Data Analytics Course Online Master’s Program


“Edutech Skills” Data Analytics preparing has been curated to help you ace the space of Data Analytics. In this online Data Analytics classes, you will find out about Data Science with R, Tableau, Power BI and SAS investigation, alongside subjects like information mining, information perception, measurable examination, Tableau incorporation with R, relapse demonstrating, and more through involved tasks and contextual analyses.


What Data Analytics courses are taught in this Data Analytics Master course?

Online instructor-led courses:

  • Introduction to Data Analytics
  • SQL for Data Analysis
  • Data Analytics Using R
  • Tableau Desktop 10
  • Business Analyst
  • Data Analytics Capstone Project

Self-paced courses:

  • Power BI
  • Statistics and Probability
  • Advanced Excel
  • SAS

What will you learn in this Data Analytics course?

In this online course, you will learn about the following topics:

  • Data Analytics domain
  • Data Analytics lifecycle
  • R programming for statistical computing
  • Data visualization techniques in Tableau
  • Statistics and probability essentials
  • Data sampling, clustering, and plotting
  • Advanced analytics using SAS

What can you expect from this affiliated with Microsoft and IBM?

On completing this Data Analyst training program, edutech skills will provide you with a Data Analyst certification after you complete the course. Moreover, you will receive certification from Microsoft and IBM which are among the top organizations in the world. These certificates aim to test your knowledge and skills in the field of Data Analyst.

Why become a Data Analyst?

Data Analyst is among the most sought-after career options in today’s technologically advanced world. There are numerous job opportunities available in this domain which is one of the main reasons why you can opt for this career option.

  • As per IBM, jobs in this domain will rise by 15% by 2020, leading to the creation of more than 2.72 million jobs for Data Analytics professionals.

Who should take up this online Data Analyst Course?

Edutech Skills Data Analytics online course is exclusively designed by industry experts for the following professionals:

  • Non-technical professionals like banking, BPO, HR, finance, marketing & sales personnel can extremely benefit and learn Data Analytics online
  • Software Developers
  • Business Intelligence & Data Analytics Professionals
  • Information Architects
  • Project Managers

What are the prerequisites for taking up this online Data Analytics Certification?

There are no prerequisites for taking up online Data Analytics courses. A basic knowledge of data analysis, statistics and probability is beneficial to take up the Data Analyst online courses.

What is the average salary for a Data Analyst in India and in the United States?

According to Glassdoor, the average income of a Data Analyst in the United States is about US$62,453 per annum. This may increase to US$95,000 per annum with more experience and better work quality.

In India, the average salary of these professionals is approximately ₹503,000 per annum and with more experience, it can rise to ₹1,005,000 per annum

How Data Analysts are different from Business Analysts or Data Scientists?

Here are a couple of differences between Data Scientists, Data Analysts, and Business Analysts:

  • In terms of skillset, Data Analysts analyze the business requirements, while Business Analysts analyze the historical data and Data Scientist make decisions based on the given data
  • Data Analysts perform the complete life-cycle of data analysis, whereas Business Analysts implement, build, analyze, and report the capabilities of business. Data Scientists, on the other hand, perform statistical analysis to build Machine Learning systems.

Why should you go for the Data Analytics Training Course?

  • A Data Analyst at Microsoft Corporation earns an average salary of $115,000 per year – Indeed
  • The USA faces a shortage of 165,000 Data Analysts & 1.5 managers with Data analysis skills – McKinsey
  • There are over 30,000 jobs available for Data Analysts in the United States alone – LinkedIn

Today Data Analytics is one of the top domains as we are living in a data-driven world. If you want to get ahead in your career, then you need to learn Data Analytics as it is being deployed in every organization regardless of the industry. Edutech Data Analytics Certification courses have been created to give you an edge in this data-driven world. Through this online training, you will work on real-world Data Analytics projects and case studies so that you can get a hands-on experience in this domain.

Introduction to Data Analytics (Live Course)

Module 1 – Introduction to Data Analytics

1.1 Data Analytics Overview

SQL for Data Analysis (Live Course)

Module 1 – Introduction to SQL

1.1 Various types of databases
1.2 Introduction to Structured Query Language
1.3 Distinction between client server and file server databases
1.4 Understanding SQL Server Management Studio
1.5 SQL Table basics
1.6 Data types and functions
1.7 Transaction-SQL
1.8 Authentication for Windows
1.9 Data control language
1.10 The identification of the keywords in T-SQL, such as Drop Table

Module 2 – Database Normalization and Entity Relationship Model

2.1 Entity-Relationship Model
2.2 Entity and Entity Set
2.3 Attributes and types of Attributes
2.4 Entity Sets
2.5 Relationship Sets
2.6 Degree of Relationship
2.7 Mapping Cardinalities, One-to-One, One-to-Many, Many-to-one, Many-to-many
2.8 Symbols used in E-R Notation

Module 3 – SQL Operators

3.1 Introduction to relational databases
3.2 Fundamental concepts of relational rows, tables, and columns
3.3 Several operators (such as logical and relational), constraints, domains, indexes, stored procedures, primary and foreign keys
3.4 Understanding group functions
3.5 The unique key

Module 4 – Working with SQL: Join, Tables, and Variables

4.1 Advanced concepts of SQL tables
4.2 SQL functions
4.3 Operators & queries
4.4 Table creation
4.5 Data retrieval from tables
4.6 Combining rows from tables using inner, outer, cross, and self joins
4.7 Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’
4.8 Temporary table creation
4.9 Set operator rules
4.10 Table variables


Module 5 – Deep Dive into SQL Functions

5.1 Understanding SQL functions – what do they do?
5.2 Scalar functions
5.3 Aggregate functions
5.4 Functions that can be used on different datasets, such as numbers, characters, strings, and dates
5.5 Inline SQL functions
5.6 General functions
5.7 Duplicate functions

Module 6 – Working with Subqueries

6.1 Understanding SQL subqueries, their rules
6.2 Statements and operators with which subqueries can be used
6.3 Using the set clause to modify subqueries
6.4 Understanding different types of subqueries, such as where, select, insert, update, delete, etc.
6.5 Methods to create and view subqueries

Module 7 – SQL Views, Functions, and Stored Procedures

7.1 Learning SQL views
7.2 Methods of creating, using, altering, renaming, dropping, and modifying views
7.3 Understanding stored procedures and their key benefits
7.4 Working with stored procedures
7.5 Studying user-defined functions
7.6 Error handling

Module 8 – Deep Dive into User-defined Functions

8.1 User-defined functions
8.2 Types of UDFs, such as scalar
8.3 Inline table value
8.4 Multi-statement table
8.5 Stored procedures and when to deploy them
8.6 What is rank function?
8.7 Triggers, and when to execute triggers?

Module 9 – SQL Optimization and Performance

9.1 SQL Server Management Studio
9.2 Using pivot in MS Excel and MS SQL Server
9.3 Differentiating between Char, Varchar, and NVarchar
9.4 XL path, indexes and their creation
9.5 Records grouping, advantages, searching, sorting, modifying data
9.6 Clustered indexes creation
9.7 Use of indexes to cover queries
9.8 Common table expressions
9.9 Index guidelines

Module 10 – Advanced Topics

10.1 Correlated Subquery, Grouping Sets, Rollup, Cube

Hands-on Exercise

  1. Implementing Correlated Subqueries
  2. Using EXISTS with a Correlated subquery
  3. Using Union Query
  4. Using Grouping Set Query
  5. Using Rollup
  6. Using CUBE to generate four grouping sets
  7. Perform a partial CUBE

Module 11 – Managing Database Concurrency

11.1 Applying transactions
11.2 Using the transaction behavior to identify DML statements
11.3 Learning about implicit and explicit transactions
11.4 Isolation levels management
11.5 Understanding concurrency and locking behavior
11.6 Using memory-optimized tables

Module 12 – Programming Databases Using Transact-SQL

12.1 Creating Transact-SQL queries
12.2 Querying multiple tables using joins
12.3 Implementing functions and aggregating data
12.4 Modifying data
12.5 Determining the results of DDL statements on supplied tables and data
12.6 Constructing DML statements using the output statement
12.7 Querying data using subqueries and APPLY
12.8 Querying data using table expressions
12.9 Grouping and pivoting data using queries
12.10 Querying temporal data and non-relational data
12.11 Constructing recursive table expressions to meet business requirements
12.12 Using windowing functions to group
12.13 Rank the results of a query
12.14 Creating database programmability objects by using T-SQL
12.15 Implementing error handling and transactions
12.16 Implementing transaction control in conjunction with error handling in stored procedures
12.17 Implementing data types and NULL
12.18 Designing and implementing relational database schema
12.19 Designing and implementing indexes
12.20 Learning to compare between indexed and included columns
12.21 Implementing clustered index
12.22 Designing and deploying views
12.23 Column store views

12.24 Explaining foreign key constraints
12.25 Using T-SQL statements
12.26 Usage of Data Manipulation Language (DML)
12.27 Designing the components of stored procedures
12.28 Implementing input and output parameters
12.29 Applying error handling
12.30 Executing control logic in stored procedures
12.31 Designing trigger logic, DDL triggers, etc.
12.32 Accuracy of statistics
12.33 Formulating statistics maintenance tasks
12.34 Dynamic management objects management
12.35 Identifying missing indexes
12.36 Examining and troubleshooting query plans
12.37 Consolidating the overlapping indexes
12.38 The performance management of database instances
12.39 SQL server performance monitoring

Module 13 – Microsoft Courses: Study Material

13.1 Performance Tuning and Optimizing SQL Databases
13.2Querying Data with Transact-SQL

SQL Projects

Writing Complex Subqueries

In this project, you will be working with SQL subqueries and utilizing them in various scenarios. You will learn to use IN or NOT IN, ANY or ALL, EXISTS or NOT EXISTS, and other majorRead More. queries. You will be required to access and manipulate datasets, operate and control statements, execute queries in SQL against databases.

Querying a Large Relational Database

This project is about how to get details about customers by querying the database. You will be working with Table basics and data types, various SQL operators, and SQL functions. The project will require youRead More. to download a database and restore it on the server, query the database for customer details and sales information.

Relational Database Design

In this project, you will learn to convert a relational design that has enlisted within its various users, user roles, user accounts, and their statuses into a table in SQL Server. You will haveRead More.. to define relations/attributes, primary keys, and create respective foreign keys with at least two rows in each of the tables.

Data Analytics Using R (Live Course)

Module 01 – Introduction to Data Science with R

1.1 What is Data Science?
1.2 Significance of Data Science in today’s data-driven world, its applications of, , lifecycle, and its components
1.3 Introduction to R programming and RStudio

Hands-on Exercise:

1. Installation of RStudio
2. Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases

Module 02 – Data Exploration

2.1 Introduction to data exploration
2.2 Importing and exporting data to/from external sources
2.3 What are data exploratory analysis and data importing?
2.4 DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Hands-on Exercise:

1. Accessing individual elements of customer churn data
2. Modifying and extracting results from the dataset using user-defined functions in R

Module 03 – Data Manipulation

3.1 Need for data manipulation
3.2 Introduction to the dplyr package
3.3 Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
3.4 Combining different functions with the pipe operator and implementing SQL-like operations with sqldf

Hands-on Exercise:

1. Implementing dplyr
2. Performing various operations for manipulating data and storing it

Module 04 – Data Visualization

4.1 Introduction to visualization
4.2 Different types of graphs, the grammar of graphics, the ggplot2 package, categorical distribution with geom_bar(), numerical distribution with geom_hist(), building frequency polygons with geom_freqpoly(), and making a scatterplot with geom_pont()
4.3 Multivariate analysis with geom_boxplot
4.4 Univariate analysis with a barplot, a histogram and a density plot, and multivariate distribution
4.5 Creating barplots for categorical variables using geom_bar(), and adding themes with the theme() layer
4.6 Visualization with plotly, frequency plots with geom_freqpoly(), multivariate distribution with scatter plots and smooth lines, continuous distribution vs categorical distribution with box-plots, and sub grouping plots
4.7 Working with co-ordinates and themes to make graphs more presentable, understanding plotly and various plots, and visualization with ggvis
4.8 Geographic visualization with ggmap() and building web applications with shinyR

Hands-on Exercise:

1. Creating data visualization to understand the customer churn ratio using ggplot2 charts
2. Using plotly for importing and analyzing data
3. Visualizing tenure, monthly charges, total charges, and other individual columns using a scatter plot

Module 05 – Introduction to Statistics

5.1 Why do we need statistics?
5.2 Categories of statistics, statistical terminology, types of data, measures of central tendency, and measures of spread
5.3 Correlation and covariance, standardization and normalization, probability and the types, hypothesis testing, chi-square testing, ANOVA, normal distribution, and binary distribution

Hands-on Exercise:

1. Building a statistical analysis model that uses quantification, representations, and experimental data
2. Reviewing, analyzing, and drawing conclusions from the data

Module 06 – Machine Learning

6.1 Introduction to Machine Learning
6.2 Introduction to linear regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions, and residuals in Linear Regression, and building a simple linear model
6.3 Predicting results and finding the p-value and an introduction to logistic regression
6.4 Comparing linear regression with logistics regression and bivariate logistic regression with multivariate logistic regression
6.5 Confusion matrix the accuracy of a model, understanding the fit of the model, threshold evaluation with ROCR, and using qqnorm() and qqline()
6.6 Understanding the summary results with null hypothesis, F-statistic, and
building linear models with multiple independent variables

Hands-on Exercise:

1. Modeling the relationship within data using linear predictor functions
2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable

Module 07 – Logistic Regression

7.1 Introduction to logistic regression
7.2 Logistic regression concepts, linear vs logistic regression, and math behind logistic regression
7.3 Detailed formulas, logit function and odds, bivariate logistic regression, and Poisson regression
7.4 Building a simple binomial model and predicting the result, making a confusion matrix for evaluating the accuracy, true positive rate, false positive rate, and threshold evaluation with ROCR
7.5 Finding out the right threshold by building the ROC plot, cross validation, multivariate logistic regression, and building logistic models with multiple independent variables
7.6 Real-life applications of logistic regression

Hands-on Exercise:

1. Implementing predictive analytics by describing data
2. Explaining the relationship between one dependent binary variable and one or more binary variables
3. Using glm() to build a model, with ‘Churn’ as the dependent variable

Module 08 – Decision Trees and Random Forest

8.1 What is classification? Different classification techniques
8.2 Introduction to decision trees
8.3 Algorithm for decision tree induction and building a decision tree in R
8.4 Confusion matrix and regression trees vs classification trees
8.5 Introduction to bagging
8.6 Random forest and implementing it in R
8.7 What is Naive Bayes? Computing probabilities
8.8 Understanding the concepts of Impurity function, Entropy, Gini index, and Information gain for the right split of node
8.9 Overfitting, pruning, pre-pruning, post-pruning, and cost-complexity pruning, pruning a decision tree and predicting values, finding out the right number of trees, and evaluating performance metrics

Hands-on Exercise:

1. Implementing random forest for both regression and classification problems
2. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
3. Using ROCR for performance metrics

Module 09 – Unsupervised Learning

9.1 What is Clustering? Its use cases
9.2 what is k-means clustering? What is canopy clustering?
9.3 What is hierarchical clustering?
9.4 Introduction to unsupervised learning
9.5 Feature extraction, clustering algorithms, and the k-means clustering algorithm
9.6 Theoretical aspects of k-means, k-means process flow, k-means in R, implementing k-means, and finding out the right number of clusters using a scree plot
9.7 Dendograms, understanding hierarchical clustering, and implementing it in R
9.8 Explanation of Principal Component Analysis (PCA) in detail and implementing PCA in R

Hands-on Exercise:

1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
2. K-means clustering for visualizing and interpreting results for the customer churn data

Module 10 – Association Rule Mining and Recommendation Engines

10.1 Introduction to association rule mining and MBA
10.2 Measures of association rule mining: Support, confidence, lift, and apriori algorithm, and implementing them in R
10.3 Introduction to recommendation engines
10.4 User-based collaborative filtering and item-based collaborative filtering, and implementing a recommendation engine in R
10.5 Recommendation engine use cases

Hands-on Exercise:

1. Deploying association analysis as a rule-based Machine Learning method
2. Identifying strong rules discovered in databases with measures based on interesting discoveries

Self-paced Course Content

Module 11 – Introduction to Artificial Intelligence

11.1 Introducing Artificial Intelligence and Deep Learning
11.2 What is an artificial neural network? TensorFlow: The computational framework for building AI models
11.3 Fundamentals of building ANN using TensorFlow and working with TensorFlow in R

Module 12 – Time Series Analysis

12.1 What is a time series? The techniques, applications, and components of time series
12.2 Moving average, smoothing techniques, and exponential smoothing
12.3 Univariate time series models and multivariate time series analysis
12.4 ARIMA model
12.5 Time series in R, sentiment analysis in R (Twitter sentiment analysis), and text analysis

Hands-on Exercise:

1. Analyzing time series data
2. Analyzing the sequence of measurements that follow a non-random order to identify the nature of phenomenon and forecast the future values in the series

Module 13 – Support Vector Machine (SVM)

13.1 Introduction to Support Vector Machine (SVM)
13.2 Data classification using SVM
13.3 SVM algorithms using separable and inseparable cases
13.4 Linear SVM for identifying margin hyperplane

Module 14 – Naïve Bayes

14.1 What is the Bayes theorem?
14.2 What is Naïve Bayes Classifier?
14.3 Classification Workflow
14.4 How Naive Bayes classifier works and classifier building in Scikit-Learn
14.5 Building a probabilistic classification model using Naïve Bayes and the zero probability problem

Module 15 – Text Mining

15.1 Introduction to the concepts of text mining
15.2 Text mining use cases and understanding and manipulating the text with ‘tm’ and ‘stringR’
15.3 Text mining algorithms and the quantification of the text
15.4 TF-IDF and after TF-IDF

Data Science Projects Covered

Market Basket Analysis

This is an inventory management project where you will find the trends in the data that will help the company to increase sales. In this project, you will be implementing association rule mining, data extraction, and data manipulation for the Market Basket Analysis.

Credit Card Fraud Detection

The project consists of data analysis for various parameters of banking dataset. You will be using a V7 predictor, V4 predictor for analysis, and data visualization for finding the probability of occurrence of fraudulent activities.

Loan Approval Prediction

In this project, you will use the banking dataset for data analysis, data cleaning, data preprocessing, and data visualization. You will implement algorithms such as Principal Component Analysis and Naive Bayes after data analysis to predict the approval rate of a loan using various parameters.

Netflix Recommendation System

Implement exploratory data analysis, data manipulation, and visualization to understand and find the trends in the Netflix dataset. You will use various Machine Learning algorithms such as association rule mining, classification algorithms, and many more to create movie recommendation systems for viewers using Netflix dataset.

Case Study 1: Introduction to R Programming

In this project, you need to work with several operators involved in R programming including relational operators, arithmetic operators, and logical operators for various organizational needs.

Case Study 2: Solving Customer Churn Using Data Exploration

Use data exploration in order to understand what needs to be done to make reductions in customer churn. In this project, you will be required to extract individual columns, use loops to work on repetitive operations, and create and implement filters for data manipulation.

Case Study 3: Creating Data Structures in R

Implement numerous data structures for numerous possible scenarios. This project requires you to create and use vectors. Further, you need to build and use metrics, utilize arrays for storing those metrics, and have knowledge of lists.

Case Study 4: Implementing SVD in R

Utilize the dataset of MovieLens to analyze and understand single value decomposition and its use in R programming. Further, in this project, you must build custom recommended movie sets for all users, develop a collaborative filtering model based on the users, and for a movie recommendation, you must create realRatingMatrix.

Case Study 5: Time Series Analysis

This project required you to perform TSA and understand ARIMA and its concepts with respect to a given scenario. Here, you will use the R programming language, ARIMA model, time series analysis, and data visualization. So, you must understand how to build an ARIMA model and fit it, find optimal parameters by plotting PACF charts, and perform various analyses to predict values.

Tableau Desktop 10 (Live Course)

Module 1 – Introduction to Data Visualization and Power of Tableau

1.1 What is data visualization?
1.2 Comparison and benefits against reading raw numbers
1.3 Real use cases from various business domains
1.4 Some quick and powerful examples using Tableau without going into the technical details of Tableau
1.5 Installing Tableau
1.6 Tableau interface
1.7 Connecting to DataSource
1.8 Tableau data types
1.9 Data preparation

Module 2 – Architecture of Tableau

2.1 Installation of Tableau Desktop
2.2 Architecture of Tableau
2.3 Interface of Tableau (Layout, Toolbars, Data Pane, Analytics Pane, etc.)
2.4 How to start with Tableau
2.5 The ways to share and export the work done in Tableau

Hands-on Exercise:

1. Play with Tableau desktop
2. Learn about the interface
3. Share and export existing works

Module 3 – Working with Metadata and Data Blending

3.1 Connection to Excel
3.2 Cubes and PDFs
3.3 Management of metadata and extracts
3.4 Data preparation
3.5 Joins (Left, Right, Inner, and Outer) and Union
3.6 Dealing with NULL values, cross-database joining, data extraction, data blending, refresh extraction, incremental extraction, how to build extract, etc.

Hands-on Exercise:

1. Connect to Excel sheet to import data
2. Use metadata and extracts
3. Manage NULL values
4. Clean up data before using
5. Perform the join techniques
6. Execute data blending from multiple sources

Module 4 – Creation of Sets

4.1 Mark, highlight, sort, group, and use sets (creating and editing sets, IN/OUT, sets in hierarchies)
4.2 Constant sets
4.3 Computed sets, bins, etc.

Hands-on Exercise:

1. Use marks to create and edit sets
2. Highlight the desired items
3. Make groups
4. Apply sorting on results
5. Make hierarchies among the created sets

Module 5 – Working with Filters

5.1 Filters (addition and removal)
5.2 Filtering continuous dates, dimensions, and measures
5.3 Interactive filters, marks card, and hierarchies
5.4 How to create folders in Tableau
5.5 Sorting in Tableau
5.6 Types of sorting
5.7 Filtering in Tableau
5.8 Types of filters
5.9 Filtering the order of operations

Hands-on Exercise:

1. Use the data set by date/dimensions/measures to add a filter
2. Use interactive filter to view the data
3. Customize/remove filters to view the result

Module 6 – Organizing Data and Visual Analytics

6.1 Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste
6.2 Formatting data using labels and tooltips
6.3 Edit axes and annotations
6.4 K-means cluster analysis
6.5 Trend and reference lines
6.6 Visual analytics in Tableau
6.7 Forecasting, confidence interval, reference lines, and bands

Hands-on Exercise:

1. Apply labels and tooltips to graphs, annotations, edit axes’ attributes
2. Set the reference line
3. Perform k-means cluster analysis on the given dataset

Module 7 – Working with Mapping

7.1 Working on coordinate points
7.2 Plotting longitude and latitude
7.3 Editing unrecognized locations
7.4 Customizing geocoding, polygon maps, WMS: web mapping services
7.5 Working on the background image, including add image
7.6 Plotting points on images and generating coordinates from them
7.7 Map visualization, custom territories, map box, WMS map
7.8 How to create map projects in Tableau
7.9 Creating dual axes maps, and editing locations

Hands-on Exercise:

1. Plot longitude and latitude on a geo map
2. Edit locations on the geo map
3. Custom geocoding
4. Use images of the map and plot points
5. Find coordinates
6. Create a polygon map
7. Use WMS

Module 8 – Working with Calculations and Expressions

8.1 Calculation syntax and functions in Tableau
8.2 Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number
8.3 LOD expressions, including concept and syntax
8.4 Aggregation and replication with LOD expressions
8.5 Nested LOD expressions
8.6 Levels of details: fixed level, lower level, and higher level
8.7 Quick table calculations
8.8 The creation of calculated fields
8.9 Predefined calculations
8.10 How to validate

Module 9 – Working with Parameters

9.1 Creating parameters
9.2 Parameters in calculations
9.3 Using parameters with filters
9.4 Column selection parameters
9.5 Chart selection parameters
9.6 How to use parameters in the filter session
9.7 How to use parameters in calculated fields
9.8 How to use parameters in the reference line

Hands-on Exercise:

1. Creating new parameters to apply on a filter
2. Passing parameters to filters to select columns
3. Passing parameters to filters to select charts

Module 10 – Charts and Graphs

10.1 Dual axes graphs
10.2 Histograms
10.3 Single and dual axes
10.4 Box plot
10.5 Charts: motion, Pareto, funnel, pie, bar, line, bubble, bullet, scatter, and waterfall charts
10.6 Maps: tree and heat maps
10.7 Market basket analysis (MBA)
10.8 Using Show me
10.9 Text table and highlighted table

Hands-on Exercise:

1. Plot a histogram, tree map, heat map, funnel chart, and more using the given dataset
2. Perform market basket analysis (MBA) on the same dataset

Module 11 – Dashboards and Stories

11.1 Building and formatting a dashboard using size, objects, views, filters, and legends
11.2 Best practices for making creative as well as interactive dashboards using the actions
11.3 Creating stories, including the intro of story points
11.4 Creating as well as updating the story points
11.5 Adding catchy visuals in stories
11.6 Adding annotations with descriptions; dashboards and stories
11.7 What is dashboard?
11.8 Highlight actions, URL actions, and filter actions
11.9 Selecting and clearing values
11.10 Best practices to create dashboards
11.11 Dashboard examples; using Tableau workspace and Tableau interface
11.12 Learning about Tableau joins
11.13 Types of joins
11.14 Tableau field types
11.15 Saving as well as publishing data source
11.16 Live vs extract connection
11.17 Various file types

Hands-on Exercise:

1. Create a Tableau dashboard view, include legends, objects, and filters
2. Make the dashboard interactive
3. Use visual effects, annotations, and descriptions to create and edit a story

Module 12 – Tableau Prep

12.1 Introduction to Tableau Prep
12.2 How Tableau Prep helps quickly combine join, shape, and clean data for analysis
12.3 Creation of smart examples with Tableau Prep
12.4 Getting deeper insights into the data with great visual experience
12.5 Making data preparation simpler and accessible
12.6 Integrating Tableau Prep with Tableau analytical workflow
12.7 Understanding the seamless process from data preparation to analysis with Tableau Prep

Module 13 – Integration of Tableau with R

13.1 Introduction to R language
13.2 Applications and use cases of R
13.3 Deploying R on the Tableau platform
13.4 Learning R functions in Tableau

Hands-on Exercise:

1. Deploy R on Tableau
2. Create a line graph using R interface

Tableau Projects Covered

Understanding the global covid-19 mortality rates

Analyze and develop a dashboard to understand the covid-19 global cases. Compare the global confirmed vs. death cases in a world map. Compare the country wise cases using logarithmic axes. Dashboard should display both a log axis chart and a default axis chart in an alternate interactive way. Create a parameter to dynamically view Top N WHO regions based on cumulative new cases and death cases ratio. Dashboard should have a drop down menu to view the WHO region wise data using a bar chart, line chart or a map as per user’s requirement.

Understand the UK bank customer data

Analyze and develop a dashboard to understand the customer data of a UK bank. Create an asymmetric drop down of Region with their respective customer names and their Balances with a gender wise color code. Region wise bar chart which displays the count of customers based on High and low balance. Create a parameter to let the users’ dynamically decide the limit value of balance which categorizes it into high and low. Include interactive filters for Job classifications and Highlighters for Region in the final dashboard.

Understand Financial Data

Create an interactive map to analyze the worldwide sales and profit. Include map layers and map styles to enhance the visualization. Interactive analysis to display the average gross sales of a product under each segment, allowing only one segment data to be displayed at once. Create a motion chart to compare the sales and profit through the years. Annotate the day wise profit line chart to indicate the peaks and also enable drop lines. Add go to URL actions in the final dashboard which directs the user to the respective countries Wikipedia page.

Understand Agriculture Data

Create interactive tree map to display district wise data. Tree maps should have state labels. On hovering on a particular state, the corresponding districts data are to be displayed. Add URL actions, which direct users’ to a Google search page of the selected crop. Web page is to be displayed on the final dashboard. Create a hierarchy of seasons, crop categories and the list of crops under each. Add highlighters for season. One major sheet in the final dashboard should be unaffected by any action applied. Use the view in this major sheet to filter data in the other. Using parameters color code the seasons with high yield and low yield based on its crop categories. Rank the crops based on their yield.

Business Analyst (Live Course)

Module 1: Introducing business analyst

1.1 Introduction to business analyst domain
1.2 The need for business analysts
1.3 The various roles and responsibilities
1.4 How the business analyst fits in the project team
1.5 Significance of communication and collaboration
1.6 Core competencies of business analyst
1.7 Techniques and approaches in business analysis
1.8 How business analysts fit in the corporate structure
1.9 The different departments in the organization that business analysts connect with

Module 2: Understanding business needs

2.1 Understanding the needs of the business
2.2 Gathering the requirements
2.3 Studying feasibility, prioritizing, assessing the risks, evaluating and choosing the right initiative
2.4 Assessing change of requirements
2.5 Getting the requirements approved

Module 3: Project management

3.1 Introduction to the various types of projects
3.2 What are the phases in an IT project
3.3 Important activities, deliverables and key people involved
3.4 Comparing the software development lifecycle and product lifecycle
3.5 How the projects depend on other projects
3.6 What are the tasks and responsibilities of project manager
3.7 Planning and monitoring a project
3.8 Critical path analysis
3.9 Creation of tasks
3.10 Relationship between tasks
3.11 Allocating the resources
3.12 Working under various constraints

Module 4: Techniques used by business analysts

4.1 Introduction to the various techniques that business analysts use like SWOT, CATWOE
4.2 Important tools used by business analysts
4.3 Analysis of strategy
4.4 Various components of strategy analysis
4.5 Identification of stakeholders and the needs of business
4.6 What is business modeling
4.7 Gathering of requirements
4.8 Analyzing, designing, implementing, testing, and deploying in the business environment

Module 5: Software project methodologies

5.1 The various software engineering processes
5.2 Understanding the software project steps
5.3 The software development lifecycle
5.4 The difference between waterfall and agile software project methodologies
5.5 RUP and RAD methodologies
5.6 Project deliverables

Module 6: UML with Microsoft Visio

6.1 UML Architecture
6.2 Modeling Types
6.3 Basic Notations
6.4 Standard Diagrams
6.5 Class Diagram
6.6 Object Diagram
6.7 Use Case Diagram
6.8 Interaction Diagram
6.9 Activity Diagram


  1. Implementing the UML Diagrams using Microsoft Vision

Data Analytics Capstone Project (Live Course)

Module 1 – Data Analyst Capstone

1.1 Data Analyst Capstone

Power BI (Self-paced)

Module 1 – Introduction to Power BI

1.1 Introduction to Microsoft Power BI
1.2 The key features of Power BI workflow
1.3 Desktop application
1.4 Power BI service
1.5 File data sources
1.6 Sourcing data from web (OData, Azure)
1.7 Building dashboard
1.8 Data visualization
1.9 Publishing to cloud
1.10 DAX data computation
1.11 Row context
1.12 Filter context
1.13 Analytics Pane
1.14 Creating columns and measures
1.15 Data drill down and drill up
1.16 Creating tables
1.17 Binned tables
1.18 Data modeling and relationships
1.19 The Power BI components like Power View, Map, Query, Pivot
1.20 Power Q & A
1.21 Understanding advanced visualization

Hands-on Exercise –

  1. Demo of building a Power BI dashboard
  2. Source data from web
  3. Publish to cloud
  4. Create power tables

Module 2 – Extracting Data

2.1 Learning about Power Query for self-service ETL functionalities
2.2 Introduction to data mashup
2.3 Working with Excel data
2.4 Learning about Power BI Personal Gateway
2.5 Extracting data from files, folders and databases
2.6 Working with Azure SQL database and database source
2.7 Connecting to Analysis Services
2.8 SaaS functionalities of Power BI

Hands-on Exercise –

  1. Connect to a database
  2. Import data from an excel file
  3. Connect to SQL Server
  4. Analysis Service
  5. Connect to Power Query
  6. Connect to SQL Azure
  7. Connect to Hadoop

Module 3 – Power Query for Data Transformation

3.1 Installing Power BI
3.2 The various requirements and configuration settings
3.3 The Power Query
3.4 Introduction to Query Editor
3.5 Data transformation – column, row, text, data type, adding & filling columns and number column, column formatting, transpose table, appending, splitting, formatting data, Pivot and UnPivot, Merge Join, relational operators, date, time calculations, working with M functions, lists, records, tables, data types, and generators
3.6 Filters & Slicers
3.7 Index and Conditional Columns
3.8 Summary Tables
3.9 Writing custom functions and error handling
3.10 M advanced data transformations

Hands-on Exercise –

  1. Install Power BI Desktop and configure the settings
  2. Use Query editor
  3. Write a power query
  4. Transpose a table

Module 4 – Power Pivot for Data Modeling and Data Analysis Expression – DAX Queries

4.1 Introduction to Power Pivot
4.2 Learning about the xVelocity engine
4.3 Advantages of Power Pivot
4.4 Various versions and relationships
4.5 Strongly typed datasets
4.6 Data Analysis Expressions
4.7 Measures, Calculated Members, Row, Filter & Evaluation Context, Context Interactions, Context over Relations, Schema Relations
4.8 Learning about Table, Information, Logical, Text, Iterator, Table, and Time Intelligence Functions
4.9 Cumulative Charts, Calculated Tables, ranking and rank over groups
4.10 Power Pivot advanced functionalities
4.11 Date and time functions
4.12 DAX advanced features
4.13 Embedding Power Pivot in Power BI Desktop

Hands-on Exercise –

  1. Create a Power Pivot Apply filters
  2. Use advanced functionalities like date and time functions
  3. Embed Power Pivot in Power BI Desktop
  4. Create DAX queries for calculate column, tables and measures

Module 5 – Data Visualization with Analytics

5.1 Deep dive into Power BI data visualization
5.2 Understanding Power View and Power Map
5.3 Power BI Desktop visualization
5.4 Formatting and customizing visuals
5.5 Visualization interaction
5.6 SandDance visualization
5.7 Deploying Power View on SharePoint and Excel
5.8 Top down and bottom up analytics
5.9 Comparing volume and value-based analytics
5.10 Working with Power View to create Reports, Charts, Scorecards, and other visually rich formats
5.11 Categorizing, filtering and sorting data using Power View
5.12 Hierarchies
5.13 Mastering the best practices
5.14 Custom Visualization
5.15 Authenticate a Power BI web application
5.16 Embedding dashboards in applications

Hands-on Exercise –

  1. Create a Power View and a Power Map
  2. Format and customize visuals
  3. Deploy Power View on SharePoint and Excel
  4. Implement top-down and bottom-up analytics
  5. Create Power View reports, Charts, Scorecards
  6. Add a custom visual to report
  7. Authenticate a Power BI web application
  8. Embed dashboards in applications
  9. Categorize, filter and sort data using Power View
  10. Create hierarchies
  11. Use date hierarchies
  12. Use business hierarchies
  13. Resolve hierarchy issues

Module 6 – Power Q & A

6.1 Introduction to Power Q & A
6.2 Intuitive tool to answer tough queries using natural language
6.3 Getting answers in the form of charts, graphs and data discovery methodologies
6.4 Ad hoc analytics building
6.5 Power Q & A best practices
6.6 Integrating with SaaS applications

Hands-on Exercise –

  1. Write queries using natural language
  2. Get answers in the form of charts, graphs
  3. Build ad hoc analytics
  4. Pin a tile and a range to dashboard

Module 7 – Power BI Desktop & Administration

7.1 Getting to understand the Power BI Desktop
7.2 Aggregating data from multiple data sources
7.3 How Power Query works in Power BI Desktop environment
7.4 Learning about data modeling and data relationships
7.5 Deploying data gateways
7.6 Scheduling data refresh
7.7 Managing groups and row level security, datasets, reports and dashboards
7.8 Working with calculated measures
7.9 Power Pivot on Power BI Desktop ecosystem
7.10 Mastering data visualization
7.11 Power View on Power BI Desktop
7.12 Creating real world solutions using Power BI

Hands-on Exercise –

  1. Configure security for dashboard Deploy data gateways
  2. Aggregate data from multiple data sources
  3. Schedule data refresh
  4. Manage groups and row level security, datasets, reports and dashboards
  5. Work with calculated measures

Module 8 – Microsoft Course

8.1 Analyzing Data with Power BI

Power BI Projects Covered

Report on Student Survey

There are many stores in which a survey was conducted based on students i.e. How much they are spending on different kinds of purchases like Video games, Indoor games, Toys, Books, Gadgets, etc. You have to create a Power BI Report. You will get hands-on experience on Tabular Visualization, Matrix Visualization, Funnel chart, Pie chart, Scatter plot, Sand dance plot

Case Study 1 – Power BI Desktop, Cloud Service, and End to End Workflow

The case study deals with ways to design a dashboard with a basic set of visualizations and deploy it to Power BI Cloud service. Further, a top-level brief overview of Transport Corp Data is shown using aggregated Key Performance Indicators (KPIs), Trends, Gio Distributions, and Filters.

Case Study 2 – Visualizations, Configuring Extended Properties, and Data Calculations DAX – Introduction

This case study explains the way to design a dashboard and perform calculations by making use of Power BI DAX formulas. The scheduled deliveries of loads are analyzed using correlation across measures. Moreover, Drill Up/Drill Down’s capabilities and reference lines are implemented.

Case Study 3 – Combination Visualizations for Correlated Value Columns

Here, the Dashboard is designed by making use of Power BI DAX formulas to perform calculations. Bucketed Categories are created to represent value measures on the categories axis. Furthermore, a scatter plot is used to identify outliers or outperformers.

Case Study 4 – Data Transformations

The case study involves designing an audit dashboard by making use of Power Query, Query Editor to perform data modeling by applying Data transformations, in turn, by managing relationships.

Case Study 5 – Data Transformations – Contd.

Here, the Dashboard is designed to analyze the trend of admissions into a State University. Query Editor is used to perform data modeling by applying transformations like append data, split data, column formatting, transpose table, pivot/unpivot, fill columns, merge join, conditional columns, index columns, and summary tables.

Statistics and Probability (Self-paced)

Information of Statistics

What is statistics?, How is this useful, What is this course for

Data Conversion

Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data

Terms of Statistics

Descriptive statistics, Let us understand some terms in statistics, Variable


Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots

Statistics & Probability

What is probability?, Set & rules of probability, Bayes Theorem


Probability Distributions, Few Examples, Student T- Distribution, Sampling Distribution, Student t- Distribution, Poison distribution


Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling

Tables & Analysis

Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( Chi-Square ), Analysis of Variance, Correlation between Nominal variables

Statistics and Probability Project

Project – Data Analysis Project

Data – Sales

Problem Statement – It includes the following actions:

Understand the business solutions, Discussion with the warehouse team, Data Collection & Storage, Data Cleaning, Build a Hypothesis Tree around the business problem, Produce the final result.

Advanced Excel (Self-paced)

Entering Data

Introduction to Excel spreadsheet, learning to enter data, filling of series and custom fill list, editing and deleting fields.

Referencing in Formulas

Learning about relative and absolute referencing, the concept of relative formulae, the issues in relative formulae, creating of absolute and mixed references and various other formulae.

Name Range

Creating names range, using names in new formulae, working with the name box, selecting range, names from a selection, pasting names in formulae, selecting names and working with Name Manager.

Understanding Logical Functions

the various logical functions in Excel, the If function for calculating values and displaying text, nested If functions, VLookUp and IFError functions.

Getting started with Conditional Formatting

Learning about conditional formatting, the options for formatting cells, various operations with icon sets, data bars and color scales, creating and modifying sparklines.

Advanced-level Validation

multi-level drop down validation, restricting value from list only, learning about error messages and cell drop down.

Important Formulas in Excel

Introduction to the various formulae in Excel like Sum, SumIF & SumIFs, Count, CountA, CountIF and CountBlank, Networkdays, Networkdays International, Today & Now function, Trim (Eliminating undesirable spaces), Concatenate (Consolidating columns)

Working with Dynamic table

Introduction to dynamic table in Excel, data conversion, table conversion, tables for charts and VLOOKUP.

Data Sorting

Sorting in Excel, various types of sorting including, alphabetical, numerical, row, multiple column, working with paste special, hyperlinking and using subtotal.

Data Filtering

The concept of data filtering, understanding compound filter and its creation, removing of filter, using custom filter and multiple value filters, working with wildcards.

Chart Creation

Creation of Charts in Excel, performing operations in embedded chart, modifying, resizing, and dragging of chart.

Various Techniques of Charting

Introduction to the various types of charting techniques, creating titles for charts, axes, learning about data labels, displaying data tables, modifying axes, displaying gridlines and inserting trendlines, textbox insertion in a chart, creating a 2-axis chart, creating combination chart.

Pivot Tables in Excel

The concept of Pivot tables in Excel, report filtering, shell creation, working with Pivot for calculations, formatting of reports, dynamic range assigning, the slicers and creating of slicers.

Ensuring Data and File Security

Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.

Getting started with VBA Macros

Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing action with Sub and ending Sub if condition not met.

Ranges and Worksheet in VBA

Learning about the concepts of workbooks and worksheets in Excel, protection of macro codes, range coding, declaring a variable, the concept of Pivot Table in VBA, introduction to arrays, user forms, getting to know how to work with databases within Excel.

IF condition

Learning how the If condition works and knowing how to apply it in various scenarios, working with multiple Ifs in Macro, The concept of message box in VBA, learning to create the message box, various types of message boxes, the IF condition as related to message boxes.

Loops in VBA

Understanding the concept of looping, deploying looping in VBA Macros.

Debugging in VBA

Studying about debugging in VBA, the various steps of debugging like running, breaking, resetting, understanding breakpoints and way to mark it, the code for debugging and code commenting.

Dashboard Visualization

Introduction to powerful data visualization with Excel Dashboard, important points to consider while designing the dashboards like loading the data, managing data and linking the data to tables and charts, creating Reports using dashboard features, Learning to create Dashboards, the various rules to follow while creating Dashboards, creation of dynamic dashboards, knowing what is data layout, introduction to thermometer chart and its creation, how to use alerts in the Dashboard setup, Understanding data quality issues in Excel, linking of data, consolidating and merging data, working with dashboards for Excel Pivot Tables.

Principles of Charting

Learning to create charts in Excel, the various charts available, the steps to successfully build a chart, personalization of charts, formatting and updating features, various special charts for Excel dashboards, understanding how to choose the right chart for the right data, How to insert a Scroll bar to a data window?, Concept of Option buttons in a chart, Use of combo box drop-down, List box control Usage, How to use Checkbox Control?

Getting started with Pivot Tables

Creation of Pivot Tables in Excel, learning to change the Pivot Table layout, generating Reports, the methodology of grouping and ungrouping of data.

Statistics with Excel


What projects will I be working on in this Excel certification training?

Project – if Function

Data – Employee

Problem Statement – It describes about if function and how to implement this if function. It includes following actions:

Calculates Bonus for all employee at 10% of their salary using if Function, Rate the salesman based on the sales and the rating scale., Find the number of times “3” is repeated in the table and find the number of values greater than 5 using Count Function, Uses of Operators and nested if function

SAS (Self-paced)

Introduction to SAS

Installation and introduction to SAS, how to get started with SAS, understanding different SAS windows, how to work with data sets, various SAS windows like output, search, editor, log and explorer and understanding the SAS functions, which are various library types and programming files

SAS Enterprise Guide

How to import and export raw data files, how to read and subset the data sets, different statements like SET, MERGE and WHERE

Hands-on Exercise: How to import the Excel file in the workspace and how to read data and export the workspace to save data

SAS Operators and Functions

Different SAS operators like logical, comparison and arithmetic, deploying different SAS functions like Character, Numeric, Is Null, Contains, Like and Input/Output, along with the conditional statements like If/Else, Do While, Do Until and so on

Hands-on Exercise: Performing operations using the SAS functions and logical and arithmetic operations

Compilation and Execution

Understanding about input buffer, PDV (backend) and learning what is Missover

Using Variables

Defining and using KEEP and DROP statements, apply these statements and formats and labels in SAS

Hands-on Exercise: Use KEEP and DROP statements

Creation and Compilation of SAS Data Sets

Understanding the delimiter, dataline rules, DLM, delimiter DSD, raw data files and execution and list input for standard data

Hands-on Exercise: Use delimiter rules on raw data files

SAS Procedures

Various SAS standard procedures built-in for popular programs: PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, PROC CORR, etc.

Hands-on Exercise: Use SORT, FREQ, SUMMARY, EXPORT and other procedures

Input Statement and Formatted Input

Reading standard and non-standard numeric inputs with formatted inputs, column pointer controls, controlling while a record loads, line pointer control/absolute line pointer control, single trailing, multiple IN and OUT statements, dataline statement and rules, list input method and comparing single trailing and double trailing

Hands-on Exercise: Read standard and non-standard numeric inputs with formatted inputs, control while a record loads, control a line pointer and write multiple IN and OUT statements

SAS Format

SAS Format statements: standard and user-written, associating a format with a variable, working with SAS Format, deploying it on PROC data sets and comparing ATTRIB and Format statements

Hands-on Exercise: Format a variable, deploy format rule on PROC data set and use ATTRIB statement

SAS Graphs

Understanding PROC GCHART, various graphs, bar charts: pie, bar and 3D and plotting variables with PROC GPLOT

Hands-on Exercise: Plot graphs using PROC GPLOT and display charts using PROC GCHART

Interactive Data Processing

SAS advanced data discovery and visualization, point-and-click analytics capabilities and powerful reporting tools

Data Transformation Function

Character functions, numeric functions and converting variable type

Hands-on Exercise: Use functions in data transformation

Output Delivery System (ODS)

Introduction to ODS, data optimization and how to generate files (rtf, pdf, html and doc) using SAS

Hands-on Exercise: Optimize data and generate rtf, pdf, html and doc files

SAS Macros

Macro Syntax, macro variables, positional parameters in a macro and macro step

Hands-on Exercise: Write a macro and use positional parameters


SQL statements in SAS, SELECT, CASE, JOIN and UNION and sorting data

Hands-on Exercise: Create SQL query to select and add a condition and use a CASE in select query

Advanced Base SAS

Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval and descriptive statistics

Hands-on Exercise: Use web UI to do statistical operations

Summarization Reports

Report enhancement, global statements, user-defined formats, PROC SORT, ODS destinations, ODS listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT and PROC PRINT

Hands-on Exercise: Use PROC SORT to sort the results, list ODS, find mean using PROC Means and print using PROC PRINT

SAS Projects

Categorization of Patients Based on the Count of Drugs for Their Therapy

This project aims to find out descriptive statistics and subset for specific clinical data problems. It will give them brief insight about Base SAS procedures and data steps.

Build Revenue Projections Reports

You will be working with the SAS data analytics and business intelligence tool. You will get to work on the data entered in a business enterprise setup and will aggregate, retrieve, and manage that data. Create insightful reports and graphs and come up with statistical and mathematical analysis to predict revenue projection.

Impact of Pre-paid Plans on the Preferences of Investors

This project aims to find the most impacting factors in the preferences of the pre-paid model. The project also identifies which variables are highly correlated with impacting factors. In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale.

k-means cluster Analysis on Iris Dataset

In this project, you will be required to do k-means cluster analysis on an Iris dataset to predict the class of a flower using the dimensions of its petals.