Business Analytics Online Course Program

58,512

This MS in Business Analytics course is explicitly intended to sharpen your insight and mastery in various Business Analytics devices and innovations, like Agile Scrum, Data Analytics, technique examination, arrangement assessment, Scrum jobs, rotate tables, and other huge ideas.

What topics will you learn in this Business Analytics program designed by Edutech Skills?

You will learn the following topics in this Business Analytics course:

  • Introduction to analytics
  • Business finance
  • Marketing and CRM
  • SQL programming
  • Statistical methods for decision-making
  • Advanced statistics
  • Optimization techniques
  • Predictive modeling 
  • Data mining
  • Machine Learning
  • Time-series forecasting
  • Web and social media analytics

Why should you sign up for Edutech Skills Business Analytics classes?

You should sign up for this online course because: 

  • There are over 50,000 Business Analytics jobs listed in the USA – LinkedIn
  • 12,000+ job openings are available for Business Analysts in India – LinkedIn
  • US$79,200 is the average annual pay of Business Analysts in the USA – Indeed

Who should enroll in the Business Analytics course by Edutech Skills?

The following individuals can sign up for the best Business Analytics online course:

  • Business Intelligence Experts
  • Business Objects and ETL Professionals
  • SQL Developers
  • Database Architects
  • Software Developers
  • Project Managers

Are there any prerequisites to take up this Business Analytics certification course by Edutech Skills?

No, but a basic understanding of SQL will accelerate your learning.

Data Science with R

Introduction to R

R language for statistical programming, various features of R, introduction to RStudio, statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of RStudio like code editor, visualization and debugging tools and learn about R-bind

R Packages

R functions, code compilation and data in well-defined format called R Packages, R Package structure, package metadata and testing, CRAN (Comprehensive R Archive Network), vector creation and variables values assignment

Sorting DataFrame

R functionality, Rep function, generating repeats, sorting and generating factor levels, transpose and stack function

Matrices and Vectors

Introduction to matrix and vector in R, understanding various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions

Reading Data from External Files

Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists and understanding how to read data from external files

Generating Plots

Generate plots in R, graphs, bar plots, line plots, histograms and components of a pie chart

Analysis of Variance (ANOVA)

Understanding analysis of variance (ANOVA) statistical technique, working with pie charts and histograms and deploying ANOVA with R, one-way ANOVA and two-way ANOVA

K-Means Clustering

K-Means clustering for cluster and affinity analysis, cluster algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships

Association Rule Mining

Introduction to Association Rule Mining, various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, algorithm and rules of Association Rule Mining and understanding single cardinality

Regression in R

Understanding what is simple linear regression, various equations of line, slope, Y-intercept regression line, deploying analysis using regression, the least square criterion, interpreting the results and standard error to estimate and measure of variation

Analyzing Relationship with Regression

Scatter plots, two-variable relationship, simple regression analysis and line of best fit

Advanced Regression

Deep understanding of the measure of variation, the concept of co-efficient of determination, F-test, the test statistic with an F-distribution, advanced regression in R and prediction linear regression

Logistic Regression

Logistic regression mean and logistic regression in R

Advanced Logistic Regression

Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring if the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system and ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier

Receiver Operating Characteristic (ROC)

Detailed understanding of ROC, area under ROC curve, converting the variable, data set partitioning, understanding how to check for multicollinearity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix and deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates

Kolmogorov–Smirnov Chart

Data analysis with R, understanding the Wald test, MC Fadden’s pseudo R-squared, the significance of the area under ROC curve, Kolmogorov–Smirnov chart which is a non-parametric test of one-dimensional probability distribution

Database Connectivity with R

Connecting to various databases from the R environment, deploying the ODBC tables for reading the data and visualization of the performance of the algorithm using confusion matrix

Integrating R with Hadoop

Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop integrated programming environment and R programming for MapReduce jobs and Hadoop execution

R Case Studies

Logistic Regression Case Study

In this case study, you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns and uncover insights and more, all through the power of R programming. Due to this, the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on various parameters. You will deploy multiple regression and note down the MPG for the car make, model, speed, load conditions, etc. It includes the model building, model diagnostic and checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) Case Study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real-world data, check the ROC curve and more.

What projects I will be working on this R Programming training?

Project 1

Domain: Restaurant Revenue Prediction

Data set: Sales

Project Description: This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real-time industry experience on handling multiple use cases and deriving the solutions. This project gives insights about feature engineering and selection.

Project 2

Domain: Data Analytics

Objective: The project is meant to predict the class of a flower using its petal’s dimensions.

Project 3

Domain: Finance

Objective: The project aims to find the most impacting factors in the preferences of pre-paid model and to identify which all are the variables highly correlated with impacting factors.

Project 4

Domain: Stock Market

Objective: This project focuses on Machine Learning by creating predictive data model to predict future stock prices.

Python for Data Science

Module 01 – Introduction to Data Science using Python

1.1 What is Data Science, what does a data scientist do
1.2 Various examples of Data Science in the industries
1.3 How Python is deployed for Data Science applications
1.4 Various steps in Data Science process like data wrangling, data exploration and selecting the model.
1.5 Introduction to Python programming language
1.6 Important Python features, how is Python different from other programming languages
1.7 Python installation, Anaconda Python distribution for Windows, Linux and Mac
1.8 How to run a sample Python script, Python IDE working mechanism
1.9 Running some Python basic commands
1.10 Python variables, data types and keywords.

Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac

Module 02 – Python basic constructs

2.1 Introduction to a basic construct in Python
2.2 Understanding indentation like tabs and spaces
2.3 Python built-in data types
2.4 Basic operators in Python
2.5 Loop and control statements like break, if, for, continue, else, range() and more.

Hands-on Exercise –
1.Write your first Python program
2. Write a Python function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. Create an object and write a for loop to print all odd numbers

Module 03 – Maths for DS-Statistics & Probability

3.1 Central Tendency
3.2 Variability
3.3 Hypothesis Testing
3.4 Anova
3.5 Correlation
3.6 Regression
3.7 Probability Definitions and Notation
3.8 Joint Probabilities
3.9 The Sum Rule, Conditional Probability, and the Product Rule
3.10 Bayes Theorem

Hands-on Exercise –

1. We will analyze both categorical data and quantitative data
2. Focusing on specific case studies to help solidify the week’s statistical concepts

Module 04 – OOPs in Python (Self paced)

4.1 Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
4.2 What are access modifiers, instances, class members
4.3 Classes and objects
4.4 Function parameter and return type functions
4.5 Lambda expressions.

Hands-on Exercise –
1. Writing a Python program and incorporating the OOP concepts

Module 05 – NumPy for mathematical computing

5.1 Introduction to mathematical computing in Python
5.2 What are arrays and matrices, array indexing, array math, Inspecting a NumPy array, NumPy array manipulation

Hands-on Exercise –

1. How to import NumPy module
2. Creating array using ND-array
3. Calculating standard deviation on array of numbers and calculating correlation between two variables.

Module 06 – SciPy for scientific computing

6.1 Introduction to SciPy, building on top of NumPy
6.2 What are the characteristics of SciPy
6.3 Various sub-packages for SciPy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more, Bayes Theorem with SciPy.

Hands-on Exercise:

1. Importing of SciPy
2. Applying the Bayes theorem on the given dataset.

Module 07 – Data manipulation

7.1 What is a data Manipulation. Using Pandas library
7.2 NumPy dependency of Pandas library
7.3 Series object in pandas
7.4 DataFrame in Pandas
7.5 Loading and handling data with Pandas
7.6 How to merge data objects
7.7 Concatenation and various types of joins on data objects, exploring dataset

Hands-on Exercise –

1. Doing data manipulation with Pandas by handling tabular datasets that includes variable types like float, integer, double and others.
2. Cleaning dataset, Manipulating dataset, Visualizing dataset

Module 08 – Data visualization with Matplotlib

8.1 Introduction to Matplotlib
8.2 Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more
8.3 Matplotlib API

Hands-on Exercise –
1. Deploying Matplotlib for creating pie, scatter, line and histogram.
2. Subplots and Pandas built-in data visualization.

Module 09 – Machine Learning using Python

9.1 Revision of topics in Python (Pandas, Matplotlib, NumPy, scikit-Learn)
9.2 Introduction to machine learning
9.3 Need of Machine learning
9.4 Types of machine learning and workflow of Machine Learning
9.5 Uses Cases in Machine Learning, its various algorithms
9.6 What is supervised learning
9.7 What is Unsupervised Learning

Hands-on Exercise –

1. Demo on ML algorithms

Module 10 – Supervised learning

10.1 What is linear regression
10.2 Step by step calculation of Linear Regression
10.3 Linear regression in Python
10.4 Logistic Regression
10.5 What is classification
10.6 Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier (Self paced), Support Vector Machine(self paced), XGBoost (self paced)

Hands-on Exercise – Using Python library Scikit-Learn for coming up with Random Forest algorithm to implement supervised learning.

Module 11 – Unsupervised Learning

11.1 Introduction to unsupervised learning
11.2 Use cases of unsupervised learning
11.3 What is clustering
11.4 Types of clustering(self-paced)-Exclusive clustering, Overlapping Clustering, Hierarchical Clustering(self-paced)
11.5 What is K-means clustering
11.6 Step by step calculation of k-means algorithm
11.7 Association Rule Mining(self-paced), Market Basket Analysis(self-paced), Measures in association rule mining(self-paced)-support, confidence, lift
11.8 Apriori Algorithm

Hands-on Exercise –
1. Setting up the Jupyter notebook environment
2. Loading of a dataset in Jupyter
3. Algorithms in Scikit-Learn package for performing Machine Learning techniques and training a model to search a grid.
4. Practice on k-means using Scikit
5. Practice on Apriori

Module 12 – Python integration with Spark (Self paced)

12.1 Introduction to PySpark
12.2 Who uses PySpark, need of spark with python
12.3 PySpark installation
12.4 PySpark fundamentals
12.5 Advantage over MapReduce, PySpark
12.6 Use-cases PySpark and demo.

Hands-on Exercise:
1. Demonstrating Loops and Conditional Statements
2. Tuple – related operations, properties, list, etc.
3. List – operations, related properties
4. Set – properties, associated operations, dictionary – operations, related properties.

Module 13 – Dimensionality Reduction

13.1 Introduction to Dimensionality
13.2 Why Dimensionality Reduction
13.3 PCA
13.4 Factor Analysis
13.5 LDA

Hands-on Exercise –
Practice Dimensionality reduction Techniques : PCA, Factor Analysis, t-SNE, Random Forest, Forward and Backward feature

Module 14 – Time Series Forecasting

14.1 White Noise
14.2 AR model
14.3 MA model
14.4 ARMA model
14.5 ARIMA model
14.6 Stationarity
14.7 ACF & PACF

Hands-on Exercise –
1. Create AR model
2. Create MA model
3. Create ARMA model

Data Science with Python Projects

Analyzing the Trends of COVID-19 With Python

In this project, you will use Pandas to accumulate data from multiple data files, Plotly (visualization library) to create interactive visualizations, and Facebook’s Prophet library to make time series models. You will also be visualizing the prediction by combining these technologies.

Analyzing the Naming Trends Using Python

In this project, you will use Python Programming and Algorithms to understand the applications of data manipulation, extract files with useful data only, and concepts of data visualization. You will be required to analyze baby names by sorting out the top 100 birth counts.

Performing Analysis on Customer Churn Dataset

Through this project, you will be analyzing employment reliability in the telecom industry. The project will require you to work on real-time analysis of data with multiple labels, data visualization for reliability factor, visual analysis of various columns to verify, and plotting charts to substantiate the findings in total.

Netflix-Recommendation System

Analysis of movies dataset and recommendation of movies with respect to ratings. You will be working with the combined data of movies and their ratings, performing data analysis on various labels in the data, finding the distribution of different ratings in the dataset, and training the SVD for the prediction of the model.

Python Web Scraping for Data Science

In this project, you will learn web scraping using Python. You will work on Beautiful Soup, web scraping libraries, common data and page format on the web, the important kinds of objects, Navigable String, the searching tree deployment, navigation options, parser, search tree, searching by CSS class, list, function, and keyword argument.

OOPS in Python

Creating multiple methods using OOPS. You will work on methods like “check_balance’ to check the remaining balance in an account, “withdraw” to withdraw from the bank, find the distribution of different ratings in the dataset, and override the “withdraw” to ensure that the minimum balance is maintained. You will also work with Parameterization and Classes.

Working With NumPy

In this case study, you will be working with the NumPy library to solve various problems in Python. You will create 2D arrays, initialize a NumPy array of 5*5 dimensions, and perform simple arithmetic operations on the two arrays. To carry out this case study successfully, you will have to be familiar with NumPy.

Visualizing and Analyzing the Customer Churn dataset using Python

This case study will require you to analyze data by building aesthetic graphs to make better sense of the data. You will be working with the ggplot2 package, bar plots and its applications, histogram graphs for data analysis, and box-plots and outliers in them.

Building Models With the Help of Machine Learning Algorithms

You will be designing tree-based models on the ‘Heart’ dataset, performing real-time data manipulation on the heart dataset, data-visualization for multiple columnar data, building a tree-based model on top of the database, and designing a probabilistic classification model on the database. You will have to be familiar with ML Algorithms.

Tableau Desktop 10

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