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Advanced Business Analytics with Python
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.
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.
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.
Entry to senior level managers handling data entry
People who want to upskill themselves in 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
Describe how organisations benefit from using analytics
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
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
Describe the analytical decision-making process
Describe the characteristics of the analytical decision-making process
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
Identify the skills of a good business analyst
Describe the current trends that are likely to shape the future of business analytics
Describe the characteristics of big data
Describe how hardware and software technologies are helping analytics handle extremely large volumes of data
Define social media analytics
Describe the capabilities and common goals of social media analytics
What is Python?
Progress of Python
Success of Python
Programming Model of Python
Introduction to Python using Jupyter Notebook
Basic Data Types
Tuples & Dictionaries
Introduction to Functions
Parameters and Arguments
Data Processing using Pandas and Nampy
Introduction to Modules & Packages
Path and Directory
Reading and Writing to Files
Advance File I/O
Define statistics and its use in business
Describe the types of data
Describe the basic statistical concepts
Explain the concept of sampling and why it is necessary
Describe the various techniques for sampling
Describe a good sample
Describe frequency distributions
Explain the various measures of central tendency
Explain the different measures of dispersion
Explain the different measures of shape
Explain the concept of ANOVA
Calculate ANOVA using Python
Test a hypothesis using ANOVA
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
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
What is clustering?
K-Means Clustering using python
Introduction to time series data
Time series forecasting using Moving Average
Time series forecasting using Naïve forecasting
Explain the concept of linearity
Describe linear programming
Formulate a linear programming problem
Describe allocation models in linear programming
Solve allocation model problems in linear programming using Python
Describe covering models in linear programming
Solve covering model problems in linear programming using Python