DATA SCIENCE & MACHINE LEARNING BOOTCAMP
DUBAI

It will help you build foundation skills in Data Science and Business Analytics and learn how to apply it across industries and functions. It is a 2 month program which includes 3-day Bootcamp in Dubai, career designing and 2 live online sessions for query resolution.

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PROGRAM OVERVIEW

This bootcamp is a focused attempt to provide a solid foundation to everyone seeking to get started as a data scientist. It doesn’t matter if you are a fresher or IT professional or non-IT professional, all you need is a passion for problem solving. After completing this course you will possess all the skills required to build analytics solutions using R.

PROGRAM STRUCTURE

GETTING READY

Before you come in, get ready for the Bootcamp. Introduction to data science. A series of online tutorials will teach you the fundamentals of data science and introduce you to the basic statistics & excel

3 DAY BOOTCAMP

Learn and apply data science. 30 hours in 3 days. Build analytics solutions. Our carefully crafted curriculum provides the right mix of theory, hands-on labs & projects. Our experienced instructors bring case from frontlines to solidify learning.

BEYOND BOOTCAMP

More learning, capstone project, case studies & doubt clearing session. Get started on 3 capstone projects. Solidify your learning by accessing several assignments & new case studies. Doubt clearing sessions will be conducted as well.

WHY SHOULD YOU DO THIS BOOTCAMP?

CUTTING EDGE CURRICULULM

COURSE CONTENT HAND CRAFTED (& NOT COPY PASTED) BY CONSULTING EXPERTS. EVERY THING IS LEARNT THROUGH PRACTICAL CASE STUDIES & INDUSTRY PROJECTS

BUILDS SOLID FOUNDATION

3 FOCUSSED DAYS WITH US IS ALL YOU NEED TO GET STARTED ON ANALYTICS SOLUTION BUILDING USING DATA FROM YOUR ORGANIZATION OR FROM OPEN DATA SOURCES

ON THE GO LEARNING

ANYTIME ACCESS ONLINE – IMMERSIVE LEARNING VIDEOS, RECORDED SESSIONS, ASSIGNMENTS, PROJECTS, SOLUTIONS & DOUBT CLEARING SUPPORT THROUGH OUR LEARNING SYSTEM

INDUSTRYMENTORSHIP

RECEIVE 1-TO-1 GUIDANCE FROM INDUSTRY EXPERTS ON HOW TO GET STARTED IN ANALYTICS CAREER OR HOW TO GROW WITHIN YOUR CURRENT ORGANIZATION

COURSE DETAIL – BOOTCAMP (3 Days)

R PROGRAMMING

  1. R overview. Installation. Packages & walkthrough.
  2. Data structures (Vector, array, factors, data frames, lists).
  3. Arithmetic & logical operators.
  4. Subsetting. Missing, indefinite & infinite values.
  5. For loops. While loops. Nested loops.
  6. Disadvantage of using loops.
  7. Alternates to loops.
  8. Understand the structure of function. Build your own function.
  9. Package installation & updates.
  10. Access package functions.
  11. Hack a function.
  12. Build your own package.
  13. Save, load & delete objects.
  14. Data import & export in R
  15. Sort & rank.
  16. Data Aggregation.
  17. Merging.
  18. Apply, Lapply, Tapply,
  19. By, Replicate functions.
  20. Data visualization in R (plot, ggplot)

CLUSTERING

  1. Introduction to clustering.
  2. Types of clustering & their uses.
  3. Data standardization
  4. K-Means clustering.
  5. Cluster profiling & validationo

LINEAR REGRESSION

  1. Introduction to linear regression technique & its uses.
  2. Details of ordinary least squares estimation technique.
  3. Modeling steps & variable handling.
  4. Model statistics interpretation.
  5. Outlier treatment
  6. Missing value treatment
  7. Covariance & Correlation
  8. Multicollinearity & Variance inflation factor
  9. Model development & validation
  10. Validation of linear regression assumptions.
  11. Metrics to measure model performance.

LOGISTIC REGRESSION

  1. Introduction to logistic regression technique & its uses.
  2. Maximum likelihood estimation technique
  3. Modeling steps.
  4. Dependent variable definition.
  5. Variable handling.
  6. Model statistics interpretation.
  7. Model development & validation
  8. Concordance
  9. Sensitivity, specificity & Accuracy
  10. Misclassification matrix
  11. KS statistic & Lift chart
  12. Weight of Evidence & Information Value.
  13. Variable reduction.
  14. Model statistics interpretation.
  15. Metrics to measure model performance

MACHINE LEARNING

  1. Introduction to machine learning
  2. Decision Trees & Random Forest
  3. Arithmetic & logical operators.
  4. Subsetting. Missing, indefinite & infinite values.
  5. For loops. While loops. Nested loops.
  6. Disadvantage of using loops.
  7. Alternates to loops.
  8. Understand the structure of function. Build your own function.
  9. Package installation & updates.
  10. Access package functions.
  11. Hack a function.
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