Business-Analytics-Certification-Course
Highest Rated

Business Analytics Certification

Business analytics is made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company and its performance. Business analytics are performed in order to identify weaknesses in existing processes and highlight meaningful data that will help an organization prepare for future growth and challenges.
The Business Analytics Course is designed to provide you expertise in data analytics and help you master the implementation of data exploration, visualization, hypothesis testing and other aspects of Data Science concepts. EduInPro’s Business Analytics Certification course is designed to make you industry-ready by training you to apply statistics and predictive analytics techniques in a business environment.
Below are the learning objectives of Business Analytics Certification:

  • In-depth techniques needed to work as a business analyst.
  • Learn popular tools used by the business analyst.
  • Complete preparation to work in a professional environment.
  • Master statistical and analytical tools such as hypothesis testing, clustering, decision trees, data visualization, regression models, data blending, data extracts, R Studio, ad-hoc analytics, heat map, treemap, Waterfall, Pareto, Gantt charts, and forecasting and more.

       

(12,000)
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Business Analytics Certification

Enroll Now
Catogory

Data Science & Business Intelligence

Duration Time

45 hours

Price

Rs. 2000

This Business Analytics Certification Course is created in a way that participant can start from the basics of Excel, SQL, Tableau moving to advanced tools like SAS, Python data science, including machine learning. EduInPro’s Business Analytics Course encompasses basic statistical concepts to advanced analytics and predictive modelling techniques, along with machine learning, with analytics tools, like Excel, Tableau, Python, SAS (including Proc SQL). This analytics certification course is for all those aspirants who want to switch into the field of data science and begin their career as a Business Analyst. Our Business Analytics Certification Training provides you with an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations. This course also enables you to develop an analytic mindset with basic data literacy that will help you make strategic decisions based on data. You will also work with the Project, you’ll apply your skills to interpret a real-world data set and make appropriate business strategy recommendations.

Business Analytics Certification Modules


  • Introduction to Analytics, Data Types, Levels of Measurement
  • Descriptive Statistics Measures of Central Tendency & Dispersion
  • Population, Sampling and Sample Size
  • Elementary Probability, Expectation, Variance, Correlation, and Statistical Distributions
  • Point Estimation, Interval Estimation
  • Hypothesis Testing, Type I & Type II Error, P-Value
  • Inferential Statistics: Normality Test, 1-Sample and
  • 2-Sample T-tests, HOV, 1-Way ANOVA, Moods Median Test, Chi-square test, DOE
  • 7 assignments with data sets
  • Linear & Logistic Regression – Case studies
  • Missing Value Analysis (+ Case study)
  • Multivariate Analysis – Factor & Cluster Analysis
  • Outliers & Extreme Value Analysis (+ Case study)
  • Business Forecasting – Time Series (+ Case study)
  • Optimization Techniques (+ Case study)
  • SAS interface, library structure and definition
  • Types of variables, Importing/exporting datasets, Variable attributes and data modification
  • Conditional statements to modify data – where, if and nested if; Appending, sorting and merging datasets
  • SAS functions for data manipulation; Missing values, Arrays
  • PROC contents, PROC format, PROC report and PROC tabulate
  • PROC steps for basic statistics – PROC univariate, PROC means, PROC summary etc.
  • Case studies I & II
  • SQL in SAS and data manipulation using PROC SQL, Conditional statements in SQL and aggregate functions
  • SQL joins and comparison with SAS merge
  • Case studies III & IV
  • Sorting, Filtering, Advance Filtering, Subtotal
  • Pivot Tables; Data Entry Forms
  • Sorting, Filtering, Advance Filtering, Subtotal
  • Pivot Tables; Data Entry Forms
  • Different Charts & Graphs – Which one to use and when
  • Vlookup, Hlookup, Match, Index
  • Style & Number Formatting, Conditional Formatting
  • Worksheet & Workbook Reference, Smart Printing Options
  • Logical Operators & Functions – IF and Nested IF
  • Data Validation, Text Functions, Macro
  • Automate a Task using Macro, Analysis Using VBA
  • Objects – Collection, Application, Workbook and Window Objects, Range
  • Variable, Data Types, and Constants
  • VBA Sub, Event and Function Procedures
  • Conditional logic (Conditional Statements and the If/Then/Else and Select/Case) and loop
  • Project
  • Statistical Analysis using Analysis Toolpak – Descriptive Stat, histogram, t test / z test / ANOVA, correlation, Time-series, etc.
  • Write SELECT queries
  • Query multiple tables
  • Use built-in functions
  • Use subqueries
  • What is Visualization?
  • Visualization Techniques
  • Maps and Geographic units
  • Creating interactive visualizations
  • Visualization Project
  • Creating Visualization with Tableau
  • Introduction to Tableau Workspace
  • Working with different data sources
  • Comparison with SAS
  • Interfaces, R Workspace, GUI of R studio
  • Data Types & data structures – vector, array, list, matrix, data frame
  • Functions – numeric functions, character functions, date functions etc.
  • Importing and exporting data (.csv, .xls, SAS data files, SPSS files etc.); knowing structure of data; Loading packages
  • Conditional statements and loops
  • Diagrammatic representation – Scatter Plot, Histogram, Boxplot, Bar chart, Line chart etc.
  • Plot function
  • Mean, Median, Std. deviation, Hypothesis Tests – z, t, chi, HOV, ANOVA etc.
  • Cluster and Factor Analysis
  • Regression – multiple, logistic
  • Keyword search, Sentiment Analysis
  • Assignments and Quiz
  • Concept of Big Data & Hadoop
  • HDFS, R for Hadoop, Flume, Spark
  • Resume Workshop
  • Interview Workshop
  • Project Mentoring
  • Industry Seminars
  • Job Updates

Course Outline

  • Core Analytics (Module I)
  • Analytics Fundamentals
    • Introduction to Analytics, Data Types, Levels of Measurement
    • Descriptive Statistics Measures of Central Tendency & Dispersion
    • Population, Sampling and Sample Size
    • Elementary Probability, Expectation, Variance, Correlation, and Statistical Distributions
    • Point Estimation, Interval Estimation
    • Hypothesis Testing, Type I & Type II Error, P-Value
    • Inferential Statistics: Normality Test, 1-Sample and
  • Inferential Statistics
    • 2-Sample T-tests, HOV, 1-Way ANOVA, Moods Median Test, Chi-square test, DOE
    • 7 assignments with data sets
  • Modelling
    • Linear & Logistic Regression – Case studies
    • Missing Value Analysis (+ Case study)
  • Multivariate
    • Multivariate Analysis – Factor & Cluster Analysis
  • Data Pre-processing
    • Outliers & Extreme Value Analysis (+ Case study)
  • Other Topics
    • Business Forecasting – Time Series (+ Case study)
    • Optimization Techniques (+ Case study)
  • SAS (Module II)
  • Data Structure
    • SAS interface, library structure and definition
    • Types of variables, Importing/exporting datasets, Variable attributes and data modification
  • Data Management & Reports
    • Conditional statements to modify data – where, if and nested if; Appending, sorting and merging datasets
    • SAS functions for data manipulation; Missing values, Arrays
  • Basic Proc Sets
    • PROC contents, PROC format, PROC report and PROC tabulate
    • PROC steps for basic statistics – PROC univariate, PROC means, PROC summary etc.
    • Case studies I & II
  • Proc SQL
    • SQL in SAS and data manipulation using PROC SQL, Conditional statements in SQL and aggregate functions
    • SQL joins and comparison with SAS merge
    • Case studies III & IV
  • Macros & Inferential Statistics
  • MS Excel and VBA
  • Excel Basics
    • Sorting, Filtering, Advance Filtering, Subtotal
    • Pivot Tables; Data Entry Forms
    • Sorting, Filtering, Advance Filtering, Subtotal
    • Pivot Tables; Data Entry Forms
    • Different Charts & Graphs – Which one to use and when
    • Vlookup, Hlookup, Match, Index
    • Style & Number Formatting, Conditional Formatting
    • Worksheet & Workbook Reference, Smart Printing Options
    • Logical Operators & Functions – IF and Nested IF
    • Data Validation, Text Functions, Macro
  • VBA Programing
    • Automate a Task using Macro, Analysis Using VBA
    • Objects – Collection, Application, Workbook and Window Objects, Range
    • Variable, Data Types, and Constants
    • VBA Sub, Event and Function Procedures
    • Conditional logic (Conditional Statements and the If/Then/Else and Select/Case) and loop
    • Project
  • Advanced Excel
    • Statistical Analysis using Analysis Toolpak – Descriptive Stat, histogram, t test / z test / ANOVA, correlation, Time-series, etc.
  • SQL Basics and Data Visualization with Tableau (Module IV)
  • Queries
    • Write SELECT queries
    • Query multiple tables
    • Use built-in functions
    • Use subqueries
  • Data Visualization
    • What is Visualization?
    • Visualization Techniques
    • Maps and Geographic units
    • Creating interactive visualizations
    • Visualization Project
  • Tableau
    • Creating Visualization with Tableau
    • Introduction to Tableau Workspace
    • Working with different data sources
  • R for Analytics and Overview of Big Data & Hadoop (Module V)
  • Introduction
    • Comparison with SAS
    • Interfaces, R Workspace, GUI of R studio
    • Data Types & data structures – vector, array, list, matrix, data frame
  • Data Management
    • Functions – numeric functions, character functions, date functions etc.
    • Importing and exporting data (.csv, .xls, SAS data files, SPSS files etc.); knowing structure of data; Loading packages
    • Conditional statements and loops
  • Charting
    • Diagrammatic representation – Scatter Plot, Histogram, Boxplot, Bar chart, Line chart etc.
    • Plot function
  • Statistical Functions
    • Mean, Median, Std. deviation, Hypothesis Tests – z, t, chi, HOV, ANOVA etc.
    • Cluster and Factor Analysis
    • Regression – multiple, logistic
  • Text Mining
    • Keyword search, Sentiment Analysis
    • Assignments and Quiz
  • Big Data (Overview)
    • Concept of Big Data & Hadoop
    • HDFS, R for Hadoop, Flume, Spark
  • Career Plus
    • Resume Workshop
    • Interview Workshop
    • Project Mentoring
    • Industry Seminars
    • Job Updates

What do I need to do to unlock my Eduinpro certificate?

Online Classroom:

  • Attend one complete batch
  • Successful completion and evaluation of any one of the given projects

Online Self-learning:

  • Complete 85% of the course
  • Successful completion and evaluation of any one of the given projects

Featured Review

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FREQUENTLY ASKED QUESTIONS
Business Analytics or Data Analytics is analyzing data in all dimensions and find the logic and domain knowledge to impact the business and its revenue. Business analytics is made up of statistical methods that can be applied to a specific project, process or product. Business analytics can also be used to evaluate an entire company and its performance. Business analytics are performed in order to identify weaknesses in existing processes and highlight meaningful data that will help an organization prepare for future growth and challenges. The need for good business analytics has led to the creation of business analytics tools and enterprise platforms that can be used to mine an organization’s data in order to automate some of the measures and obtain meaningful insights.
• Working professionals in various industries seeking to develop skills in data analytics. • Professionals who wish to enhance their skills set from basic to advanced level of data analysis. • Aspiring data scientists.
Yes, you need a laptop with the R console and R studio installed. Participants need to install the below software before attending the course. R console can be downloaded from the link given below: • URL: https://CRAN.R-project.org/mirrors.html (make this link no-follow) R studio can be downloaded from the link given below: • URL: https://www.rstudio.com/products/rstudio/download/ (make this link no-follow)
• Data analytics along with R can be used to identify data trends, patterns, and relationships. • Understanding business analytics help to track the business objective. • Business Analytics helps in identifying the business problems and enhance the effective and accurate decision making process. • Deep learning is used in scenarios like NLP, Image and Text analytics which has wide application in industry
• Understand data analytics framework. • Understand data exploration techniques. • Understand multiple data modelling techniques to tackle business scenarios. • Hands-on experience with R programming of machine learning. • Visualization using Tableau. • Build neural networks. • Understand deep learning.
Participants will be working extensively on multiple data sets which are from globally known machine learning data repositories. Sufficient exercises and assignments will be covered during the course.
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