Business Analytics with Python

Course Overview

Business Analytics with Python Training is a course designed to teach individuals how to use the programming language Python to analyze and visualize data for business applications. The course covers topics such as statistical analysis, data manipulation, and data visualization, and aims to provide learners with the skills to extract insights and make data-driven decisions in a business setting.

 

At the end of the training, participants will be able to:

  1. To teach individuals the fundamentals of the Python programming language and its applications in data analysis and visualization.
  2. To provide learners with the skills to manipulate and clean data using Python.
  3. To introduce learners to statistical analysis techniques and how to apply them using Python.
  4. To enable learners to visualize data and create effective graphs and charts using Python.
  5. To equip learners with the knowledge and skills to extract insights from data and make data-driven decisions in a business setting using Python.

Pre-requisite

  • 1.Basic knowledge of any object-oriented programming language
  • 2.Comfortable with enterprise data and statistical terms
  • Suggested
  •  
  • 3.Fundamental understanding of Python and libraries such as NumPy, Pandas, SciPy
  • 4.Basic Statistics and Business Analytics Concepts

Duration

2 days

Course Outline

  1. Understanding Data
  2. Introduction to Data Analytics
  3. Introduction to Business Analytics, Business Intelligence and Data Mining
  4. Analytical Decision Making
  5. Future of Business Analytics
  6. Big Data Analytics
  7. Social Media Analytics
  8. Basic Statistical Concepts
  9. Type of Data
  10. Sampling Techniques
  11. Applications in industry domains
  12. Methodologies
  13. Decision making using data
  1. Installing Python
  2. Choosing an IDE
  3. iPython/Jupyter Notebook
  1. Inspection of data
  2. Data sanitization
  3. Data manipulation
  4. Reading and Writing Text Files
  5. JSON with Python
  6. HTML with Python
  7. Microsoft Excel files with Pytho
  1. Importing & Reading data
  2. Variable Types
  3. Variable Assignment
  4. Calculation with Variables
  5. Python Lists
  6. Writing functions
  7. Arguments
  8. Methods & String Methods
  9. List Methods
  10. Working with Packages
  11. Selective Import
  12. Control statements
  13. Loops
  14. String operations
  1. Series
  2. DataFrames
  3. Index objects
  4. Reindex
  5. Drop Entry
  6. Selecting Entries
  7. Data Alignment
  8. Rank and Sort
  9. Summary Statistics
  10. Missing Data
  11. Index Hierarchy
  1. Merge
  2. Merge on Index
  3. Concatenate
  4. Combining DataFrames
  5. Reshaping
  6. Pivoting
  7. Duplicates in DataFrames
  8. Mapping
  9. Replace
  10. Rename Index
  11. Binning
  12. Outliers
  13. Permutatio
  1. Measures of central tendency and dispersion
  2. Basic probability
  3. Binomial distribution
  4. Poisson distribution
  5. Normal distribution
  6. Level of significance
  7. P value
  8. Types of errors
  9. Hypothesis Testing
  10. T-Tests
  11. ANOVA
  12. Categorical Data Analysis
  13. Correlation & Covariance
  1. Installing Seaborn
  2. Basics – Column, Line, Pie Charts
  3. Histogram
  4. Boxplot
  5. Stem & Leaf
  6. Scatterplot
  7. QQ
  8. Regression Plot
  9. Heatmaps
  1. Regression – Linear Regression
  2. Regression – Multiple Linear Regression
  3. Regression – Logistic Regression
  4. Classification – Decision tree
  5. Time Series Analysis
  6. Case study: Application to supervised learning
  1. K-means Clustering
  2. Casestudy: Application of unsupervised learning
  1. Collaborative Filtering
  2. Association Rules
  3. Apriori
  4. Case study: Movie/Book recommendatio
  1. Sentiment Analytics
  1. Case study: Customer segmentation
  2. Case study: Market basket analysis

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