Overview of Business Analyst
Overview of Data Science
Introduction to Data Analysis
Data Analysis in R
Basic Statistics dataset in R
Univariate Statistics dataset in R
Theoretical testing in R
S&P- 500 Indices
Marks and Hours study Relationship
Finding Insurance Damages
Market Basket Analysis
Time Series Modelling
ARIMA in R
Relax & Revise
Data Science Course
Data Science course is designed to help meet the expanding needs for these "Data Scientists", who are skilled in the utilization of a unique blend of Science, Art and Business. Students will learn to combine tools and techniques from statistics, computer science, data visualization and the social sciences to solve problems using data.
Data Visualization Course
Data Visualization course is designed to help you understand and use the important concepts and techniques to move from simple to complex visualizations and learn how to combine them in interactive dashboards.
Business Analytics Course
Business analytics certification program will provide you with the edge when you look at the competitive market. With Business Analytics training course, you are able to extract useful information from scores of bytes in a few minutes.
Business Analytics Course in Delhi -Fees modules & Other Details
Business Analytics Course Modules:
1. Overview of Business Analyst and Data Science.
2. Introduction to Data Analysis and preparing data for analysis in R Language
• Understanding the concept of R and learn to work with R Software
• Studying Credit Card Data using R
• Learn different type of Credit Card data
• Learn salary calculation of credit card customers
• Operation in R: basic operation, string operation, vectors in R
• Lists and data frames in R
• Outlining the data of Credit Card
• Maintaining, planning and Stuffing data in R
3. Basic Statistics and Univariate Statistics on the Credit Card dataset in R
• Creating # of customers, # of data
• Learn to determine possibilities of # of Credit Cards of users
• Learn to distribute the salary of users
• Determine the confidence interval of customers
• Learn Central Limit Theorem: implementation of confidence of customers on sample
4. Theoretical testing in R for Education dataset
• Determining region of acceptance and rejection
• Learn to calculate alpha and P-value
• Learn to calculate error in p1 and p2
• Learn to score more than 80 % of marks
• Learn Analysis of Variance- ANOVA
• Learn R chi-squared test
5. Learn about NASDAQ-100 and S&P- 500 Indices
• Learn to find out the difference between NASDAQ- 100 and S&P-500
• Learn to validate relationship between NASDAQ- 100 and S&P-500
6. Relationship between marks and hours of study
• Learn to predict # of marks based on # of marks
• Learn simple regression
• Understanding linear aggression
• Sample linear regression
• Understanding R square of marks
7. Learn the techniques of finding insurance damages of new client
• Learning statement of dimension and data
• Creating dummy variables
• Learn random sampling method: used for sampling of data
• Data sets: training, testing and validating
• Learn regression and its different model
• Learn R's inbuilt function
• Learn and visualizing multicollinearity
• Learn techniques to remove p- values and multicollinearity
• ANOVA and methods of its creation
• ANOVA and p- value
• R square: finding, decoding
• R square and multicollinearity
• Linear Regression presumptions
• Heteroscedasticity, BP test
8. Understanding of Logistic Regression
• Logit Equation and Hypothesis
• Conversion of Sigmoid function
• Cut-off matrix
• Logit Model
• Understanding KS Cut-off, F Beta Cut- off, ROC Curves, AUC
• Model for creating Gain, Lift Chart and Concordance
9. Algorithms: Supervised and Unsupervised
• Supervised Algorithm- Cleaning of mobile spam
• Naive Bayes model
• Laplace Estimator Approach
• Algorithm for Insurance Losses: Random Forest Algorithm
• Unsupervised Algorithm
• K-means Clustering
10. Analysis of market basket
• Understanding stored transactions data
• Learn statement of problem and dimensions
• Apriori Algorithm : understanding, techniques of calculating, observing, inspecting and finding output
11. Time Series Modelling
• Understanding Time series Analytics in R
• SMA (Simple Moving Average) method: Learning, methods of calculating, automating
• Understanding MAPE and RMSE
• WMA (Weighted Moving Average): Calculating and automating method
• SES (Single Exponential Smoothing)
• DES (Double Exponential Smoothing): Understanding and calculating
• TES (Triple Exponential Smoothing): Understanding and calculating
12. ARIMA in R
• AR (Autoregressive) and MA (Moving Averages) processes
• ARMA models: Combination of AR & MA
• ARMA to ARIMA
• Learn Dickey Fuller Test
• Box Jenkins model (ACFs and PACFs)
13. Understanding Ridge Regression
• Comparison of Ridge Regression with Linear Regression
• Cost function in Ridge Regression: Understanding and Calculating
• Lambda with Ridge Regression
14. Understanding Neural Network
• Back Propagation, Neural Network
• Nodes of Neural Network
• Network and layer of Neural Network
• Practising of Neural Network
• Performance optimization of Neural Network model
What Is Business Analytics?
Business analytics means the knowledge, ability and practices for repetitive exploration of past business performance to seek information and to make business planning accordingly. It understands the performance of business with the help of data and statistical method. Traditionally, business intelligence aims at using the set of metrics to measure the past performance and helps in further business planning which is based on data and statistical technique.
The business analytics consist of stages or spectrum which includes the initial stage with descriptive analysis, reporting, detective analysis, dashboards, predictive analysis, and big data. These all stages are described step by step by measuring the complexity and business value on two different scale.
1. Descriptive Analytics-
This spectrum is the initial stage which helps to understand the questions which comprises how many, how often, who ,when and other counting related questions . Questions which describe the data are aggregated and divided into various cuts which are done through descriptive analytics. The main objective is to understand what data is telling about before knowing the dimensions and task which consist of numbers in different forms. Pivot tables is the example of this. This type of analysis involves great amount of analytics and such task are often automated and may not be large by the human attempt.
The next stage of analytics starts from first question that what happened?, what to do? And other reason aimed questions. Hypothesis testing is the main objective to find out the explanations of questions for observed data. There are major tools which are used in reporting like MS- excel, SQL, or Oracle. Generally, all the reports are implemented with the help of MS-excel. It consist of finding the correct dimensions for integration, divisions of data and observing the patterns in the data. Through certain statistical approach and data, these questions can be understood and most of the analytics job comes in this region of spectrum.
3. Detective Analytics-
The next stage comprises of the question including why did it happen? The unexpected changes are explained in this stage like why did the revenue is not increasing from past 3 months? Or Why did the latest project was not performed or over-performed? These questions can be understood with the help of finding past performance by analyzing the differences from past trends. There are certain tools like MS-excel , MS-access, Minitab. It generally needs advance excel and time series graphs while dealing with the above questions.
This spectrum consists of a well designed and presented synopsis for key business standards. It contains the pictorial images which helps the user to understand the accurate information. This is also known as ‘Business Intelligence’ as it is a traditional approach of creating data models, reports and dashboards on the data. Advance excel can be used to implement for small size of data. Although, many organizations uses the high end software like hyperion.
5. Predictive Analytics-
It consist of stage where the question is comprises of what if, who,will and other upcoming related questions. This stage is the combination of past historical trends and the information are gathered to anticipate the future. Through in this analysis, you have to predict the behavior of customer on the basis of past information. SPSS, R ,SAS are the common tools for predictive models and it involves the skills in which individual should have the ability to solve the problem with highly understanding of business.
5. Prescriptive Analytics-
In prior stage, the role of predictive analytics is to integrate information to anticipate future but not suggest any action. But prescriptive analytics goes beyond the anticipation by suggesting actions in terms of business needs and objectives. Therefore it is the combination of descriptive and predictive analytics. It is referred to as “final frontier of analytic capabilities” because it is the final stage of the business analytics. It requires the mathematical and computational science and recommends decision to benefit from the result of descriptive and predictive analytics.
Business analytics has become a bit of fuzzword, but it plays a pivotal role in any business. It includes the large portion of decision support system, continuous improvement programs and many other tactics to make the business competitive in market. The efficient business analytics require the first step like capacity utilization and other measure to properly implement these techniques. There are tools included while doing analytics like use of SPSS (Statistical Package for Social Science) software, R, MS- excel, MS-access, SAS, and other tools and software.
Analytics has been implemented in business from long decades. Fredrick Winslow Taylor in the late 19 century has introduced the phase of management exercises. Henry ford had observed each component in his newly introduced assembly line.
Business analytics is largely depends upon enough volume of high quality data. The main problem is to integrate and make compatible data from different system and then to determine what subsets of data to make available. Earlier, analytics was meant as a type of after-the-fact technique of forecasting consumer behavior by inspecting the numbers of units sold in the final quarter or last year. It requires a lot of more storage space to accommodate the data warehousing and also requires speed. Business analytics now has become an essential tool that can resolve the problem of data warehousing and influence the outcome of customer interactions. When a particular customer is involving in purchase, an organization with analytics can customize the sales volume to attract to the customer. Analytics enables the storage space for all the data with efficient speed to offer the data in real time.
The business analytics has been used by every organizations. Self service has become the major tool. Majority of users now desire to have software without any difficulty and does not need specialized training. With growing these users, companies such as Tableau, Qlik are using simple-to-use tools with the rising trend of these tools. This type of tools can be installed on a single computer or in server from enterprise wide deployment. Less specialized business analyst can also operate this type of tools to generate reports, charts that can track particular metrics in data sets.
Hopefully now you understand the basic concepts and fee structure of Business Analytics / Analyst Course. If you're interested doing business analysis course then you can also take a free demo class at our institute. We have Seven branches in Delhi NCR.
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