Jigsaw Academy - Data Scientist Outline (1)

8/11/2019 Jigsaw Academy - Data Scientist Outline (1)

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Jigsaw Academy aims to meet the growing demand for

talent in the field of analytics by providing industry-relevant

training and education to develop business-ready

professionals


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Data Scientist Course: Analytics Techniques

Jigsaw Academy Education Pvt. Ltd.

Overview of Analytics What is analytics? Types of problems in analytics Case studies of application of analytics in

business When analytics does not work Analytics vs. data warehousing, OLAP,

Statistics Widely used analytic software Companies using analytics Day in the life of a business analyst Career path in analytics Qualities of a business analyst

BigData Analytics What is BigData? Applications Sample cases Structured vs. Unstructured Data Hadoop Ecosystem MapReduce Concepts BigData Tools Companies using BigData Caree path in Data Science Focus on Applications Case study Twitter Analysis

Analytic Methodology Problem definition Data selection

Data exploration Data partition Data cleansing Data transformation Modeling Validation Deployment Assessment Re-start

Module: Introduction to Analytics& BigData Overview

Models and Algorithms Modeling Terminology Linear Regression Logistics Regression Decision Trees MARS Rule Induction K-nearest Neural Network Genetic Algorithm

Problem Definition Basics of problem definition Case study - Car Insurance Case study - Credit Cards Case study - Telecom

Analytic Tools Overview of Analytic Tools GUI based and Programming based Excel SAS R

Others


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Data Scientist Course: Analytics Techniques


Data Preparation Why data prep Outlier treatment Missing values treatment Telecom case study Categorical variables Dummy variables Derived variables Lag variables Interaction variables Variable transformation Quadratic variables Date, time variables Sampling and partitioning Case study - Auto manufacturer

Regression

Basics of Regression Linear Regression Logistic Regression Interpretation of modeling results Violation of regression assumptions Insurance Case study

Decision Trees What are decision trees? Examples of trees Terminology in decision trees Data preparation for trees How to create a tree? Measure of effectiveness

Gini Chi-square Information gain Reduction in variance Others

Application of algorithms Case study - Fraud detection Case study - Car Insurance pricing Use of decision trees

Pros and cons What makes a good tree? When to use Decision trees? Widely used software for Decision

trees

Clustering What is clustering Types of clustering K-means clustering Measures of homogeneity Data prep Hierarchical clustering Cluster evaluation Cluster profiling When to use Important considerations Clustering in SAS - case study on

store clustering

Pitfalls to avoid while Modeling Misleading patterns

Biased population Data at wrong level Already known insights Un-actionable insights


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Data Scientist Course: Tools Training


Introduction to R What is R? Origins of R Current Status R Ecosystem Commercial products How R can help in Business Analytics,

Data Mining , Data Visualization R Installation

Windows Linux Using VMware for virtual partition CRAN and Packages

R Interfaces Command line Graphical User Interfaces

IDE Web Interfaces

Advantages and Disadvantages ofusing R

Data Input Data Import Spreadsheet Like Data Statistical File Formats Databases

Internet Data Data from Packages Using GUI for Data Import

Data Manipulation Transposing Dataset Conditional selection of rows,

columns, variables Using Reshape Merging Datasets

Data Exploration Types of Data in R Summarizing data in R Using GUI for Data Exploration Graphs in R

Module: R

Data Visualization Introduction to ggplot2 Plotting using R data frames Graphics on large data Visualizing Statistical Outputs

Regression modeling using R Logistic Regression Linear Regression

Creating a model and Scoring amodel

Understanding Model Output

(Coefficients, Fit, Residuals, R square,

P Value)

Decision Tress & Clustering in R Hierarchical Clustering K Means Clustering Decision trees using rpart package

How to export data? Exporting Graphs Saving code and output Exporting using GUI


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Working with Big Data


Big Data Overview What is Big Data? Generators Drivers Characteristics Structured vs. Unstructured Data Challenges

Big Data Analytics Comparison with traditional

Analytics Tools and Technologies Applications Sample Case Studies Project Life Cycle

Hadoop RDBMS Limitations No-SQL Databases History of Hadoop Why Hadoop Hadoop Ecosystem Big Data Applications Installation Modes

Working with Hadoop

MapReduce What is MapReduce? Map Process Reduce Process Anatomy of Map Reduce

program

Hadoop Components HDFS Overview HDFS Architecture HBASE Overview HIVE

FLUME

Module: Big Data Module

RHadoop for Big Data R & Hadoop Overview Why Rhadoop? Advantages Installation Configuration Sample Use Cases

Final Case Study Case Study: Twitter Analytics

using HIVE and FLUME


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Data Scientist Course: Statistics


Statistics Introduction to statistics Summary statistics

Mean Median Mode Variance

Random Variables Probability Probability distribution

Binomial Poisson Normal

Module: Statistics

Hypothesis testing Intuition Standard approaches

T-test One sample Two Sample

Chi-square test Variance Association

Multiple Sample Tests ANOVA Chi Square

Non parametric testing

Documents

Jigsaw Academy - Data Scientist Outline (1)