WHAT IS DATA SCIENCE?

o Types of data scientists

o General tasks for data scientists

o Required skill set

DATA SCIENCE PROJECT LIFE CYCLE –OVERVIEW

DATA COLLECTION AND PRE-PROCESSING

GETTING STARTED WITH R PROGRAMMING 

o Introduction to ‘R’ Programming Interfaces : R console and R studio

o Understanding data types, reading data in R, data manipulation techniques

o Debugging in R

o Data Exploration

o Data Visualization

DATA ANALYTICS USING R 

Pre-requisites: Basic Statistics Concepts

o Advanced visualization techniques

o Test of hypothesis

o ANOVA

ALGORITHMS USING R

o Predictive modelling basics

o Introduction to CARET package

o Variable selection method

o Pre requisite to Predictive Modelling

o Fitting a Model to Data:

o Simple linear regression

o Interpretation of Results

o Assumptions Validations

o Introduction to Multiple regression analysis

o Logistic regression

o Decision Tree

o Market Basket Analysis

o Clustering Techniques: k -means Clustering

o k nearest neighbours

o Time Series Forecasting

INTRODUCTION TO PYTHON FOR DATA SCIENCE:

? Basics of Python (Hello world!)

o Variables and Types

o Manipulations

DATA STRUCTURES

o List

o Sub setting of List

o Nested List

o Manipulation Lists

o Dictionary

o Manipulation Dictionary

o Nested Dictionary

o Array

o Matrix

CONTROL FLOW, FUNCTION

o Understanding control flow

o Functions

o Methods

o Packages

INTRODUCTION TO NUMPY

o 1D Numpy array

o 2D Numpy array

o Matrix Manipulation

o Basic statistics with Numpy

o Linear Transformation in Matrix

INTRODUCTION TO MATPLOTLIB (FOR VISUALIZATION)

o Scatter Plots

o Bar Charts

o Line charts

o Faceting

o Histograms

o Box Plots

INTRODUCTION TO PANDAS

o Reading csv files

o Reading data from database

o Reading hdfs file and from json objects

o Reading .xlsx file

o Creating Data Frames using Dictionary and List

o Sub setting Data frames

WHAT IS DATA SCIENCE?

o Types of data scientists

o General tasks for data scientists

o Required skill set

DATA SCIENCE PROJECT LIFE CYCLE –OVERVIEW

DATA COLLECTION AND PRE-PROCESSING

GETTING STARTED WITH R PROGRAMMING 

o Introduction to ‘R’ Programming Interfaces : R console and R studio

o Understanding data types, reading data in R, data manipulation techniques

o Debugging in R

o Data Exploration

o Data Visualization

DATA ANALYTICS USING R 

Pre-requisites: Basic Statistics Concepts

o Advanced visualization techniques

o Test of hypothesis

o ANOVA

ALGORITHMS USING R

o Predictive modelling basics

o Introduction to CARET package

o Variable selection method

o Pre requisite to Predictive Modelling

o Fitting a Model to Data:

o Simple linear regression

o Interpretation of Results

o Assumptions Validations

o Introduction to Multiple regression analysis

o Logistic regression

o Decision Tree

o Market Basket Analysis

o Clustering Techniques: k -means Clustering

o k nearest neighbours

o Time Series Forecasting

INTRODUCTION TO PYTHON FOR DATA SCIENCE:

? Basics of Python (Hello world!)

o Variables and Types

o Manipulations

DATA STRUCTURES

o List

o Sub setting of List

o Nested List

o Manipulation Lists

o Dictionary

o Manipulation Dictionary

o Nested Dictionary

o Array

o Matrix

CONTROL FLOW, FUNCTION

o Understanding control flow

o Functions

o Methods

o Packages

INTRODUCTION TO NUMPY

o 1D Numpy array

o 2D Numpy array

o Matrix Manipulation

o Basic statistics with Numpy

o Linear Transformation in Matrix

INTRODUCTION TO MATPLOTLIB (FOR VISUALIZATION)

o Scatter Plots

o Bar Charts

o Line charts

o Faceting

o Histograms

o Box Plots

INTRODUCTION TO PANDAS

o Reading csv files

o Reading data from database

o Reading hdfs file and from json objects

o Reading .xlsx file

o Creating Data Frames using Dictionary and List

o Sub setting Data frames


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