DATA SCIENCE AND BIG DATA ANALYTICS

This Data Science and Big Data Analytics course educates participants to a foundation level on big data and the state of the practice of analytics
COURSE OBJECTIVE:
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LESSON PLANS


​SESSION 1: DATA ANALYTICS AND BIG DATA INTRODUCTION:
Session Goal:
  • Overview of Data Analytics.
  • Overview of Data Scientist and Data Analytics Role.
  • Overview of Big Data.
  • Importance of Big Data and Data Analytics in current business Scenarios.
  • Tools used for Data Analytics Purpose.
  • Hadoop Introduction.
  • Map Reduce Introduction.
  • Hive, PIG and Sqoop Introduction.

SESSION 2: STATISTICS:
Session Goal:
  • Data Exploration Technique.
    • Exploring Categorical Data.
    • Exploring Numerical Data.
    • Numerical Summaries.
  • Correlation & Linear Regression.
    • Linear Regression and Modeling.
    • Scatterplots and Outliars.
    • Correlation Concepts.
    • Simple Linear Regression.
    • Understanding the Linear Model.
    • Regression Model.
  • Correlation & Linear Regression.
    • Regression Models in detail.
    • Regression Coefficients.
    • Interpretation of Model Fit.
    • Standard Error of Residuals.
  • Inference
    • Idea of Inference.
    • Randomized Distributions.
    • Randomization Dotplot.
    • Randomization Distribution.
    • Completion of Randomization Test.
    • Hypothesis Testing Errors.
    • Confidence Intervals.

​SESSION 3: MACHINE LEARNING:
Session Goal:
  • Machine Learning.
    • Machine Learning Introduction.
    • Performance Measures.
    • Classification.

  • Regression and Clustering.
    • Regression with Machine Learning.
    • Clustering with Machine Learning.
  • Regression and Classification Models.
    • Regression Models with Machine Learning.
    • Classification Model with Machine Learning.
    • Tuning Model Parameters.
  • Selecting Models.
    • Case Study for Selecting Models​.

SESSION 4: R PROGRAMMING LANGUAGE:
Session Goal:
  • Introduction to R
    • Introduction to Vectors.
    • Introduction to Matrices.
    • Introduction to Factors.
    • Introduction to DataFrames.
    • Introduction to List.
  • Intermediate R
    • Conditional and Controls.
    • If and Else Statements.
    • Loops in R.
    • Functions in R.
    • Advanced Functions in R.
    • lapply Family.
    • sapply Family.
    • vapply Family.
    • grep1 Function.
    • Date and Time Functions.
    • String Functions.
  • Importing Data in R
    • Importing Data from Flat File.
    • Importing data from Excel File.
    • Importing data from database.
    • Importing data from WebSite.
    • Importing data from Statistical Software Package.
  • Cleaning Data in R.
  • Text Mining Techniques in R.
    • Text Mining Concepts.
CASE STUDY AND PROJECTS:
Case studies are Integral part of Training. As part of this course we will ensure you implement Real-time case studies ​in various domains which includes:
  1. Banking.
  2. Telecom
  3. Ecommerce.
  4. HealthCare.​
​These case studies will be evaluated by domain experts and you would get an opportunity to get Feedback on the work.
TRAINING FEATURES:
1) Extensive Real Time Live Examples, Projects & POCs for improved practical competency, ensure deployment readiness and implementation.
2) Custom Lab, Software and Environment provided with Real-time Project Simulation.
3) Recorded Videos complemented with corresponding lecture ppts, materials & lab guides. (Provided in the form of MP4 videos, pdf, ppt for offline access as well).
4) Certification and Job-Interview Counselling & Coaching after every training.​