Data Science Pro

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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

Data Structure

  1. Vectors
  2. Matrices
  3. Dataframes
  4. Lists

Functions

  1. Numeric Functions
  2. Character Functions
  3. Date Functions
  4. User Defined Functions

Data Transformation

  1. Computing New Variables
  2. Duplicate Values
  3. Sorting

Data Formatting

  1. Labelling
  2. Numeric Formatting
  3. Character Formatting
  4. Date Formatting

Restructuring Data

  1. Merge
  2. Append

Looping Structure

  1. If-else statements
  2. For Loops
  3. While Loops
  4. Repeat, Next, Break

Graphics

  1. Scatterplots, Histograms, Barcharts, Dotplots
  2. Labels, Legends, Titles, Axes

Python Programming

Python Programming

  1. Control Flow
    1. If-THEN-ELSE
    2. Loop with For and While
    3. Break and Continue Statement
    4. Range () Function
  2. Function
    1. User Defined
    2. System Defined
  3. Data Structures
    1. Linear Structure
    2. Qeue, Stack and Deque Interfaces
    3. List, Uset and SSet Interfaces
    4. Tuples & Ranges
    5. Strings
  4. Classes
    1. Names and Objects
    2. Scopes and Namespaces
    3. Classes and Variables
    4. Odds and Ends
  5. Exceptional Handling
    1. File Handling
    2. File Operations
    3. Dealing with Errors
    4. Importing Modules

Useful Data Science Libraries

  1. Numpy
  2. Sckit-learn
  3. Panda
  4. NLTK
  5. PILLOW
  6. Scrapy
  7. Matplotlib

Import Datasets and Modules

  1. Import Datasets and Modules
  2. Create and Import Python Modules

Data Preparation and Visulization

  1. Data Cleansing
  2. Variable Transformation
  3. Expoloratory Data Analysis
  4. Plotting the Graphs

Machine Learning and Artificial Intelligence

Basic Statistics

  1. Descriptive Statistics
  2. Hypothesis
  3. Ttest
    1. One Sample T-Test
    2. Two Sample T-test
    3. Paired T-Test
  4. Anova
    1. One Way Anova
    2. Two Way Anova
    3. Multivariate Anova
  5. Chi-Square
  6. Correlation
  7. Interpreation of Statistical Results

Linear Regression Model for Prediction

  1. Introduction
  2. Calculations, Equation and Assumptions
  3. Area of Application and Technique
  4. Validation Techniques
  5. Interpretation
  6. Training and Testing the Model

Logistic Regression Model for Prediction

  1. Introduction
  2. Assumptions
  3. Area of Application and Methods
  4. Diagnostics
  5. Validation Methods
  6. Interpretation
  7. Training and Testing the Model

Cluster Analysis for Segmentation

  1. Introduction
  2. Area of Application and Methods
  3. Hierarchal Clustering
  4. K-Means Clustering
  5. Segmentation of Clusters
  6. Interpretation of Outputs
  7. Validation

Time Series for Forecasting

  1. Introduction
  2. Components
  3. Area of Applications
  4. Moving Average Methods
  5. Exponential Smoothing Method
  6. Holt Winter Method
  7. Box-Jenkins Method
  8. ARIMA
  9. Interpretation of Outputs
  10. Validation

Machine Learning Algorithms:

  1. Introduction of Machine Learning and Components
  2. Supervised and Unsupervised Methods
  3. Sampling Techniques in Machine Learning
  4. Multi fold Validation Technique
  5. Bagging and Boosting Methods
  6. Ensemble Techniques
  7. Random Forest with Interpretation of Outputs
  8. Gradient Boosting Machines with Interpretation of Outputs
  9. XGBoost with Interpretation of Outputs

Artificial Neural Networking

  1. Introduction
  2. Different Layers of Neural Network
  3. Multi Layers Neural Network
  4. Regression Problem
  5. Classification Problem
  6. Fine Tuning of Hyper Parameters
  7. Interpretation of Outputs
  8. Validation

Support Vector Machine

  1. Introduction
  2. Regression Problem
  3. Setting of Hyper Parameters
  4. Interpretation of Outputs
  5. Validation
Course Detail
Fees:
Rs 35,000

 

Class Mode:
Classroom / Online

 

Duration:
3 Months

 

Course Contents:
Data Science with Python & R programming

 

Course Benefits:
Student will able to learn Python, R , Machine learning & AI

 

Apply Now

RS 24,000

Mode: Classroom/Online (2 Months)

Data Science with R

Data Science with R Programming

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RS 29,000

Mode: Classroom/Online (2 Months)

Data Science with Python

Data Science with Python

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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.

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Data
Science

Machine
Learning

Artificial
Intelligence

Python
Programming

R
Programming

Data
Visualization

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