Marketing Analytics Certification Training with R

• 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

• 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

• Basic Data Structures & Programming Constructs
• Libraries
• Numpy
• Pandas
• Matplotlib

• 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)

• 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

• 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

• 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

• 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

• Difference between classification and regression decision trees from CART models
• 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)

• 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

• Applying AR to the grocery store for Market Basket Analysis
• Metrics- Support, Confidence, Lift
• Frequent Itemsets and Rules; Filtering rules
• Applications of AR

• 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

• Business Communication
• Preparation for the Interview
• Presentation Skills

Resume Writing Techniques