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Advanced Business Analytics with Python
Learn how to make efficient, profitable, data-driven business decisions and take a giant leap in your career with our certificate in advanced business analytics with Python.
-
297
Learners -
198+
Hours -
24x7
Online LMS -
20
Case Studies
297
Learners
Learners
198+
Hours
Hours
24x7
Online LMS
Online LMS
20
Case Studies
Case Studies
-
Knowledge
Partner
₹
20,322
Ask us everything about the program
Advanced Business Analytics with Python
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Overview
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Get RecognisedLike 84% of our learners, get recognised at work for your analytical skills. -
Job OpportunitiesComplete business analysis course online and become a candidate for over 2,00,000 jobs. -
Who Should AttendEngineering, IT, commerce & finance students -
Salary PackagesThe average pay for an entry-level Business Analyst is ₹4.8 lakhs per year. -
Expert FacultyLearn from vastly experienced data scientists and Python programmers.
Get Recognised
Like 84% of our learners, get recognised at work for your analytical skills.
Job Opportunities
Complete business analysis course online and become a candidate for over 2,00,000 jobs.
Who Should Attend
Engineering, IT, commerce & finance students
Salary Packages
The average pay for an entry-level Business Analyst is ₹4.8 lakhs per year.
Expert Faculty
Learn from vastly experienced data scientists and Python programmers.
About Course
Are you ready to fast track your career in the field of data science? Start by learning the fundamentals of programming in Python and gaining an in-depth understanding of how to use the skill to extract information and knowledge from data.
Python is the most popular language used in the field of data science. Even industry giants like Google and Netflix use it to generate insights and build better products. It can be quickly learnt and is versatile, making life easy for people who work with tonnes of data.Manipal ProLearn’s comprehensive certificate in Business Analytics using Python is tailored to train you on all aspects of Business Analytics; starting from exploratory data analysis, statistical and quantitative analysis, testing analytics models and forecasting through predictive modelling using Python & Microsoft Excel. The course will elucidate some of the most complex statistical tools used for data analysis through live online sessions by experts, real-life case study demonstrations, and videos.
What skills you learn
After completing the certificate program in business analytics using Python, you’ll be considered as a strong and competent data modelling professional. After completing the course, you’ll be able to:
- Use basic statistical concepts on multiple types of data to prepare reports.
- Use data sampling techniques to select, manipulate and analyse different data points to identify patterns and trends.
- Solve complex problems with Excel and Python - the most essential tools for Finance and analytics-driven companies.
- Optimise business situations that involve whole numbers, take decisions that involve multiple input variables to predict between two possible outputs, and optimise business situations where the two variables do not move in a linear fashion.
- Model decisions under a variety of future uncertain states, depending on the decision maker’s proneness or aversion to risks.
- Compute correlation between data points in a time series.
- Compute the regression model for time series data that has a correlation within itself.
- Test hypothesis for experiments involving different treatments and Identify the source of differences to pinpoint which experimental treatments were effective.
- Model continuous outcomes that depend on more than one input variable.
Who Should Attend this course
Entry to senior level managers handling data entry
Engineering/IT Graduates
Management/Commerce/Economics Graduates
People who want to upskill themselves in Business Analytics
Entry to senior level managers handling data entry | Engineering/IT Graduates |
Management/Commerce/Economics Graduates | People who want to upskill themselves in Business Analytics |
Key Features
Learn the most popular tools in analytics
Gain hands-on experience in Python and Excel – two of the most widely used tools for data analytics.
Comprehensive Course Curriculum
The certificate in business analytics using Python is taught through videos, case studies, reading material, and live online sessions by experts.
Certificate & credits by AWS Educate
Manipal ProLearn Certification powered by AWS Educate will be issued to the students, post completion of the course. You’ll also receive 75$ of AWS credit from AWS Educate.
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Course Curriculum
1.1 What is Business Analytics?
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
Describe how organisations benefit from using analytics
1.3 Understanding Data
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
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
Describe the analytical decision-making process
Describe the characteristics of the analytical decision-making process
1.6 Analysing Business Problems Using Key Questions
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
Identify the skills of a good business analyst
1.8 Future of Business Analytics
Describe the current trends that are likely to shape the future of business analytics
1.9 Big Data Analytics in the Enterprise
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
Define social media analytics
Describe the capabilities and common goals of social media analytics
2.2 Need For Python
What is Python?
Progress of Python
Success of Python
Programming Model of Python
2.3 Introduction to Python
Python Installation
Introduction to Python using Jupyter Notebook
Simple Input/Output
Basic Data Types
Control Structures
Arithmetic Operators
Logical Operators
2.4 Python Programming
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
Path and Directory
File Operations
Reading and Writing to Files
Advance File I/O
2.6 Basic Statistical Concepts and Types of Data
Define statistics and its use in business
Describe the types of data
Describe the basic statistical concepts
2.7 Sampling Techniques
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
Describe frequency distributions
Explain the various measures of central tendency
2.9 Variability and Shape
Explain the different measures of dispersion
Explain the different measures of shape
3.1 One-way Analysis of Variance
Explain the concept of ANOVA
Calculate ANOVA using Python
Test a hypothesis using ANOVA
3.2 Correlation
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
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
What is clustering?
K-Means Clustering using python
NbClust
3.5 Time series
Introduction to time series data
Time series forecasting using Moving Average
Time series forecasting using Naïve forecasting
3.6 Linear Programming
Explain the concept of linearity
Describe linear programming
Formulate a linear programming problem
3.7 Linear Programming – Allocation Models
Describe allocation models in linear programming
Solve allocation model problems in linear programming using Python
3.8 Linear Programming – Covering Models
Describe covering models in linear programming
Solve covering model problems in linear programming using Python
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