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    • Marketing Analytics Certification Training Course Plus Lean Six Sigma Green Belt Certification Training

    Marketing Analytics Certification Training Course(CMAP) + Lean Six Sigma Green Belt Certification Training Course(CSSE-GB)

    BY
    Henry Harvin

    Brochure Enquire
    Mode

    Online

    Duration

    64 Hours

    Fees

    ₹ 22050 24500

    Quick Facts

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

    Course and certificate fees

    Fees information
    ₹ 22,050  ₹24,500
    certificate availability

    Yes

    certificate providing authority

    Henry Harvin

    The syllabus

    Phase 1

    • Marketing Analytics Using Python and R Programming

    Module 1

    Introductory Topics to Analytics
    • Understanding the need for Analytics in Specific Domain; CRISP Modeling
    Introduction to Data Management
    • Properties & Types of Data, Measurement Scale, Basic Statistics on Data
    Basics of Python Programming
    • Need for R
    • Features of R
    • Download, Setup, Installation
    • Python & Python Studio
    • Configuration. eg. Learning to set up Python and share Code
    Data Structures in R
    • Creating and Understanding Basic Data Structures in Python - Vector, List, Matrix, Array, Data Frame & Factors which help in creating data in Python programming.
    Data Manipulation & Summarisation in R
    • Understanding how data can be summarised in different ways to do Descriptive Analysis which describes features of data.
    Evaluation & Introduction to Lean Six Sigma
    • History & Evolution of Six Sigma
    • Six Sigma & Lean Definition
    • COPQ
    • Variation
    • DMAIC Phases
    • Six Sigma Roles & Responsibilities

    Module 2

    Analytical Modeling
    • Understand what is Modeling and how it can be used in Various Domain
    Statistical Tests
    • P-Value
    • Z Value
    • Hypothesis
    • Null Hypothesis
    • Alternative Hypothesis
    • F Test
    • ANOVA Introduction
    Linear Regression (Using Python)
    • Start of Machine Learning,
    • Develop a Prediction Model for predicting financial values based on one or more than 1 Independent Variable
    • Understand the assumptions and measures of goodness of Model
    • Understand its prediction ability
    Visualization using Graphs
    • Creating Graph in Python and understanding which graph to be used when.
    Missing Value and Outlier Analysis
    • Understanding how missing values & outliers are handled in data summarisation & modelling
    Logistic Regression
    • Predicting Binary Outcome (Buy or not, Churn or not, Loan Default or not) based on Independent Variables eg. Predicting Cases for Fraud, Default on Payment, etc.
    Define
    • Project Charter
    • Project Charter Contents
    • Process Mapping
    • SIPOC
    • Identifying Customers & VOC
    • Establishing VOC to CTQ
    • KANO Model
    • RACI Model
    • Quiz

    Module 3

    Clustering
    • Grouping customers based on characteristics so that they can be a target for sale increase
    Decision Trees
    • When to use CART & CHAID to create a decision tree based on categorical variables.
    Ensembles (Bagging & Boosting)
    • Random Forest, XGBoost: Problems of Decision Tree covered in Random Forest
    • How group thinking impacts the decisions (from a business point of view).
    Measure
    • What is Data?
    • Data Classification & Type
    • Data collection plan
    • Sampling & Sampling Strategies
    • Mean
    • Median
    • Mode
    • Range
    • Variance
    • Standard Deviation
    • Case Study – Basic Statistics
    • Normal Distribution
    • Testing Normality
    • Histogram
    • Dot Plot
    • Box Plot
    • Time Series Plot

    Module 4

    Twitter Analysis
    • Configure Twitter Account & Application
    • Setup for downloading tweets and analyse them for positive and negative sentiments related to Financial News/ Articles.
    • Analyse
    • Measurement System Analysis
    • Accuracy & Precision
    • Repeatability
    • Reproducibility
    • Gage R & R study
    • Process Capability
    • Capability Formulas
    • DPMO, DPU, DPO
    • Rolled Throughput Yield
    • 7 QC Tools
    • Central Limit Theorem
    • Hypothesis testing
    • Alpha risk
    • Beta Risk
    • t-test

    Post Learning

    • Final Assessment
    • Certificate Dispatch
    • Monthly Brush Up Sessions- Live Online (12months)

    Phase 2

    • Six Sigma Green Belt

    Module 5

    • Improve
    • Improvement Strategy
    • Regression
    • Correlation
    • Scatter Plot
    • Brainstorming
    • 5S
    • FMEA
    • Piloting Solutions
    • Automation
    • Regression
    • Correlation
    • Scatter Plot
    • Brainstorming
    • Automation
    • Mistake Proofing

    Module 6

    • Control
    • Statistical Process Control
    • SPC Selection Process
    • Control Plan

    Complimentary Module 1: Soft Skills Development

    • Soft Skills Training

    Complimentary Module 2: Resume Writing

    • Resume Writing

    Projects Covered

    Analytics
    • HR: Analyze the Attrition rate of Employees
    • Sales: Predicting Department wise Sales
    • Multi-Domain: Business Analytics Optimization
    • Marketing: Website Trend Analysis
    • Financial Analysis: Stock Market Prediction
    • Finance: Analyze ETF Trends
    Six Sigma
    • Quality of Work Life in an Organization of Employees
    • Improve Total time & Rolled Throughput Yield
    • Optimization of Average call times in a BPO-Voice Process
    • Defect Reduction in Die Casting
    • Improving Internal SQR in XXM Activities

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