DATA SCIENCE

data science _ image

Data Science is the study of the generalizable extraction of knowledge from data. Being a data Scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and programming languages along with a good understanding of the craft of problem formulation to engineer effective solutions.

This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset.

-Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.

-The focus in the treatment of these topics will be a balanced approach on breadth and depth, and emphasis will be placed on integration and synthesis of concepts and their application to real time problems.

-To make the learning contextual, real datasets from a variety of disciplines will be used.

 

 

Program Highlights

  • Most Comprehensive Curriculum
  • Trained by passionate and Industry experts
  • Each concept will be explained by golden rule

Theory -> Example -> Software Implementation (R/Python) -> Real-Time applicability

  • Designed for the Industry
  • Live Project
  • Placement Assistance

Audience

Any degree. No programming and Statistics knowledge required.

Duration & Mode of Training

3 months, Online Training

Course Content

Introduction to Data Science

Introduction to Data Science – the 3 W’s

  • What is Data Science?
  • Why now?
  • Where Data Science is applicable?

Business Statistics

Introduction to statistics

Summarizing Data

  • Central Tendency measures – Mean, Median and Mode
  • Measures of Variability – Range, Interquartile Range, Standard Deviation and Variance
  • Measures of Shape – Skewness and Kurtosis
  • Covariance, Correlation

Data Visualization

  • Histograms
  • Pie charts
  • Bar Graphs
  • Box Plot

Probability basics

Parametric and Non parametric Statistical Tests

  • ‘f’ Test
  • ‘z’ Test
  • ‘t’ Test
  • Chi-Squaretest Probability Distributions
  • Expected value and variance
  • Discrete and Continuous
  • Bernoulli Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Exponential Distribution
  • Empirical Rule
  • Chebyshev’s Theorem

Sampling methods and Central Limit Theorem

  • Overview
  • Random sampling
  • Stratified sampling
  • Cluster sampling
  • Central Limit Theorem Hypothesis Testing
  • Type I error
  • Type II error
  • Null and Alternate Hypothesis
  • Reject or Acceptance criterion
  • P-value

Confidence Intervals

ANOVA

  • Assumptions
  • One way
  • Two way

Artificial Intelligence – Machine Learning Introduction

Introduction to Machine Learning

  • What is Machine Learning?
  • Statistics (vs) Machine Learning
  • Types of Machine Learning

-Supervised Learning

-Un-Supervised Learning

-Reinforcement Learning

Artificial Intelligence – Supervised Machine Learning

Classification

  • Nearest Neighbor Methods (knn)
  • Logistic

Tree based Models – Decision Tree

  • Basics
  • Classification Trees
  • Regression Trees

Probabilistic methods

  • Bayes Rule
  • Naïve Bayes Regression Analysis
  • Simple Linear Regression
  • Assumptions
  • Model development and interpretation
  • Sum of Least Squares
  • Model validation
  • Multiple Linear Regression

Regression Shrinkage Methods

  • Lasso
  • Ridge

Advanced Models – Black Box

  • Support Vector Machine
  • Neural Networks

Ensemble Models

  • Bagging
  • Boosting
  • Random Forests Optimization
  • Gradient Descent (Batch and Stochastic) Recommendation Systems
  • Collaborative filtering

-User based filtering

-Item based filtering

Artificial Intelligence – Unsupervised Machine Learning

Association Rules (Market Basket Analysis)

  • Apriori

Cluster Analysis

  • Hierarchical clustering
  • K-Meansclustering

Dimensionality Reduction

  • Principal Component Analysis
  • Discriminant Analysis (LDA/GDA)

Model Validation

Confusion Matrix

ROC Curve (AUC)

Gain and Lift Chart

Kolmogorov-Smirnov Chart

Root Mean Square Error (RMSE)

Cross Validation

  • Leave one out cross validation (LOOCV)
  • K-foldcross validation

Artificial Intelligence – Natural Language Processing

Introduction to Natural Language Processing

Sentiment Analysis

Text Similarity

Artificial Intelligence – Deep Learning

Deep Learning Introduction

Convolutional Neural Network

Recurrent Neural Network

R Programming Language

Introduction

  • R Overview
  • Installation of R and RStudio software
  • Important R Packages
  • Datatypes in R – Vectors, Lists, Matrices, Arrays, Data Frames

Decision making & Loops

  • If-else,while, for
  • Next, break. try-catchFunctions
  • Writing functions
  • Nested functions

Built-in functions

  • Vapply, Sapply, Tapply, Lapply etc. Data Preparation/Manipulation
  • Reading and Writing Data
  • Summarize and structure of data
  • Exploring different datasets in R
  • Sub Setting Data Frames
  • String manipulation in Data Frames
  • Handling Missing Values, Changing Data types, Data Binning Techniques, Dummy Variables

Data Visualization using ggplot2

  • Basic charts – Histograms, Bar plots, Line graphs, Scatter plots etc.

Python Programming Language

Introduction

  • How is Python different from R
  • Installing Anaconda- Python
  • Setting up with spyder

Datatypes in Python

Importing modules

Introduction to Strings

String manipulation

Control loops:

  • For
  • While
  • If else

Functions

  • Lambda
  • apply

Numpy

Pandas

  • Introduction to Dataframes
  • Conversion of written R codes into python

Scipy-Machine Learning in Python

Beautiful Soup

Matplotlib