DATA SCIENCE TRAINING

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START:
November 11, 2020
PRICE
150,000.00

Address

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

DATA SCIENCE TRAINING

Course description

  • Why Should I Learn Data Science From JK Michaels?

    • DATA SCIENCE TRAINING: This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
    • According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
    • Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
    • Randstad reports that pay hikes in the analytics industry are 50% higher than the IT industry

    COURSE OBJECTIVES

    The Data Science  has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies.
    • Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
    • Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.

    WHAT YOU WILL LEARN IN THIS DATA SCIENCE TRAINING

    This data science training course will enable you to:
    • Gain a foundational understanding of business analytics
    • Install R, R-studio, and workspace setup, and learn about the various R packages
    • Master R programming and understand how various statements are executed in R
    • Gain an in-depth understanding of data structure used in R and learn to import/export data in R
    • Define, understand and use the various apply functions and DPYR functions
    • Understand and use the various graphics in R for data visualization
    • Gain a basic understanding of various statistical concepts
    • Understand and use hypothesis testing method to drive business decisions
    • Understand and use linear, non-linear regression models, and classification techniques for data analysis
    • Learn and use the various association rules and Apriori algorithm
    • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering

     

Lesson 00 – Course Introduction

  • Course Introduction

 

  • Lesson 01 – Introduction to Business Analytics
    • 1.001 Overview
    • 1.002 Business Decisions and Analytics
    • 1.003 Types of Business Analytics
    • 1.004 Applications of Business Analytics
    • 1.005 Data Science Overview
    • 1.006 Conclusion
    • Knowledge Check
  • Lesson 02 – Introduction to R Programming
    • 2.001 Overview
    • 2.002 Importance of R
    • 2.003 Data Types and Variables in R
    • 2.004 Operators in R
    • 2.005 Conditional Statements in R
    • 2.006 Loops in R
    • 2.007 R script
    • 2.008 Functions in R
    • 2.009 Conclusion
    • Knowledge Check
  • Lesson 03 – Data Structures
    • 3.001 Overview
    • 3.002 Identifying Data Structures
    • 3.003 Demo Identifying Data Structures
    • 3.004 Assigning Values to Data Structures
    • 3.005 Data Manipulation
    • 3.006 Demo Assigning values and applying functions
    • 3.007 Conclusion
    • Knowledge Check
  • Lesson 04 – Data Visualization
    • 4.001 Overview
    • 4.002 Introduction to Data Visualization
    • 4.003 Data Visualization using Graphics in R
    • 4.004 ggplot2
    • 4.005 File Formats of Graphic Outputs
    • 4.006 Conclusion
    • Knowledge Check
  • Lesson 05 – Statistics for Data Science-I
    • 5.001 Overview
    • 5.002 Introduction to Hypothesis
    • 5.003 Types of Hypothesis
    • 5.004 Data Sampling
    • 5.005 Confidence and Significance Levels
    • 5.006 Conclusion
    • Knowledge Check
  • Lesson 06 – Statistics for Data Science-II
    • 6.001 Overview
    • 6.002 Hypothesis Test
    • 6.003 Parametric Test
    • 6.004 Non-Parametric Test
    • 6.005 Hypothesis Tests about Population Means
    • 6.006 Hypothesis Tests about Population Variance
    • 6.007 Hypothesis Tests about Population Proportions
    • 6.008 Conclusion
    • Knowledge Check
  • Lesson 07 – Regression Analysis
    • 7.001 Overview
    • 7.002 Introduction to Regression Analysis
    • 7.003 Types of Regression Analysis Models
    • 7.004 Linear Regression
    • 7.005 Demo Simple Linear Regression
    • 7.006 Non-Linear Regression
    • 7.007 Demo Regression Analysis with Multiple Variables
    • 7.008 Cross Validation
    • 7.009 Non-Linear to Linear Models
    • 7.010 Principal Component Analysis
    • 7.011 Factor Analysis
    • 7.012 Conclusion
    • Knowledge Check
  • Lesson 08 – Classification
    • 8.001 Overview
    • 8.002 Classification and Its Types
    • 8.003 Logistic Regression
    • 8.004 Support Vector Machines
    • 8.005 Demo Support Vector Machines
    • 8.006 K-Nearest Neighbours
    • 8.007 Naive Bayes Classifier
    • 8.008 Demo Naive Bayes Classifier
    • 8.009 Decision Tree Classification
    • 8.010 Demo Decision Tree Classification
    • 8.011 Random Forest Classification
    • 8.012 Evaluating Classifier Models
    • 8.013 Demo K-Fold Cross Validation
    • 8.014 Conclusion
    • Knowledge Check
  • Lesson 09 – Clustering
    • 9.001 Overview
    • 9.002 Introduction to Clustering
    • 9.003 Clustering Methods
    • 9.004 Demo K-means Clustering
    • 9.005 Demo Hierarchical Clustering
    • 9.006 Conclusion
    • Knowledge Check
  • Lesson 10 – Association

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