Name: Introduction To Marketing Analytics Course with Python- Henry Harvin® in Boston | Henry Harvin
Uploaded: 2020-07-09 18:27:28
Description: Certified Python' (CPMA) course is a focused 32-hours instructor-led training and certification program that equips participants to explore+analyze+solve marketing 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
- 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: Data Manipulation and Descriptive Summary
This module will guide the candidate with the knowledge of 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
- 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 Marketing 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: Linear Regression
This module will guide you through the basic understanding of Linear Regression. Know about multiple linear regression and its libraries. Learn the metrics of Linear Regressions and its application & assumptions
- Simple Linear Regression
- Libraries - sklearn, statsmodel
- Predict DV on IVs
- Multiple Linear Regression
- 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 Marketing 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 the 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
Module 13: Complementary Module 1: Soft Skills Development
- Business Communication
- Preparation for the interview
- Presentation Skills
Module 14: Complimentary Module 2: Resume Writing
- Resume Writing

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Marketing Analytics Certification Training with Python Course in Boston

25 Nov 2024

32 Hours

25 Nov 2024

32 Hours

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Key Highlights

32 Hours of Instructor-Led Sessions

11 Hours of Live Interactive Doubt Solving Sessions

6 Hours of Live Master Sessions by Industry Experts

192 Hours of Self-Paced Learning

192 Hours Hands on with Cloud Labs

Job-Ready Portfolio of 3 Capstone Projects

76 Auto-Graded Assessments

6+ Industry Case Studies

48 Guided Hands-On Exercises

3+ Assignments and Mini Projects

1 Mock Interview and 1 Hackathon

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About the Marketing Analytics with Python (CPMA) Course in Boston

Marketing Analytics with Python (CPMA) Course

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Curriculum For Marketing Analytics with Python (CPMA) Course in Boston

Module 1: Analytics- General

Module 2: Python Environment

Module 3: Basic Programming and Data Structures

Module 4: Data Manipulation and Descriptive Summary

Module 5: Statistics

Module 6: Graphical Representation of Data

Module 7: Linear Regression

Module 8: Logistic Regression

Module 9: classification

Module 10: Cluster Analysis

Module 11: Association Rule Analysis

Module 12: Text Mining

Module 13: Complementary Module 1: Soft Skills Development

Module 14: Complimentary Module 2: Resume Writing

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Managing Tangible and Intangible Assets

Applying Analytics Tools & Techniques

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