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Description

Business Analytics
is a systematic and methodical presentation of data of any organization which is driven out of statistical and numerical analysis. Business analytics is a method which is used by the majority of companies today in order to make decision making effective and efficient. Thus, Business analytics course is a course which helps the students to learn the need and use of business analytics.
Business Analytics
serves as a systematic process which is a combination of data analytics and Business Intelligence on the basis of which companies rely on their process of management, I.e. Planning for future goals, controlling the combination of capital and manpower, organizing the process in an efficient way possible, coping up with future-oriented goals and making analysis on the basis of Business Analytics.
Business Analytics
is a continuous process which involves collection, processing and analysing the business data and operations, and with the help of statistical models and ideologies, transforming the outcome into business insights. The insights lead to improve the efficiency of the organization to achieve its objectives and early adaption of prone to complications. So, Business Analytics is a systematic presentation of data through which the company achieve their goals and get first-mover advantage in the industry.
Business Analytics
prepares students In developing their skills in making business strategies and formulating plans of how to conduct a systematic analysis of organization data and evaluating the best possible outcome that will lead to achieving organizational objectives. Business Analytics is a subset of Business intelligence and specifically focuses on implementing the identified goals into actions. Business intelligence is generally descriptive in nature which provides tools and methods to identify, categorize and analyse raw data and compares with past and current operations.
Who this course is for:
Candidates interested in business analytics
Graduate Or Postgraduates
Working Professionals
Business Analyst
IT Professionals
Managers

What you'll learn

Business Analytics

Career in Analytics

Population and Sample

Center Tendency

Percentile

Distribution

Organizing Data

Bubble Chart

Excel Pivots

Correlation

Regression

Hypothesis Testing

Multiple Linear Regression

Multicollinearity

Logistic Regression

Segmentation

Clusters

Decision Trees

Time series Analysis & Forecasting

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction
7
1.1-Introduction - What is Analytics, Benefits from Analytics
1.2-Analytics vs Reporting, Analytics Use Cases Part 1
1.3-Analytics vs Reporting, Analytics Use Cases Part 2
1.4-Analytics Case Studies Part 1
1.5-Analytics Case Studies Part 2
1.6-Analytics Emerging Trends
1.7-Career in Analytics
2-Statistics for Business Analytics
6
2.1-Introduction to Statistics
2.2-Population and Sample, Descriptive and Inferential Statistics
2.3-Central Tendency and Dispersion Measures Part 1
2.4-Percentile Concepts
2.5-Exercises using Excel
2.6-Distributions (Normal, Skewed and Uniform)
3-Organizing and Graphing Data
6
3.1-Organizing and Graphing Data
3.2-Bubble Chart
3.3-Assignments 1 and 2
3.4-Excel Pivots Part 1
3.5-Excel Pivots Part 2
3.6-Assignment 3
4-Correlation and Regression
5
4.1-Correlation Concepts and Exercises Part 1
4.2-Correlation Concepts and Exercises Part 2
4.3-Regression Concepts and Exercises Part 1
4.4-Regression Concepts and Exercises Part 2
4.5-Assignments 4 and 5
5-Hypothesis Testing
3
5.1-Re-cap concepts of Dispersion
5.2-Hypothesis Testing Concepts with Examples Part 1
5.3-Hypothesis Testing Concepts with Examples Part 2
6-Multiple Regression
4
6.1-Multiple Regression through Exercises Part 1
6.2-Multiple Regression through Exercises Part 2
6.3-Multicollinearity Concept & its impact
6.4-Assignment 6
7-Logistic Regression
2
7.1-Logistic Regression
7.2-Logistic Regression Part 2
8-Segmentation
4
8.1-Introduction to Segmentation
8.2-Segmentation Techniques (Cluster Analysis) Part 1
8.3-Segmentation Techniques (Cluster Analysis) Part 2
8.4-Segmentation Techniques (Decision Tree)
9-Time Series Analysis and Forecasting
5
9.1-Introduction to Time Series
9.2-Forecasting Techniques - Moving Average & Exponential Smoothing Part 1
9.3-Forecasting Techniques - Moving Average & Exponential Smoothing Part 2
9.4-Forecasting Techniques - Double and Triple Exponential Smoothing Part 1
9.5-Forecasting Techniques - Double and Triple Exponential Smoothing Part 2
10-Assignment Solutions
6
10.1-Assignment 1
10.2-Assignment 2
10.3-Assignment 3
10.4-Assignment 4
10.5-Assignment 5
10.6-Assignment 6