Diploma in Applied Data Science

Duration:  5 Months

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What will you learn?

Python
Tableau
Advanced Excel
SQL
Machine Learning
Deep Learning
Data Modelling
Data Visualization
Application Fee

Rs.100 + GST

Expected Course Commencement Date

11th December 2022

Last Date to Apply

5th December 2022

Course Fee

Rs. 39,500 + GST

Class Timings

Weekday (Monday to Friday): 8.30 pm to 9.30 pm (Online) Live Interactive
Sunday: 10 am to 5.30 pm (Physical Classroom Session at LIBA) once a month

Course Plan

Hybrid Model – 100+ Hours (Classroom + Virtual)

Benefits of doing the programme

Industry Experts Session

1:1 Career Coaching Sessions

Structured Programme

Practical Case Studies – Datasets

Convenient Learning Format

Placement Assistance

Hybrid Mode of Delivery

Hands on
Learning

Programme Overview

  • Designed for working professionals, Students

  • 5+ Programming tools & Languages

  • Student Support is available 24 x 7, 7 days a week

  • 100+ hours of Virtual + Classroom Sessions

Job Opportunites

Data Analyst
Data Scientist
Business Analyst
Product Analyst
Data Engineer

Who is this Programme for?

Software Professionals

Research & Development Professionals

Marketing & Sales Professionals

IT Professionals

Engineers

Eligibility

Bachelor’s Degree with minimum 50% or equivalent passing marks.

No coding experience required

Faculty

Mr Karthik

CEO – Blackboard Learnings

Syllabus

  • Exploring Excel
  • Advance functions
  • Professional charts in Excel
  • Pivot table – Create, format, edit
  • Creating master pivot table
  • Slicers and get pivot data
  • Exploring different visualizations
  • Groups, Sets and Parameters
  • Calculated field, Level of Details (LOD)
  • Filtering, Slicing and dicing
  • Advanced data prep and analytics
  • Creating dashboards with KPIs
  • Art of Story telling
  • 2 Portfolio projects
  • Numerical and categorial variables
  • Mean, median, mode
  • Variance, correlation, regression
  • Central limit theorem
  • Confidence intervals
  • Hypothesis testing

 

  • Data exploration and manipulation
  • Data visualisation
  • Data manipulation
  • Multiple linear regression
  • Decision trees
  • Bagging and boosting
  • Regression metrics
  • Logistic regression
  • Tree based models- Decision tree, Random Forest, XG Boost
  • Support vector machine
  • Naïve byes
  • K nearest neighbours and k means clustering
  • Encoding categorical data
  • Missing value and outlier treatment
  • Feature engineering
  • Dimensionality reduction techniques
  • Ensemble modelling
  • Text analytics
  • Time series forecasting
  • Intuition – back propagation and activation function
  • Initialization and optimizers
  • Convolutional Neural Networks (CNN)
  • Introduction to SQL
  • Data joining
  • EDA with SQL
  • Create end to end ML models
  • Publish the model with user interface
  • Build project portfolio in Github
  • Connecting and shaping data
  • Creating table relationships
  • Data modelling in Power BI
  • Analysis and calculations with DAX
  • Visualizations with Power BI reports
  • Data transformation with Power query editor
  • 2 portfolio projects