BUSINESS ANALYTICS TRAINING COURSE

BUSINESS ANALYTICS TRAINING COURSE OCTOBER CLASS

BUSINESS ANALYTICS TRAINING COURSE: This course aims to introduce you to business analytics as a foundational part of your business education. It covers elements of data discovery and collection, data quality, analysis and data sharing, and generalizing data analytics results to wider business conclusions and decisions.

START:
April 16, 2019
PRICE
250,000.00

Address

PLOT 14,Odeniran close,Off Opebi,Oregun Link Bridge,Opebi Ikeja,Lagos   View map

BUSINESS ANALYTICS TRAINING COURSE

OVERVIEW

BUSINESS ANALYTICS TRAINING COURSE: This course aims to introduce you to business analytics as a foundational part of your business education. It covers elements of data discovery and collection, data quality, analysis and data sharing, and generalizing data analytics results to wider business conclusions and decisions. It deploys IBM SPSS software, applied to a wide variety of business applications, including estimation and predictive analysis.

Business Analytics is “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” It is a combination of :
1. Techniques: Predictive Analytics, Data Exploration, Clustering, Classification, Market Basket Analysis.
2. Tools: Excel, SAS, R and Python.
3. Applications: in Retail, Finance, Telecom etc.

BUSINESS ANALYTICS TRAINING COURSE

Course Outline

Statistical Techniques
  • Different types of data, Data summarization
  • Frequency table, Distributions and Histogram
  • Measures of central tendency and dispersion
  • Skewness and kurtosis
  • Basic Probability, Conditional Probability
  • Normal Distribution
  • Sampling methods
  • Point and Interval estimation
  • Central Limit Theorem
  • Null and alternative hypothesis
  • Level of significance
  • P value
  • Types of errors
  • Hypothesis Testing

Linear and Multiple Linear Regression

  • Simple and Multiple Linear Regression
  • R2 and Adjusted R2
  • ANOVA
  • Interpretation of coefficients
  • Dummy Variables
  • Residual Analysis
  • Outliers

Logistic Regression

  • Assumptions
  • Logistic Function
  • Chi-Square
  • Hosmer Lemeshow test
  • Kolmogorov-Smirnov statistic and chart
  • Classification Table
  • Interpreting Coefficients
  • Dependent Variable Prediction
  • Principles of Forecasting
  • Time Series
  • Causal models
  • Types of Forecasting Methods and their characteristics
  • Moving Average
  • Exponential Smoothing
  • Trend
  • Seasonality
  • Cyclicity
  • Holt Winter’s forecasting method

Market Basket Analysis

  • Basic concepts
  • Data mining techniques
  • Frequent Itemset Mining Methods
  • Apriori, FPGrowth
  • Pattern Evaluation Methods: Lift, Chi –Square

Classification

  • Decision Tree Induction
  • Bayes Methods
  • Rule-Based Classification
  • Model Evaluation and Selection
  • Ensemble Approaches

Clustering

  • Partitioning Methods
  • Hierarchical Methods
  • Density-Based Methods
  • Grid-Based Methods
  • Evaluation of Clustering
  • K-means Method

Excel

  • Formatting of Excel Sheets
  • Use of Excel Formula Function
  • Data Filter and Sort
  • Charts and Graphs
  • Table formula and Scenario building
  • Lookups
  • Pivot tables

R and Python

  • Reading and Writing Data
  • Data types
  • Important Packages
  • Data Manipulation
  • Building models using learned algorithms
  • Evaluating and optimizing models

Orientation on Big Data and Hadoop

  • Awareness of Big Data and Hadoop
  • Nosql vs Sql
  • The four V’s
  • Is Big Data = Hadoop
  • Big Data and Cloud Computing
  • Generators of Big Data
  • Applications of Big Data

Exposure to Web and Mobile Analytics with focus on:

  • Text Analytics
  • Sentiment Analytics
  • Click Analytics, Google Analytics
  • Difference between Web and Mobile Analytics

 

Course Outline

Statistical Techniques
  • Different types of data, Data summarization
  • Frequency table, Distributions and Histogram
  • Measures of central tendency and dispersion
  • Skewness and kurtosis
  • Basic Probability, Conditional Probability
  • Normal Distribution
  • Sampling methods
  • Point and Interval estimation
  • Central Limit Theorem
  • Null and alternative hypothesis
  • Level of significance
  • P value
  • Types of errors
  • Hypothesis Testing

Linear and Multiple Linear Regression

  • Simple and Multiple Linear Regression
  • R2 and Adjusted R2
  • ANOVA
  • Interpretation of coefficients
  • Dummy Variables
  • Residual Analysis
  • Outliers

Logistic Regression

  • Assumptions
  • Logistic Function
  • Chi-Square
  • Hosmer Lemeshow test
  • Kolmogorov-Smirnov statistic and chart
  • Classification Table
  • Interpreting Coefficients
  • Dependent Variable Prediction
  • Principles of Forecasting
  • Time Series
  • Causal models
  • Types of Forecasting Methods and their characteristics
  • Moving Average
  • Exponential Smoothing
  • Trend
  • Seasonality
  • Cyclicity
  • Holt Winter’s forecasting method

Market Basket Analysis

  • Basic concepts
  • Data mining techniques
  • Frequent Itemset Mining Methods
  • Apriori, FPGrowth
  • Pattern Evaluation Methods: Lift, Chi –Square

Classification

  • Decision Tree Induction
  • Bayes Methods
  • Rule-Based Classification
  • Model Evaluation and Selection
  • Ensemble Approaches

Clustering

  • Partitioning Methods
  • Hierarchical Methods
  • Density-Based Methods
  • Grid-Based Methods
  • Evaluation of Clustering
  • K-means Method

Excel

  • Formatting of Excel Sheets
  • Use of Excel Formula Function
  • Data Filter and Sort
  • Charts and Graphs
  • Table formula and Scenario building
  • Lookups
  • Pivot tables

R and Python

  • Reading and Writing Data
  • Data types
  • Important Packages
  • Data Manipulation
  • Building models using learned algorithms
  • Evaluating and optimizing models

Orientation on Big Data and Hadoop

  • Awareness of Big Data and Hadoop
  • Nosql vs Sql
  • The four V’s
  • Is Big Data = Hadoop
  • Big Data and Cloud Computing
  • Generators of Big Data
  • Applications of Big Data

Exposure to Web and Mobile Analytics with focus on:

  • Text Analytics
  • Sentiment Analytics
  • Click Analytics, Google Analytics
  • Difference between Web and Mobile Analytics

 

business analysts

everyone that want to be certified

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