Data Science

data-science-classes-in-nagpur
Program Overview
Key Highlights
  • 1400+ Hours of Learning
  • Designed for Professionals
  • 15+ Case Studies and Assignments
  • 6 Practical Hands-on Capstone Projects
  • No Cost EMI Options available
  • Dedicated Student Success Mentor
  • Learning management system
  • Daily Doubt Resolution Sessions
  • Career Mentorship Sessions(1:1)
  • High Performance Coaching(1:1)
  • Exclusive Job opportunities
  • Personalised Industry Session
Tools Covered For Data Science
Python-img
Python
R-Language-img
R Language
SQL-img
SQL
Pandas-img
Pandas
Description
Data Science is the process of analysing and interpreting large volumes of data and to make marketing model on the basis of the database by using Python/R SAS etc. Business analytics involves management, business and computer science. The business part involves both a high-level understanding of the business as well as the practical limitations that exist where as the analytical aspect involves an understanding of data, statistics and computer science.
Skills You Will Learn
  1. Coding Skills & Programming
  2. SQL
  3. Python/R
  4. Marketing Strategies
  5. Business Finance
Syllabus
Module 1 : Introduction to Data Science
  • Python/R for Data Science
  • Introduction to Python/R
  • Dealing with Data using Python/R
  • Visualization using Python / R
  • Python-Markdown
  • Missing Value Treatment
  • Exploratory Data Analysis using Python/R
Module 2 : Marketing & CRM
  • Core Concepts of Marketing
  • Customer Life Time Value
  • Marketing Metrics for CRM
Module 3 : Statistical Methods for Decision Making
  • Descriptive Statistics
  • Introduction to Probability
  • Probability Distributions
  • Hypothesis Testing and Estimation
  • Goodness of Fit
Module 4 : Business Finance
  • Fundamentals of Finance
  • Working Capital Management
  • Capital Budgeting
  • Capital Structure
Module 5 : SQL Programming
  • Introduction to DBMS
  • ER Diagram
  • Schema Design
  • Key Constraints & Basics Of Normalization
  • Joins
  • Sub queries Involving Joins & Aggregations
  • Sorting
  • Independent Sub queries
  • Correlated Sub queries
  • Analytic Functions
  • Set Operations
  • Grouping and Filtering
Module 6 : Optimization Techniques
  • Linear Programming
  • Goal Programming
  • Integer Programming
  • Non-Linear Programming
Module 7 : Advanced Statistics
  • Analysis of Variance
  • Regression Analysis
  • Dimension Reduction Techniques
Module 8 : Predictive Modeling
  • Multiple Linear Regression(MLR) for Predictive Analytics
  • Logistic Regression
  • Linear Discriminant Analysis
Module 9 : Data Mining
  • Introduction to Supervised and Unsupervised Learning
  • Clustering
  • Random Forest
  • Decision Trees
  • Neural Networks
Module 10 : Time Series Forecasting
  • Introduction to Time Series
  • Correlation
  • Forecasting
  • Autoregressive Moving Average (ARMA) Models
  • Autoregressive Integrated Moving Average (ARIMA) Models
  • Case Studies
Module 11 : Machine Learning
  • Handling Unstructured Data
  • Machine Learning Algorithms
  • Bias Variance Trade-off
  • Handling Unbalanced Data
  • Boosting
  • Model Validation
Duration - 6 Months
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