Introduction to Data Science

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Data science job trend is growing and according to data scientist jobs are toughest jobs to be filled and the best growth potential over the next seven years

Next Space brings you a comprehensive introductory course to kick start your Data Science journey

Course curriculum in Brief:

  • How to load data
  • How to clean real world data
  • How to make meaning from noisy data
  • How to model data using machine learning
  • How to visualize data
  • Introduction to R Programming, Python and Hadoop

Details Course Curriculum

Overview of Data science :

  • Big Data
  • Big Data applications
  • Characteristics of Big Data (Volume Variety and Velocity)
  • Challenges of Big data
  • Data science
  • Components of Data science
  • Data science prospects
  • Types of data scientists
  • Data science process

Data exploration and Presentation:

  • Data Visualization, Box Plot, Basic Statistics (descriptive statistics)
  • Introduction to Exploratory data analysis (EDA)
  • Background on Statistical methods for evaluation

 Probability distributions:

  • Probability definition,
  • Probability rules,
  • Important theorems in probability

Hypothesis Testing:

  • Hypothesis,
  • Steps in Hypothesis Testing,
  • Steps in Hypothesis Testing,
  • Different testing procedure.

 Correlation and Regression:

  • Cross tabulations,
  • Chi square,
  • correlation analysis,
  • Linear regression model,
  • regression coefficients,
  • Interpreting a model

Logistic Regression:

  • Assumptions,
  • Link functions,
  • Logit transformations,
  • Regression Model,
  • Regression coefficients and interpretation,
  • ROC Curve. (This is optional)

 Machine learning:

  • What is machine learning
  • Applications of machine learning
  • Data processing /manipulation
  • Background on supervised and supervised learning

Introduction to R Programming

Introduction to R:

  • Installation of R
  • Installing Packages/Libraries
  • Basic operations
  • Importing data

 Statistics in R:

  • Descriptive statistics,
  • Correlation,
  • Cross tabulation and Chi square

Predictive Models in R:

  • Linear Regression,
  • Logistic Regression

 Unsupervised Learning Models:

  • K means clustering in R

Supervised Learning Models:

  • KNN algorithms
  • Naïve Bayes theorem
  • Neural Networks
  • Support Vector Machines

Introduction to Hadoop and Python:

Python Basics:

  • Python Installation
  • Basic operations
  • Libraries

Hadoop Basics:

  • Introduction to Hadoop
  • Installation
  • Basic operations.

Trainer Profile

Tharangini has trained over 250 Individual and aided them in achieving excellence in their career. She has over 7 years of experience in training in analytics and consulting in analytics services. She has also worked in machine learning algorithms. And have expertise in R, SPSS and SAS

We intend to take in 10 participants only. If you are in town, I think it’s a good idea to book your early bird ticket now

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