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    Certified Marketing Analytics Practitioner

    BY
    Henry Harvin

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    Mode

    Online

    Duration

    32 Hours

    Fees

    ₹ 13500 15000

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom +1 more
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 13,500  ₹15,000
    certificate availability

    Yes

    certificate providing authority

    Henry Harvin

    The syllabus

    Module 1: Analytics - General

    • 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: R Programming

    • R Environment and R Studio
    • Data Management
    • Improving functionality with additional packages
    • Quiz Module 2

    Module 3: Basic Programming and Data Structures

    • Basic Data Structures & Programming Constructs
    • Libraries
    • Numpy
    • Pandas
    • Quiz Module 3

    Module 4: Data Manipulation and Descriptive Summary

    • 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

    • 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

    • 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

    • 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

    • 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

    • 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)
    • Quiz Module 9

    Module10: Cluster Analysis

    • 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

    • 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

    • 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

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