Data Science Training in Hyderabad

Data Science course content

Introduction about Data Science:

What is data science?
Need of data science?
Use cases of Data science
How is data science different from business intelligence?
Who are data scientists?
What are the skills required and life cycle of data science?


1. R & Python programming

2. Model building



R & Python Programming:

Section 1: Data science with R & Python

Application of machine learning
Understand Business Analytics and R, Python
Knowledge on the R & python language
Community and ecosystem
Understand the use of 'R & python' in the industry
Compare R, Python with other software in analytics
Install R, Python and the packages useful for the course
Perform basic operations in R, Python using command line
Learn the use of IDE R, Pyhton Studio and Various GUI
Use the R, Python help feature in R, Python
Knowledge about the worldwide R, Pyhton community collaboration
Section 2: Introduction to R & Python Programming

The various kinds of data types in R, Pyhton and its appropriate uses
The built-in functions in R & Python like: seq(), cbind (), rbind(), merge()
Knowledge on the various Sub setting methods
Summarize data by using functions like: str(), class(), length(), nrow(), ncol()
Use of functions like head(), tail(), for inspecting data
Indulge in a class activity to summarize data
If Else
Nested If Else
For Loop
While Loop
Section 3: Data Manipulation in R & Python

The various steps involved in Data Cleaning
Functions used in Data Inspection
Tackling the problems faced during Data Cleaning
Uses of the functions like grepl(), grep(), sub()
Coerce the data
Uses of the apply() functions
Section 4: Data Import Techniques in R & Python

Import data from spreadsheets and text files into R & python
Import data from other statistical formats like sas7bdat and spss
Packages installation used for database import
Connect to RDBMS from R, pyhton using ODBC and basic SQL queries in R & Python
Basics of Web Scraping
Section 5: Exploratory Data Analysis

Understanding the Exploratory Data Analysis(EDA)
Implementation of EDA on various datasets
Boxplots
Understanding the cor() in R & Python
EDA functions like summarize(), llist()
Multiple packages in R & Python for data analysis
The Fancy plots like Segment plot
HC plot in R & Python
Section 6: Data Visualization in R & Python

Understanding on Data Visualization
Graphical functions present in R & Python
Plot various graphs like tableplot, histogram, boxplot
Customizing Graphical Parameters to improvise the plots
ggplot2


Model building:

Section 7: Data Pre-processing

Get the dataset
Importing the Libraries
Missing Data
Categorical Data
Splitting the Dataset into the Training set and Test set
Feature Scaling
Data Pre-processing Template!


Supervised Techniques:

Section 8: Regression

Simple Linear Regression
Multiple Linear Regression
Polynomial Regression
Support Vector Regression (SVR)
Decision Tree Regression
Random Forest Regression
Evaluating Regression Models Performance
R-Squared Intuition

Adjusted R-Squared Intuition

Interpreting Linear Regression Coefficients



Supervised Techniques Classification:

Section 9: Classification

Logistic Regression
K-Nearest Neighbours (K-NN)
Support Vector Machine (SVM)
Naive Bayes
Decision Tree Classification
Random Forest Classification
Evaluating Classification Models Performance
False Positives & False Negatives

Confusion Matrix

Accuracy Paradox

CAP Curve

CAP Curve Analysis



Unsupervised Techniques:

Section 10: Clustering

K -Means Clustering
Section 11: Association Rule Learning

Apriori (Market basket Analysis)
Section 12: Text mining

Sentiment analysis (Twitter)
Natural Language processing (NLP)
Section 13: Deep Learning

What is Deep Learning?

Artificial Neural networks (ANN)

The Neuron
The Activation Function
How do Neural Networks work?
How do Neural Networks learn?
Gradient Descent
Stochastic Gradient Descent
Back propagation
Convolutional Neural Networks (CNN, Image recognition)

What are convolutional neural networks?
Step 1 - Convolution Operation
Step 1(b) - ReLU Layer
Step 2 - Pooling
Step 3 - Flattening
Step 4 - Full Connection
SoftMax & Cross-Entropy
Section 14: Model Selection & Boosting

Model Selection

k-Fold Cross Validation

Grid Search

XGBoost

Section 15: Projects

2 Real time projects

Section 16: Statistics

Statistics will be covered during the course where ever it s required.



Tableau:

Section 1: Introduction about Tableau

Section 2: Tableau Basics: Your First Bar chart

Section 3: Timeseries, Aggregation, and Filters

Section 4: Maps, Scatterplots, and Dashboards

Section 5: Joining and Blending Data, PLUS: Dual Axis Charts

Section 6: Table Calculations, Advanced Dashboards, Storytelling

Section 7: Advanced Data Preparation



SQL:

1.Introduction about sql server

2.Introduction TSQL (transact structured query language)

3.Database

4.DML Commands

5.DQL

6.Joins

7.Functions

8. Stored procedures & views