Diploma in Business Analytics

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Overview

Learn Business Analytics for a Dream Career

Description

The 3.5-month Diploma in Business Analytics curriculum offers comprehensive Business Analytics knowledge and proficiency. To be a business analyst you should be good at analytical skills, and handling data, and also one should have hands-on knowledge of the latest tools and software relevant to Business Analytics. Cranes Varsity’s Business Analytics course provides the learner with all necessary tools, software, and skill sets required to master business analytics.

Cranes Varsity’s Diploma in Business Analytics is designed to cater to the Non-Engineering Graduate students and also to the Working professionals from any domain. This does not necessitate any prior knowledge of Business Analytics and/or software programming.

The Diploma in Business Analytics course is split into various modules, students will go through these modules stage by stage with regular assessments. Modules covered include Databases management with SQL, Python programming, and Data analysis using EXCEL and TABLEAU, along with this students will also learn statistical analysis techniques and basic Machine learning concepts.

After completing the course, you’ll have the ability to think like a business analyst, describing, predicting, and informing business decisions in the specific areas of marketing, human resources, finance, and operations. You’ll also have a basic understanding of data, as well as an analytical mindset that will aid you in making strategic decisions based on data.

Business Analytics Course Modules

Generic
  • RDBMS using MySQL
  • Python Programming
  • Exploratory Data Analysis using Pandas
Business Analytics Specialization
  • Mathematics and Statistics for Data Science
  • Machine Learning using sklearn
  • Formulating Business Analytics Problems
  • Data Analysis and Visualization using Tableau / Power BI
  • Data Analysis and Visualization using MS Excel
Projects
  • Apply statistical methods to make decisions in various business problems, including bank, stock markets, etc.
  • Apply regression to predict future flight price
  • Apply classification to classify customer
  • Use clustering to cluster banking customers
  • Computer vision projects like Face recognition, Image Quality Improvement, etc.
Platform
  • Anaconda Distribution Jupyter, Spyder, MySQL
  • Tableau, Excel

Course Content

Generic:

  • Introduction to Python
  • Python Functions
  • Scope of Variables
  • List and Tuple
  • Map and filter functions
  • Set and Dictionary
  • Python Data types and Conditions
  • Default arguments
  • Global specifier
  • List Methods
  • String
  • Exception Handling
  • Control Statements
  • Functions with variable number of args
  • Working with multiple files
  • List Comprehension
  • List comprehension with conditionals
  • File Handling

Business Analytics Specialization:

  • Logarithm
  • Standard Deviation
  • Descriptive and Inferential Statistics
  • Linear Algebra
  • Differential Calculus
  • Chain Rule
  • Python Scipy Library
  • Mean, Median, Mode
  • Percentile
  • Log Normal Distribution
  • PCA: principle component Analysis
  • Probability and Distribution
  • Binomial Theorem
  • Hypothesis testing
  • Mean Absolute Deviation
  • Normal Distribution and Z Score
  • Visualizing Data
  • Variance: ANOVA
  • Statistical Significance
  • Inferential Statistics
  • Chi-square test, T test
  • Ordinal, frequency encoding
  • Mean Absolute Deviation
  • Normal Distribution and Z Score
  • Visualizing Data
  • Variance: ANOVA
  • Statistical Significance
  • Data Preprocessing
  • Transformation

  • Understand what is Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Train test split the data
  • ML Workflow for project implementation
  • Classification
  • Regression
  • Ordinal, frequency encoding
  • Standardization and normalization
  • Train test split the data
  • K fold cross validation
  • Regression
  • Simple linear regression
  • Multiple linear regression
  • Performance measure for regression
  • MSE, R-Squared, MAE, SSE
  • Feature selection for Regression
  • MSE, R-Squared, MAE, SSE
  • Various types of classification
  • Logistic regression
  • Naïve Bayed Classification
  • Decision tress and its types
  • K Nearest Neighbour Classification
  • Performance Measure for Classification
  • Accuracy, Recall, Precision, Fmeasure

  • Tableau Introduction
  • Working with sets
  • Connect Tableau with Different Data Sources
  • Cards in Tableau
  • Tableau Calculations using Functions
  • Traditional Visualization vs Tableau
  • Creating Groups
  • Visual Analytics
  • Charts, Dash-board
  • Building Predictive Models
  • Tableau Architecture
  • Data types in Tableau
  • Parameter Filters
  • Joins and Data Blending
  • Dynamic Dashboards and Stories

  • Introduction to Excel
  • Intro to Analyzing Data Using Spreadsheets
  • Charting techniques in Excel
  • Viewing, Entering, and Editing Data
  • Converting Data with Value and Text
  • Interactive dashboard creation
  • Introduction to Data Quality
  • Apply logical operations to data using IF
  • Data analytics project using Excel

  • Title selection
  • Final results
  • Dataset Selection
  • Report submission
  • Interim results

Placement Statistics

Embedded Training Course FAQs

This course enables you to apply for various job roles like business analyst, data analyst, and business intelligence with high pay packages, your career path will also be bright

Every company needs business as they coordinate with various teams within the organization to help identify issues and fix them, completing this course enables you to apply for highly in-demand jobs.

SQL, Python programming, Microsoft Excel, Tableau, statistical analysis, and basics of machine learning

NO, programming knowledge is not mandatory but having one is an advantage.

Projects like customer analysis, insurance risk analysis, and also projects related to finance and marketing are included, students are open to choose their own projects.

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