## Data Science Pro

Data Science is an art of unfolding the story hidden behind the historical data using new generation software like Python, R and other statistical software. Our Data Science pro include Python, R Programming, Basic and Advance Statistics, Machine learning Algorithms, Artificial Intelligence Algorithms. Program has been designed by veteran industry professionals who have more than 15 years of industry experience. This Data Science program is designed considering current industry requirement and real scenarios at work place. Program has many real projects and case studies which helps learner to understand the complete Data Science concepts and applications.

## R Programming

- Vectors
- Matrices
- Dataframes
- Lists

- Excel
- CSV
- Text
- Flat files

- Numeric Functions
- Character Functions
- Date Functions
- User Defined Functions

- Computing New Variables
- Duplicate Values
- Sorting

- Labelling
- Numeric Formatting
- Character Formatting
- Date Formatting

- Merge
- Append

- If-else statements
- For Loops
- While Loops
- Repeat, Next, Break

- Scatterplots, Histograms, Barcharts, Dotplots
- Labels, Legends, Titles, Axes

## Python Programming

**Control Flow****If-THEN-ELSE****Loop with For and While****Break and Continue Statement****Range () Function**

**Function**- User Defined
- System Defined

**Data Structures****Linear Structure****Qeue, Stack and Deque Interfaces****List, Uset and SSet Interfaces****Tuples & Ranges****Strings**

**Classes****Names and Objects****Scopes and Namespaces****Classes and Variables****Odds and Ends**

**Exceptional Handling****File Handling****File Operations****Dealing with Errors****Importing Modules**

- Numpy
- Sckit-learn
- Panda
- NLTK
- PILLOW
- Scrapy
- Matplotlib

- Import Datasets and Modules
- Create and Import Python Modules

- Data Cleansing
- Variable Transformation
- Expoloratory Data Analysis
- Plotting the Graphs

## Machine Learning and Artificial Intelligence

**Descriptive Statistics****Hypothesis****Ttest**- One Sample T-Test
- Two Sample T-test
- Paired T-Test

**Anova**- One Way Anova
- Two Way Anova
- Multivariate Anova

**Chi-Square****Correlation****Interpreation of Statistical Results**

- Introduction
- Calculations, Equation and Assumptions
- Area of Application and Technique
- Validation Techniques
- Interpretation
- Training and Testing the Model

- Introduction
- Assumptions
- Area of Application and Methods
- Diagnostics
- Validation Methods
- Interpretation
- Training and Testing the Model

- Introduction
- Area of Application and Methods
- Hierarchal Clustering
- K-Means Clustering
- Segmentation of Clusters
- Interpretation of Outputs
- Validation

- Introduction
- Components
- Area of Applications
- Moving Average Methods
- Exponential Smoothing Method
- Holt Winter Method
- Box-Jenkins Method
- ARIMA
- Interpretation of Outputs
- Validation

- Introduction of Machine Learning and Components
- Supervised and Unsupervised Methods
- Sampling Techniques in Machine Learning
- Multi fold Validation Technique
- Bagging and Boosting Methods
- Ensemble Techniques
- Random Forest with Interpretation of Outputs
- Gradient Boosting Machines with Interpretation of Outputs
- XGBoost with Interpretation of Outputs

- Introduction
- Different Layers of Neural Network
- Multi Layers Neural Network
- Regression Problem
- Classification Problem
- Fine Tuning of Hyper Parameters
- Interpretation of Outputs
- Validation

- Introduction
- Regression Problem
- Setting of Hyper Parameters
- Interpretation of Outputs
- Validation

**RS 24,000**

**Mode: Classroom/Online (2 Months)**

### Data Science with R

Data Science with R Programming

### RS 29,000

### Mode: Classroom/Online (2 Months)

### Data Science with Python

Data Science with Python

## KEY FEATURES

5 Months of Classroom Training.

Session have assignments and daily task.

Recorded Videos and presentation for reference.

Books and related data sets in form of excel and csv.

Interview Questions for related topic.

Used Case Study and Projects to understand real scenarios.