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INTRODUCTION TO DATA SCIENCE

➢ Market trend of Data Science

➢ Opportunities for Data Science

➢ What is the need for Data Scientists

➢ What is Data Science

➢ Data Science Venn Diagram

➢ Data Science Use cases

➢ Knowing the roles of a Data Science practitioner

➢ Data Science – Skills set

➢ Understanding the concepts & definitions of:

o Artificial Intelligence

o Machine Learning – Deep Learning

o NLP

o Computer Vision

DATA AND TOOLS

➢ What is Business Intelligence?

➢ What is ETL?

➢ Layers of a Data Warehouse

➢ OLAP VS OLTP

➢ Facts and Dimensions

➢ Big Data tools and it’s uses

➢ Big Data Stack

➢ Understanding Structured text Data

➢ Understanding Unstructured text Data

DATA SCIENCE DEEP DIVE

➢ Understanding Descriptive vs Predictive vs Prescriptive Analytics

➢ Difference between Analytics vs. Analysis

➢ Data Science Project Lifecycle

➢ Technology Stack Involved in the Lifecycle

o Machine Learning tools

o Development tools

o Languages

o Data Platforms

➢ CRISP – Cross-industry standard process for Data Mining

➢ 5WIH – The questions that kick start a ML project 80-20 Rule of Data Analytics

➢ Supervised Vs Unsupervised Learning

➢ Data Science – Use case bubble

➢ Data Mining

DATA

➢ Data Wrangling or Data Munging

➢ Data Categorization basics

➢ Different Types of Data

➢ Types of Data Collection

➢ Data Sources

➢ Data Collection plan

➢ Data Quality Issues

➢ Types of Data Error

➢ Ration Scale Vs Interval Scale

➢ Predictors/Features vs Predictions/Labels

➢ Understanding Imbalance in Data

STATISTICS & PROBABILITY

➢ What is Statistics

➢ Sample Vs Population

➢ Measure of central vs Dispersion

➢ Frequency Distribution

➢ Cumulative Frequency Distribution

➢ Mean, Median, Mode

➢ Quartiles/Percentile

➢ Range, Variance, Standard Deviation, Co-efficient of Variation

➢ 68-95-99 Rule of SD

➢ Z Score (Standard Score)

➢ P-Value

➢ Maximum Likelihood Estimation

➢ Probability vs Likelihood

➢ PDF vs PMF

➢ Normal Distribution of Data

➢ Skewness & it’s types

➢ Kurtosis & it’s types

➢ Kth Central Moments

➢ Co-Variance/Joint Probability Distribution

➢ Correlation

➢ Entropy

➢ ANOVA

➢ Chi-Square

➢ F tests

➢ Types of Data Distribution

SETUP

➢ Anaconda & Python

➢ Understanding Jupiter Notebooks

➢ Python Package Installation

➢ Tableau Installation

➢ Oracle Database & Server

DATA SOURCING, EXPLORATORY DATA ANALYSIS & READINESS

➢ Concept of List, Data frame, Dictionary

➢ Connecting to Databases using Python

➢ Importing data from csv, text, Excel

➢ Converting JSON, XML, to Data frame

➢ Understanding EDA

➢ Frequency Distribution

➢ Analyzing NA, blanks

➢ Using SQL concepts inside Python

DATA TRANSFORMATION/WRANGLING

➢ Handling missing Values

➢ Handling Outliers

➢ Normalization techniques

➢ Standardization techniques

➢ Regularization techniques

➢ Feature Extraction

➢ Train Test data selection

DATA SCIENCE CONCEPTS

➢ No Free Lunch

➢ Hypothesis vs Null Hypothesis

➢ BIAS VS Variance tradeoff

➢ Local Vs Global Minima/Maxima

➢ Bias – Loss/ Loss-Cost Function

LINEAR REGRESSION

➢ Understanding Regression math

➢ Linear Algebra concepts

➢ Least Mean Square

➢ Analyzing Co-relation

➢ Heat Maps, Pair Plots, Distribution Graphs

➢ Simple Vs Multiple Linear regression

➢ Train Test data selection

POLYNOMIAL REGRESSION

➢ Understanding the math

➢ Polynomial Algebra concepts

➢ Degree of Polynomial

CLASSIFICATION

➢ Overfitting/ Under fitting/ Optimal Fits

➢ Handling Categorical Data inside

➢ Confusion Matrix

➢ Type I & Type II errors

➢ Precision Vs Accuracy

➢ AUC/ROC curve

LOGISTIC REGRESSION

➢ Understanding the statistics behind Logistic Sigmoid

➢ Logistic regression math

RANDOM FOREST

➢ Understanding the Decision Tree & Bagging

➢ Math behind Classification and Regression in tree

➢ Decision Tree concepts

➢ Using Random Forest for Regression

➢ K fold Cross Validation

➢ Model Optimizers

➢ Hyper parameter Tuning

➢ Building aDecision TreesModel in R

NAÏVE BAYES THEOREM

➢ Understanding the Naïve Bayes theorem

➢ Bayesian Vs Gaussian theorems

➢ Using naïve Bayes for Regression

➢ Model Optimizers

➢ Hyper parameter Tuning

NLP FOR MACHINE LEARNING FEATURING

➢ Label Encoding

➢ One hot encoding

➢ Synonym treatment

➢ Stemming

➢ Lemmatization

➢ Stop words

➢ Parts Of Speech Tagging

➢ TF-IDF and its math Behind

SUPPORT VECTOR MACHINE

➢ Understanding the SVM Concept

➢ Hyper plane and Kernel

➢ Using SVM for Regression

➢ Grid Search

➢ Model Optimizers

➢ Hyper parameter Tuning

GRADIENT BOOSTING MACHINE & XGBOOST

➢ Understanding the Boosting Concept

➢ Hyper plane and Kernel

➢ Learning Rate

➢ Model Optimizers

➢ Hyper parameter Tuning

K MEANS CLUSTERING ALGORITHM

➢ Understanding Nearest Neighbors concept

➢ Statistics behind K Means Clustering Algorithm

KERAS TENSOR FLOW – MLP DEEP LEARNING (NEURAL NETWORKS)

➢ Understanding Deep learning

➢ MLP Vs other Deep Learning

➢ How Neural Network works & Architecture

➢ Activation functions.

➢ Model Optimizers

➢ Hyper parameter Tuning

➢ Best Practice and when to use DL

H2O.AI

➢ Introduction to H20.ai

➢ Pros and Cons

➢ Available models in H20.ai

SAMPLING & DIMENSION REDUCTION (DR)

➢ Introduction to Sampling

➢ Over sampling and Under sampling

➢ SMOTE/SMOTENC & Near Miss

➢ Pros and Cons of sampling

➢ Introduction to DR

➢ PCA & it’s code

DEPLOYMENT OF MODEL TO PRODUCTION

➢ Introduction to Pyinstaller

➢ Pickle and Joblib

TABLEAU BASICS

➢ Introduction to Tableau

➢ Data sources

➢ Exploratory Data Analysis

➢ Clustering Analysis and Inferences using Tableau

➢ Creating visualizations

ADDED FEATURES

➢ Resume Preparation

➢ Resume Preparation Tips

➢ Sample Resumes

➢ Soft copy of Notes for each module

➢ 4 case studies on use case diagrams & 2 Real-time project specifications