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Advanced Business Analytics with Python

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Overview
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    Get Recognised
    Like 84% of our learners, get recognised at work for your analytical skills.
  • manipal overview icon
    Job Opportunities
    Complete business analysis course online and become a candidate for over 2,00,000 jobs.
  • manipal overview icon
    Who Should Attend
    Engineering, IT, commerce & finance students
  • manipal overview icon
    Salary Packages
    The average pay for an entry-level Business Analyst is ₹4.8 lakhs per year.
  • manipal overview icon
    Expert Faculty
    Learn from vastly experienced data scientists and Python programmers.
manipal overview icon
Get Recognised
Like 84% of our learners, get recognised at work for your analytical skills.
manipal overview icon
Job Opportunities
Complete business analysis course online and become a candidate for over 2,00,000 jobs.
manipal overview icon
Who Should Attend
Engineering, IT, commerce & finance students
manipal overview icon
Salary Packages
The average pay for an entry-level Business Analyst is ₹4.8 lakhs per year.
manipal overview icon
Expert Faculty
Learn from vastly experienced data scientists and Python programmers.
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Course Curriculum
1.1 What is Business Analytics?
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Describe business analytics
Describe the evolution of analytics beginning with “scientific management” to its present form
Describe the differences between analytics and analysis and explain the concept of insights
Describe the broad types of business analytics

1.2 A Case for Business Analytics
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Describe how organisations benefit from using analytics

1.3 Understanding Data
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Describe the importance of data in business analytics
Describe the differences between data, information and knowledge
Describe the various stages that an organization goes through in terms of data maturity
Explain what an organization can do in the absence of good quality data

1.4 Business Analytics, Business Intelligence and Data Mining
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Explain the differences between Business Analytics and Business Intelligence
Describe the two major components within Business Analytics and Business Intelligence
Understand how Data Mining as a technique helps both Business Intelligence and Business Analytics

1.5 Analytical Decision-Making
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Describe the analytical decision-making process
Describe the characteristics of the analytical decision-making process

1.6 Analysing Business Problems Using Key Questions
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Describe how a business problem can be broken down repeatedly into key questions and then answered through analytics
Describe the characteristics of a good key question

1.7 Skills of a Good Business Analyst
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Identify the skills of a good business analyst

1.8 Future of Business Analytics
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Describe the current trends that are likely to shape the future of business analytics

1.9 Big Data Analytics in the Enterprise
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Describe the characteristics of big data
Describe how hardware and software technologies are helping analytics handle extremely large volumes of data

2.1 Social Media Analytics
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Define social media analytics
Describe the capabilities and common goals of social media analytics

2.2 Need For Python
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What is Python?
Progress of Python
Success of Python
Programming Model of Python

2.3 Introduction to Python
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Python Installation
Introduction to Python using Jupyter Notebook
Simple Input/Output
Basic Data Types
Control Structures
Arithmetic Operators
Logical Operators

2.4 Python Programming
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Strings, Lists
Tuples & Dictionaries
Introduction to Functions
Parameters and Arguments
Recursion
Data Processing using Pandas and Nampy
Introduction to Modules & Packages

2.5 FILE Input/Output
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Path and Directory
File Operations
Reading and Writing to Files
Advance File I/O

2.6 Basic Statistical Concepts and Types of Data
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Define statistics and its use in business
Describe the types of data
Describe the basic statistical concepts

2.7 Sampling Techniques
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Explain the concept of sampling and why it is necessary
Describe the various techniques for sampling
Describe a good sample

2.8 Frequency Distributions and Measures Of Central Tendency
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Describe frequency distributions
Explain the various measures of central tendency

2.9 Variability and Shape
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Explain the different measures of dispersion
Explain the different measures of shape

3.1 One-way Analysis of Variance
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Explain the concept of ANOVA
Calculate ANOVA using Python
Test a hypothesis using ANOVA

3.2 Correlation
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Evaluate the statistical relationships between two random variables and understand the measure of correlation
Identify and quantify the correlation between two datasets using Python
Explain the concepts of correlation versus causation

3.3 Linear Regression
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Explain how to model statistical relationships between two data series using linear regression
Create a linear regression model to forecast values using linear regression in Python

3.4 K-Means Clustering
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What is clustering?
K-Means Clustering using python
NbClust

3.5 Time series
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Introduction to time series data
Time series forecasting using Moving Average
Time series forecasting using Naïve forecasting

3.6 Linear Programming
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Explain the concept of linearity
Describe linear programming
Formulate a linear programming problem

3.7 Linear Programming – Allocation Models
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Describe allocation models in linear programming
Solve allocation model problems in linear programming using Python

3.8 Linear Programming – Covering Models
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Describe covering models in linear programming
Solve covering model problems in linear programming using Python

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Frequent Questions we get
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