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Call:91-9666019191 For Enquiries! ## Data science Training in Hyderabad

#### Data Science Cource

• Recap of Demo
• Introduction to Types of Analytics
• Project life cycle
• 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
• 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

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

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

### Data science training

15,000
• 20 students
• Duration : 40days
• Hours : 50
Popular

### WORKING HOURS

 Monday 07:00 AM TO 10:00 PM Tuesday 07:00 AM TO 10:00 PM Wednesday 07:00 AM TO 10:00 PM Thursday 07:00 AM TO 10:00 PM Friday 07:00 AM TO 10:00 PM Saturday 07:00 AM TO 10:00 PM Sunday 07:00 AM TO 10:00 PM
##### Supervised-Classifiers
• 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)

• Network Topology
• Support Vector Machines
• Concept with a business case
• ARMA (Auto-Regressive Moving Average), Order p and q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q

##### Supervised - 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
• Lasso and Ridge Regressions

• Logit and Log Likelihood
• Category Baselining
• Modeling Nominal categorical data
##### Data Mining Unsupervised - Clustering
• Hierarchial Clustering / Agglomerative Clustering
• K-Means Clustering
• Why dimension reduction
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix algebra
• SVD – Decomposition of matrix data
• Definition of a network (the LinkedIn analogy)
• Introduction to Google Page Ranking
• What is Market Basket / Affinity Analysis
• Measure of association
• Support
• Confidence
• Lift Ratio
• Apriori Algorithm
• Sequential Pattern Mining