Self-Paced, Live Online, Classroom Enterprise Training
Acquire expertise in utilizing commonly accessible Python libraries and functions, such as NumPy, Pandas, and SciPy, to proficiently analyze data using quantitative techniques. Develop a thorough comprehension of statistical methods, cutting-edge data mining strategies, pattern recognition, data visualization, as well as exploratory and predictive modeling. Employ this knowledge to generate meaningful insights from public and enterprise data. Become the lead data analyst that your organization necessitates.
inperson:
5 days
online:
40 hours
Certified Corporate Trainer
Lifetime Access
Quiz & Assignments
24x7 Support
Real Time Projects
Cost Effective Programs
Objective
Upon the completion of our Business Analytics with Python program, you will possess the following abilities:
Create an analytical approach to management by utilizing data and comprehending statistical and quantitative models.
Identify trends, outliers, and concisely summarize data sets.
Formulate and assess hypotheses to guide managerial decisions.
Acquire hands-on experience in Python coding and its libraries to build data analytics solutions tailored to your organization's requirements.
Establish correlations between multiple variables through regression analysis.
Classify data to identify the optimal outcome in a given business context.
Categorize customers into segments through cluster analysis.
Construct a predictive model of customer behavior through collaborative filtering.
Analyze customer perception using unstructured text analytics.
Deliver comprehensive data-driven solutions to any business challenge.
Target Audience
Developers, Aspiring data scientists and Managers who wish to utilize the versatility of the Python language to create comprehensive analytics solutions for their organization.
Prerequisites
Familiarity with any object-oriented programming language
Detailed Outline
Chapter 1: An overview of 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
Types of Data
Sampling Techniques
Applications of analytics in industry domains
Methodologies
Data-driven decision making
Chapter 2: Setting up Python on a device
Installing Python on a device
Selecting an Integrated Development Environment (IDE)
Using iPython or Jupyter Notebook
Chapter 3: Performing Data Preprocessing
Inspecting the data for quality and consistency
Sanitizing the data to remove errors and inconsistencies
Manipulating the data for further analysis
Reading and Writing Text Files with Python
Working with JSON files using Python
Parsing HTML files using Python
Handling Microsoft Excel files with Python
Chapter 4: Jump-start Programming with Python
Importing and reading data in Python
Understanding different variable types in Python and how to assign values to them
Performing calculations using variables in Python
Working with Python lists, which are used to store collections of items
Creating and using functions in Python, including working with function arguments
Exploring methods and string methods in Python, which are functions that operate on strings and other objects
Understanding list methods in Python, which are functions that operate on lists
Working with packages in Python, which are collections of modules that can be used to extend Python's capabilities
Using selective import to only import the parts of a package that are needed
Implementing control statements in Python to direct the flow of a program
Working with loops in Python to repeat code until a certain condition is met
Performing string operations in Python, such as concatenation and slicing
Chapter 5: Overview of Pandas
Series
DataFrames
Index objects
Reindexing data
Dropping entries
Selecting specific entries
Data alignment
Sorting and ranking data
Calculating summary statistics
Handling missing data
Using index hierarchy
Chapter 6: Data Manipulation
Merge
Index-based merging
Concatenation
Combining dataframes
Reshaping
Pivoting
Handling duplicates in dataframes
Mapping values
Replacing values
Renaming dataframe indices
Binning data
Identifying and handling outliers
Permutation of data
Chapter 7: Fundamental statistics
Central tendency and dispersion measures
Elementary probability
Binomial and Poisson distributions
Normal distribution
Significance level
P-value
Types of errors
Hypothesis testing
T-tests
Analysis of Variance (ANOVA)
Categorical data analysis
Correlation and covariance
Chapter 8: Analysis and visualization of data for discovery and insight purposes.
Seaborn installation process
Introduction to basic charts such as column, line, and pie charts
Histograms
Boxplots
Stem-and-leaf plots
Scatterplots
Q-Q plots
Regression plots
Heatmaps
Chapter 9: Learning under supervision
Linear regression for regression analysis
Multiple linear regression for regression analysis with multiple predictors
Logistic regression for classification analysis
Decision tree algorithm for classification analysis
Time series analysis for forecasting
Case study: Applications of supervised learning techniques
Chapter 10: Learning without supervision
K-means clustering algorithm for unsupervised learning
Case study: Applications of unsupervised learning techniques
Chapter 11: Recommendation system
Collaborative filtering algorithm for recommendation systems
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