Name: Introduction To Business Analytics with Python (CPBA) Course- Henry Harvin® in Oklahoma City | Henry Harvin
Uploaded: 2020-07-09 15:57:19
Description: Certified Python Business Analyst (CPBA) course is a focused 32-hours Virtual Instructor-led training and certification program that equips participants to explore, analyze, and solve business problems using popular analytics tools such as Python & Advanced Excel.

Module 1: Analytics - General
This module will guide the candidate with the knowledge of Analytics. Learn about the different roles in Analytics. Know about the tools and techniques in Analytics. Gain knowledge about Data Science, Data Mining, STatistics, MAchine learning, and more. Learn about the CRISP Modeling Framework.
- What is Analytics (BI, BA, Levels, etc)
- Why Analytics (Appl in various domains..)
- Different Roles in Analytics
- Tools and Techniques in Analytics
- Data Science, Data Mining, Statistics, Machine Learning, Supervised and Non-Supervised Techniques
- CRISP Modeling Framework
- Scales of Measurements
- Quiz Module 1
Module 2: Python Environment
This module will help the candidate to gain knowledge about the Python Environment. Learn about Ananconda setup and various IDEs, GIT, and more. Create and Manage Analytics/ML Projects
- Anaconda - Download & Setup
- IDEs - Jupyter, Spyder, PyCharm
- Git - Setup and Configuration with IDEs
- Creating and Managing Analytics/ ML Projects
- Quiz Module 2
Module 3: Basic Programming and Data Structures
This module will help the candidate with knowledge of basic programming and data structures. Gain extensive knowledge about Libraries, Numpy, pandas, Matplotib
- Basic Data Structures & Programming Constructs
- Libraries
- Numpy
- Pandas
- Matplotlib
- Quiz Module 3
Module 4: Pre process and Descriptive Summary
This module will guide the candidate with the knowledge of Data Processing, Data Manipulation and Descriptive summary. Know about Group summaries, crosstab, pivot, reshape data and manage missing values. Learn to manage indexes in Pandas, Scaling of data, and more
- Pre Processing Data
- Group Summaries
- Crosstab, Pivot and Reshape data
- Managing Missing Values
- Outliers Detection
- Various types of Joins, merge
- Managing indexes in pandas
- Partitioning data into train and test set
- Scaling of Data (useful for Clustering)
- Quiz Module 4
Module 5: Statistics
This module will guide the candidate through the basics of statistics in Business Analytics. Learn extensively about Hypothesis testing, Probability distribution, and Sampling Techniques
- Basic Statistics (mean, median, mode)
- Other Statistics (sd, var, quantile, skewness, kurtosis)
- Hypothesis Tests (t-test, Chi-sq tests, etc)
- Probability Distributions (normal, binomial, etc)
- Sampling Techniques
- Quiz Module 5
Module 6: Graphical Representation of Data
This module will guide you through the techniques of Graphical Representation of Data. Learn about the selection of graphs and types of graphs. Manage plot parameters and advanced graphs such as correlations, heatmap, mosaic, and more
- Selection of Graph
- Basic Graphs (histogram, barplot, boxplot, pie, etc)
- Libraries (matplotlib, seaborn, plotline)
- Managing plot parameters(size, title, axis, legend, etc)
- Advanced Graphs (correlation, heatmap, mosaic, etc)
- Exporting graphs
- Quiz Module 6
Module 7: Modeling Techniques & Linear Regression
This module will guide you through the basic understanding of modeling techniques and Linear Regression. Know about multiple linear regression and its libraries. Learn the metrics of Linear Regressions and its application & assumptions
- Modeling Techniques
- Simple Linear Regression
- Multiple Linear Regression
- Libraries - sklearn, statsmodel
- Predict DV on IVs
- Metrics of Linear Regression(R2, RMSE, p-values)
- Applications of Linear Regression
- Assumptions of Linear Regression
- Quiz Module 7
Module 8: Logistic Regression
This module will guide the learner with knowledge of Logistic Regression. Know the metrics of logistic regression. Predict the probability of DV on IV. Know extensively about applications of Logistic regression
- Difference between Linear and Logistic
- Logistic Regression
- Metrics of Logistic Regression (confusion matrix, ROC curve)
- Predict the probability of DV on IV
- Applications of Logistic Regression
- Quiz Module 8
Module 9: Classification
This module will guide the candidate with the knowledge of classification in Financial Analytics. Understand the tree from the plot and know about the classification tree. Learn to improve tree accuracy using random forests. Know the applications of decision tree, KNN, Neural Networks, SVM, and more
- Difference between classification and regression decision trees from CART models
- Understanding tree from the plot
- Classification Tree - predict class, plot, accuracy
- Regression Tree - predict numerical value, plot, RMSE
- Improving tree accuracy using Random Forests
- Bagging and Boosting
- Applications of Decision Tree
- KNN (k-nearest neighbors)
- Neural Networks
- Gradient Descent
- SVM (Support Vector Machine)
- Quiz Module 9
Module 10: Cluster Analysis
This module will guide you through the knowledge of Cluster Analysis. Know about the Clustering for grouping data and its types. Learn about extracting data in clusters and application of clustering
- Clustering for Grouping Data
- Types - Hierarchical & Non-Hierarchical
- K Means - output metrics (iter, error, plot)
- Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot
- Extracting the data in clusters, Cluster Centers
- Applications of Clustering
- Quiz Module 10
Module 11: Association Rule Analysis
This module will guide you through the knowledge of the Association Rule analysis. Learn to apply AR to the grocery store for market basket analysis. Know about the frequent Itemsets and rules and application of AR
- Applying AR to the grocery store for Market Basket Analysis
- Metrics- Support, Confidence, Lift
- Frequent Itemsets and Rules; Filtering rules
- Applications of AR
- Quiz Module 11
Module 12: Text Mining
This module will guide the candidate through the understanding of Text Mining. Manage unstructured data and extract tweets from Twitter and words for sentiment analysis. Know the application of text mining
- Managing Unstructured Data; Unstructured to Structured Data
- Extracting Tweets from Twitter
- Extracting words for Sentiment Analysis
- Wordcloud to visualize the frequency of occurrence of words in the text
- Applications of Text Mining
- Quiz Module 12

Business Analytics with Python (CPBA) Course in Oklahoma City

25 Nov 2024

32 Hours

25 Nov 2024

32 Hours

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Key Highlights

32 Hours of Instructor-Led Sessions

10 Hours of Live Interactive Doubt Solving Sessions

5 Hours of Live Master Sessions by Industry Experts

192 Hours of Self-Paced Learning

Job-Ready Portfolio of 3 Capstone Projects

79 Auto-Graded Assessments

6+ Industry Case Studies

48 Guided Hands-On Exercises

3+ Assignments and Mini Projects

1 Mock Interview and 2 Hackathons

Get 1-Year Gold Membership of Henry Harvin® Data Science & Analytics Academy

Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage

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2 Hours of Mentorship by Industry Experts

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About Business Analytics with Python (CPBA) Course in Oklahoma City

Business Analytics

Agota™ Framework

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5+ Projects covered (Hands-on Practical Experience)

Curriculum For Business Analytics with Python (CPBA) Course in Oklahoma City

Module 1: Analytics - General

Module 2: Python Environment

Module 3: Basic Programming and Data Structures

Module 4: Pre process and Descriptive Summary

Module 5: Statistics

Module 6: Graphical Representation of Data

Module 7: Modeling Techniques & Linear Regression

Module 8: Logistic Regression

Module 9: Classification

Module 10: Cluster Analysis

Module 11: Association Rule Analysis

Module 12: Text Mining

Admission Closes On 25 Nov 2024

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What you'll Learn in this course

Designing and Managing Analytics

Designing ML Projects

Python Programming Languages

Fundamentals of NumPy Arrays

Basic Concepts of Statistics

Text Mining for Sentiment Analysis

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