearity,
WOE and IV,
Residual Analysis,

learn important keywords in R like Business Intelligence, Business Analytics, Data, and Information, how R can play an essential role in solving complex analytical problems, use 'R' in the industry, compare R with other analytics software, and install R its packages.

Introduction to terms like Business Intelligence,
Business Analytics,
Data, Information,
how information hierarchy can be improved/introduced,
understanding Business Analytics and R,
knowledge about the R language,
its community and ecosystem,
comprehend the use of 'R' in the industry, compare R with other software in analytics
Install R and the packages usable for the course, perform necessary functions in R
using the command line, knowing the usage of IDE R Studio and Various GUI,
use the 'R help' feature in R,
knowledge about the worldwide R community collaboration.

Learn the basics of R programming, like data types and functions. This module presents a scenario and lets you think about the options to resolve it, such as which datatype should store the variable or which R function can help you in this scenario.

The different kinds of data types in R and its appropriate uses.
the built-in functions in R like: seq(), cbind (), rbind(), merge(),
Knowledge on the various subsetting methods,
summarize data by using functions like: str(), class(), length(), nrow(), ncol(),
use of functions like head(), tail(),
for inspecting data,
Indulge in a class activity to summarize data,
dplyr package to perform SQL join in R

learn dirty data set and perform Data Cleaning on it, resulting in a data set ready for any analysis. Thus utilizing and exploring the popular functions needed to clean data in R.

The various steps involved in Data Cleaning,
functions used in Data Inspection,
tackling the problems faced during Data Cleaning,
uses of the functions like grep(), grep(), sub(),
Coerce the data and utilizes the apply() functions.

comprehend the versatility and robustness of R, which can take up data in different formats, from a csv file to the data scratched. This module teaches you different data importing techniques in R.

Import data from spreadsheets and text files into R,
port data from further statistical forms like sas7bdat and spss,
packages installation used for database import,
connect to RDBMS from R utilizing ODBC and necessary SQL questions in R,
basics of Web Scraping.

learn that exploratory data analysis is an essential step in analyzing various tasks involved in a typical EDA process.

Understanding the Exploratory Data Analysis(EDA),
implementation of EDA on various datasets,
whiskers of Boxplots.
understanding the cor() in R,
EDA functions like summarize(), list(),
multiple packages in R for data analysis,
the Fancy plots like the Component plot, HC plot in R.

learn that Visualization is the USP of R. You will learn the concepts of creating complex and straightforward visualizations in R.

Understanding of Data Visualization,
graphical functions present in R,
plot various graphs like table plots,
customizing Graphical Parameters to improvise actions,
understanding GUIs like Deducer and R Commander,
introduction to Spatial Analysis.

comprehend the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning, and the distinction between the two types, discuss the process involved in 'K-means Clustering' and the various statistical measures you require to know to execute it in this module.

Introduction to Data Mining,
Understanding Machine Learning,
Supervised and Unsupervised Machine Learning Algorithms,
K-means Clustering.

know how to see the associations between many variables utilizing the popular data mining technique called the "Association Rule Mining" and execute it to predict buyers' next purchase. Learn a new design that can be utilized for recommendation purposes called "Collaborative Filtering." Different real-time-based scenarios are shown utilizing these techniques.

Association Rule Mining,
User-Based Collaborative Filtering (UBCF),
Item Based Collaborative Filtering (IBCF)

Learn the base of 'Regression Techniques.' Linear and logistic RegressionRegression is explained from the basics with the examples, and it is implemented in R using two case studies dedicated to each type of RegressionRegression discussed.

Linear Regression,
Logistic Regression.

Learn about the Analysis of Variance (Anova) Technique. The algorithm and various aspects of Anova have been discussed in this module, Sentiment Analysis, and how we can fetch, extract, and mine live data from Twitter to find out the sentiment of the tweets.

Sentiment Analysis.

Learn concepts of Decision Trees and Random Forest. Random Forests algorithm is discussed step-wise and explained with real-life examples. These concepts are implemented on a real-life data set at the end of the class.

Decision Tree,
the three features for classification of a Decision Tree,
Gini Index,
Pruning and Information Gain,
bagging of Regression and Classification Trees,
concepts of Random Forest,
working of Random Forest,
features of Random Forest,
among others.

discusses different concepts taught throughout the course and their execution in a project.

Analyze census data to expect insights on the income of the people,
based on the factors like age, education, work class, occupation
using Decision Trees,
Logistic Regression and Random Forest.
Analyze the Sentiment of Twitter data,
The data to be examined is streamed live from Twitter, and sentiment analysis is conducted.

Get a brief idea of the Data Visualization and Tableau Prep Builder tool.

Data Visualization,
Business Intelligence tools,
Introduction to Tableau,
Tableau Architecture,
Tableau Server Architecture,
Introduction to Tableau Prep,
Tableau Prep Builder User Interface,
Data Preparation techniques utilizing the Tableau Prep Builder tool,
Create a simple data flow utilizing the Tableau Prep Builder tool,
Group and Replace feature utilizing Tableau Prep Builder tool,
Pivoting data using the Tableau Prep Builder tool,
Aggregate data utilizing the Tableau Prep Builder tool,
Perform Unions and Joins utilizing the Tableau Prep Builder tool

get a brief idea of Tableau UI components and various ways to establish a data connection.

Features of Tableau Desktop,
Connect to data from File and Database,
Types of Connections,
Joins and Unions,
Data Blending,
Tableau Desktop User Interface,
Basic project (Make a workbook and publish it on Tableau Online),
Joins using Tableau Desktop,
Data Blending feature within Tableau,
Create a Workbook and post it over Tableau Online,
Save a workbook in different formats

comprehend the significance of Visual Analytics and analyze the diverse charts, features, and techniques used for Visualization.

Visual Analytics,
Basic Charts (Bar Chart, Line Chart, and Pie Chart),
Data Granularity,
Basic Charts in Tableau,
Illustrate Hierarchies, Data Granularity, and Highlight parts in Tableau,
Complete Sorting, Filtering, and Grouping techniques in Tableau,
Sets in Tableau

understand basic calculations such as Numeric, String Manipulation, Date Function, Logical and Aggregate, Table Calculations, and Level Of Detail (LOD) expressions.

Types of Calculations,
Built-in Operations (Number, String, Date, Logical, and Aggregate),
Operators and Syntax Conventions,
Table Calculations,
Level Of Detail (LOD) Calculations,
Using R within Tableau for Calculations,
Demonstrate calculations using Built-in Functions in Tableau,
Execute Quick Table and Level Of Detail (LOD) calculations in Tableau,
Installing R and designating a connection with R within Tableau

deep dive into Visual Analytics in a more fine manner. It covers different advanced methods for analyzing data, including Forecasting, Trend Lines, Reference Lines, Clustering, and Parameterized concepts.

Trend lines,
Reference lines,
Demonstrate Parameters in Calculations,
Perform Data Visualization utilizing Trend lines, Forecasting, and Clustering feature in Tableau,
Project 1- Domain: Media & Entertainment Industry

deep dive into advanced analytical scenarios, utilizing Level Of Detail expressions.

Case 1 - Count Customers by Order
Case 2- Profit per Business Day
Case 3- Comparative Sales
Case 4- Profit Vs. Target
Case 5- Finding the second order date
Case 6- Cohort Analysis
All the use cases are Hands-on intensive

understanding of Geographic Visualizations in Tableau.

Introduction to Geographic Visualizations
Manually assigning Geographical Locations
Types of Maps
Spatial Files
Custom Geocoding
Polygon Maps
Web Map Services
Background Images
Make a Map and assign Geographic locations to the fields
Explain how to make a Map from a Spatial file
Discover how to make a Filled Map, Symbol Map, and a Density Map
Perform Custom Geocoding in Maps
Build a Polygon Map
Establish a connection with the WMS Server
Identify the bottlenecks
Different configurations needed to resolve the bottlenecks
Know tips and tricks to efficient code mappings
Cache and DTM buffer memory configuration

learn to plot various advanced graphs in Tableau Desktop.

Box and Whisker's Plot,