Business Analytics with Python

  • Course level: All Levels


Live Online & Classroom Enterprise Training

Master the advanced business analytics concepts to improve your organization’s performance. Learn to create analytics solutions to derive insights from enterprise data using powerful functions and libraries in Python. Become the lead business analytics professional your team needs.

What Will I Learn?

  • After completing our Business Analytics with Python training, you will be able to:
  • Develop analytical approach to management to utilize data, understand statistical and quantitative models
  • Recognize trends, detect outliers, and summarize data sets concisely
  • Develop and test hypotheses to inform managerial decisions
  • Gain hands-on knowledge of Python coding and its libraries to develop data analytics solutions for your organization's needs.
  • Derive relationship between two or more variables through regression
  • Classify data to identify the best possible outcome in a given business context
  • Define customer segments through cluster analysis
  • Create a predictive model of customer choice through collaborative filtering
  • Learn to analyze customer perception through unstructured text analytics
  • Provide comprehensive solution to any business challenge through better data-driven decision

Topics for this course


Introduction to Business Analytics?

Understanding Data Introduction to Data Analytics Introduction to Business Analytics, Business Intelligence and Data Mining Analytical Decision Making Future of Business Analytics Big Data Analytics Social Media Analytics Basic Statistical Concepts Type of Data Sampling Techniques Applications in industry domains Methodologies Decision making using data

Installing Python?

Installing Python Choosing an IDE iPython/Jupyter Notebook

Data Preprocessing?

Inspection of data Data sanitization Data manipulation Reading and Writing Text Files JSON with Python HTML with Python Microsoft Excel files with Pytho

Jump-start Programming with Python?

Importing & Reading data Variable Types Variable Assignment Calculation with Variables Python Lists Writing functions Arguments Methods & String Methods List Methods Working with Packages Selective Import Control statements Loops String operations

Introduction to Pandas?

Series DataFrames Index objects Reindex Drop Entry Selecting Entries Data Alignment Rank and Sort Summary Statistics Missing Data Index Hierarchy

Data Operations?

Merge Merge on Index Concatenate Combining DataFrames Reshaping Pivoting Duplicates in DataFrames Mapping Replace Rename Index Binning Outliers Permutatio

Basic Statistics?

Measures of central tendency and dispersion Basic probability Binomial distribution Poisson distribution Normal distribution Level of significance P value Types of errors Hypothesis Testing T-Tests ANOVA Categorical Data Analysis Correlation & Covariance

Exploratory Data Analysis (EDA) & Visualization?

Installing Seaborn Basics - Column, Line, Pie Charts Histogram Boxplot Stem & Leaf Scatterplot QQ Regression Plot Heatmaps

Supervised Learning?

Regression - Linear Regression Regression - Multiple Linear Regression Regression - Logistic Regression Classification - Decision tree Time Series Analysis Case study: Application to supervised learning

Unsupervised Learning?

K-means Clustering Casestudy: Application of unsupervised learning

Recommender System?

Collaborative Filtering Association Rules Apriori Case study: Movie/Book recommendatio

Text Analytics?

Sentiment Analytics

Lab Session?

Case study: Customer segmentation Case study: Market basket analysis

About the instructor

5.00 (1 ratings)

79 Courses

12 students

Business Analytics with Python

Material Includes

  • Enterprise Reporting
  • Lifetime Access
  • CloudLabs
  • 24x7 Support
  • Real-time code analysis and feedback
  • 100% Money Back Guarantee


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

Target Audience

  • Developers
  • Aspiring Data Scientists,
  • Managers who want to leverage the flexibility of Python language to create comprehensive analytics solutions for their organizatio