Business Data Analytics with R – LVC

BUSINESS DATA ANALYTICS is the practice of iterative, methodical exploration of an organization’s data, with an emphasis on statistical analysis. Business data analytics is used by companies committed to data-driven decision-making.

Business Data Analytics or Business Analytics is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage.

Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.

Business Data Analytics techniques break down into two main areas.

The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.

The second area of business analytics involves deeper statistical analysis. This may mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns, for example.

Specific types of business analytics include:

Descriptive analytics, which tracks key performance indicators to understand the present state of a business;
Predictive analytics, which analyzes trend data to assess the likelihood of future outcomes; and
Prescriptive analytics, which uses past performance to generate recommendations about how to handle similar situations in the future.
In this program, you will learn the nuances of data collection, data presentation, and model building using real-life data sets. You will learn how to build supervised and unsupervised machine learning models, you will be introduced to algorithms to solve classification and segmentation problems. We will also introduce you to R platform, and different algorithms which can be used in the model building activity.

At the end of the program you will develop a clear understanding of the need for business analytics and will be able to apply it to solve some interesting problems cutting across various business domains.

This training program focuses on Forecasting, Econometrics and Time Series Analyze and predict future outcomes based on historical patterns.

It is one of the marquee courses that provides you a competitive edge over others and a means to be ahead of your peers. It makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling and fact-based management to drive decision making.

EVENT CONTENT

Module 1

  • Analytics v/s Analysis
  • Types of Analytics, Business domains within Analytics
  • Types of Data Variables & Summarizing Data
  • Central Tendency, Symmetry and Skewness
  • Random Variables, Probability Distribution, Central Limit Theorem
  • Sampling and Statistical inference, Confidence Intervals
  • Case Study 1: Banking Sector Credit Card Department

Module 2

  • Day 1 Re-cap
  • Hypothesis Testing
  • Analysis of Variance
  • Introduction to Tools and Software commonly used in Analytics
  • Multivariate Linear Regression Theory
  • Continuation of Multivariate Linear Regression Theory

Module 3

  • Multivariate Linear Regression (Using Excel and R)
  • Logistic Regression (Using R)
  • Case Study 2 : Auto Insurance Company, Banking Sector Credit Card Department
  • Case Study 3: Auto Insurance Company, Banking Sector Credit Card Department
  • Continuation of Case Study 3 : Auto Insurance Company, Banking Sector Credit Card Department

Module 4

  • Models of time series : Moving Averages and Autoregressive Models
  • Model Estimation , Model Validation , Model forecasting
  • Identification of ARIMA Model & Estimation of Best ARIMA models
  • Validation of Model and Forecasting Sales
  • Case Study 4: Sales Forecasting of Automobile company
  • Continuation of Case Study 4: Sales Forecasting of Automobile company
  • Summarization, Q&A

Training Duration: 8 Days, 4 Hours per day

Training Timing: 11:00 AM to 3:00 PM GMT (Click here to view timing in your geography)

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Module 1: Introduction to Analytics

Data & Basic Statistics

1
Analytics v/s Analysis
2
Types of Analytics, Business domains within Analytics
3
Types of Data Variables & Summarizing Data
4
Central Tendency, Symmetry and Skewness
5
Random Variables, Probability Distribution, Central Limit Theorem
6
Sampling and Statistical inference, Confidence Intervals
7
Case Study 1: Banking Sector Credit Card Department

Module 2: Basic Statistics & Predictive Modeling

1
Hypothesis Testing & Analysis of Variance
2
Introduction to Tools and Software commonly used in Analytics
3
Multivariate Linear Regression Theory
4
Multivariate Linear Regression (Using Excel and R)

Module 3: Predictive Modeling & Forecasting

1
Logistic Regression (Using R)
2
Case Study 2:Case: Auto Insurance Company, Banking Sector Credit Card Department
3
Case Study 3: Auto Insurance Company, Banking Sector Credit Card Department

Module 4: Time Series Modeling

1
Models of time series : Moving averages & Autoregressive Models
2
Model Estimation , Model Validation & Model forecasting

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Enrolled: 278 students
Duration: 8 Weeks
Lectures: 16
Video: 32 Hours

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Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed