Data Science for Manager

Data Science is a complex and broad discipline that involves computer science, mathematics, statistics, and understanding the sources of data. The Data Science for Managers course primarily focuses on the methods and tools employed to analyze the data of a business, gather insights, and utilize them to make further business decisions.
 

Eligibility:

Data Science for Managers course is intended for professionals who are involved in decision - making roles. Candidates interested in enrolling in Data Science for Managers course should have the following:

• Basic knowledge of working in the Data Science domain
• Knowledge of statistics and mathematics 
• Experience of working in a decision - making role

Outline:

Data Science Introduction

• Data Science Introduction 
• Data Science Evolution 
• Business Applications 
• Benefits of Data Science 
• Challenges of Data Science 

Data Science Context & Classification

• Data Science Context: Data Mining / Business Intelligence / Data Science 
• The perspective of Data Science 
• Data Science Classification 
• Various Types Of Data Science 

Data Science Workflow

• Data Science Steps 
• Recognize the Problem 
• Define a Problem 
• Structure the Problem 
• Analyze the Problem 
• Interpret the Results 
• Implement Solution 
• Crisp - Dm Reference Model

Data Science Introduction

• Overview of Statistics 
• Descriptive Statistics 
• Inferential Statistics 
• Basics Terminologies 
• Cochran's Min Size Formula 
• Types of Data 

Examples of Various Data 

• Cochran’s Formula Example 
• Examples of Variables
• Harnessing Data
• Harnessing Data Introduction
• Making Sense of the Data 
• Sampling Methods 
• Simple Random Sampling 
• Stratified Sampling 
• Systematic Sampling 
• Biased Sampling Methods

Exploratory Data Analysis

• Exploratory Data Analysis 
• Measures of Central Tendencies 
• Mean 
• Median 
• Mode 
• Measures of Variability 
• Range 
• Calculating Standard Deviation

Histogram & Empirical Rule

• Z - Score / Z - Value/ Standard Score 
• Calculating Z - Score 
• Histogram 
• Empirical Rule 
• Outliers 

Normal Distribution And Correlation

• Normal Distributions, Mean, Variance 
• Normality Testing
• Skewness 
• Kurtosis 
• Correlation

Decision Modeling

• Mathematical Modeling 
• Decision Modeling 
• Break-Even Decision Model 
• Prescriptive Model Pricing

Sales Promotion Model

• Sales Promotion Decision 
• Sales Prediction With Multiple Regression Analysis

Kerc Inc – Case Study: Predictive Analytics with Less Uncertainty

• Kerc Inc – Car Manufacturer Case Study 
• Kerc Inc Case Modeling 
• Optimizing Kerc Production Model

Wonderzon Retailer: Case Study

• Wonderzon Case Study: Problem Statement 
• Wonderzon Case Modeling
•  Predicting Optimum Logistics Cos

Predictive Analytics

• Predictive Analytics Introduction 
• Types of Predictive Analytics
• Application of Predictive Analytics

Regression

• Mathematical Modeling Regression 
• Simple Learning Regression 
• Predicting Profit with Regression

Multiple Regression

• Multiple Regression Modeling 
• Predictive Analytics Summary

Smart It Inc Internet Usage - Plan Decision Predictive Analytics with High Uncertainty

• Monte Carlo Simulation Introduction 
• Smart It Inc. Internet Plan: Problem Statement 
• Model Building 
• Optimization and Plan Decision

Kickathlon Sports Retailer: Decision Tree

• Kickathlon – Supplier Decision Making 
• Decision Tree Convention 
• Kickathlon – Supplier Decision Tree

Decision Tree – Decision Making Process

• Decision Tree Convention 
• Common Decision Strategies 
• Maxi - Min Strategy 
• Maxi - Max Strategy 
• Maximize Expected Value Strategy 
• Decision Trees with Monte Carlo Simulation Uncertain Output Range 
• Decision Tree Summary

Tableau: Connecting to Data Source

• Tableau Introduction 
• Connecting to Various Data Sources 
• Fixing Data Issues while Importing

Tableau: Basic Visualizations

• Tableau Panes 
• Creating Basic Visualizations 
• Insights for Data Through Visualizations 
• Exploratory Data Analysis 

Finding Insights

• Stacked Bar Graph 
• Deriving Insights Through Compound Plots

Time Series Forecasting

• Forecasting Sales 
• Adjusting Forecasting Parameters

Filters & Dashboards

• Various Filters Options 
• Interactive Filters 
• Building Effective Dashboards

Publishing Insights

• Various Publishing Options 
• Tableau Packaged Workbook 
• Summary

Learning Outcomes:

After completing the Data Science for Managers course, candidates will be able to: 

• Comprehend the application of Data Science techniques in the business world
• Analyze data with advanced Data Science tools
• Engage in basic Data Science activities and interpret results
• Acquire insight on Business Analytics and Statistics
• Using Excel for decision modeling

Accreditation:

Our Data Science for Managers course is accredited by ISO 9001: 2015 and the Knowledge and Human Development Authority (KHDA).

Why Learner's Point:

Being one of the foremost providers of the Data Science for Managers course, we have vast knowledge and valuable experience in the field. 

Our Data Science for Managers course offers the following benefits:

• Our Data Science for Managers course is designed based on real - world scenarios, formulating and evaluating practical requirements
• Our courses strive to provide a reality check and insight into the requirements of a challenging role and how to fulfill it
• We also provide live interactive sessions and hands - on experience through assignments associated with the actual work environment
• To enhance the learning experience of our students we provide organized group sessions and integrated training modules
• Our discrete approach to career guidance enables our students to get successfully placed in renowned organizations