Data Science Training in Hyderabad|Data Science Course Institutes In Hyd

Jacinta InfoTech

## DATA SCIENCE

Module 1 – Data Science Project Lifecycle

• Installation of Python IDE
• Anaconda and Spyder
• Working with Python and some basic commands& Examples
• Introduction to R and RStudio with some basics

Various graphical techniques to understand data

• Bar plot
• Histogram
• Box plot
• Scatter plot
• The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
• Random Variable and its definition
• Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function

Basic Statistics

Various sampling techniques

• Measure of central tendency
• Mean / Average
• Median
• Mode
• Measure of Dispersion
• Variance
• Standard Deviation
• Range
• Expected value of probability distribution
• Measure of Skewness
• Measure of Kurtosis
• Normal Distribution
• Standard Normal Distribution / Z distribution
• Z scores and Z table
• QQ Plot / Quantile-Quantile plot

• Sampling Variation
• Central Limit Theorem
• Sample size calculator
• T-distribution / Student’s-t distribution
• Confidence interval
• Population parameter – Standard deviation known
• Population parameter – Standard deviation unknown

Module 3 – Hypothesis Testing

Introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.

• Type I and Type II errors
• ANOVA
• Chi-Square test

High-Level overview of Machine Learning

• Supervised Learning
• Classifier
• Regression
• Unsupervised Learning
• Clustering

Supervised – Classifiers

Module 4 – Machine Learning Classifiers – KNN

Module 5 – Classifier – Naive Bayes

Module 6 – Decision Tree

Module 7 – Logistic Regression

• Simple Logistic Regression
• Multiple Logistic Regression
• Confusion matrix
• False Positive, False Negative
• True Positive, True Negative
• Sensitivity, Recall, Specificity, F1
• Receiver operating characteristics curve (ROC curve)

Module 8 – Bagging And Boosting

Module 9 – Black Box Methods

• Network Topology
• Support Vector Machines

Module 10 – Survival Analysis

• Concept with a business case

Module 11 – Forecasting

• ARMA (Auto-Regressive Moving Average), Order p and q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
•
• Supervised – Regression

Module 12 – Linear Regression

• Scatter Diagram
• Correlation Analysis
• Principles of Regression
• Ordinary least squares
• Simple Linear Regression
• Understanding Overfitting (Variance) vs Underfitting (Bias)
• LINE assumption
• Collinearity (Variance Inflation Factor)
• Linearity
• Normality
• Multiple Linear Regression

Module 13 – Polynomial Regression

Module 14 – Decision Tree & Random Forest

Module 15 – Regularization Techniques

• i).Lasso and Ridge Regressions

Data Mining Unsupervised- Clustering

Module 17 – Data Mining Unsupervised – Clustering

• HierarchialClustering / Agglomerative Clustering
• K-Means Clustering

Module 18 – Dimension Reduction

• Why dimension reduction
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix algebra
• SVD – Decomposition of matrix data

Module 19 – Data Mining Unsupervised – Network Analytics

• Definition of a network (the LinkedIn analogy)
• Introduction to Google Page Ranking

Module 20 – Data Mining Unsupervised – Association Rules

• What is Market Basket / Affinity Analysis
• Measure of association
• Support
• Confidence
• Lift Ratio
• Apriori Algorithm
• Sequential Pattern Mining

Module 21 – Data Mining Unsupervised – Recommender System

Module 22 – Text Mining

Module 23 – Natural Language Processing

Assignments/Projects/Placement Support

Module 24 – Assignments

Module 25 – Projects

1. Mini Project
2. Major Project

Module 26 – Resume Prep and Interview Support