Diploma in Applied Data Science

Duration: 5 Months

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What will you learn?

Python
Tableau
Advanced Excel
SQL
Machine Learning
Statistics
Power BI
Application Fee

Rs.500 + GST

Expected Course Commencement Date

04-Nov-2023

Last Date to Apply

03-Nov-2023

Course Fee

Rs. 60,000 + GST

Class Timings

Weekday (Monday, Wednesday, Friday): 8.00 pm to 9.30 pm (Online) Live Interactive
Saturday & Sunday: 10 am to 5.30 pm (Physical Classroom Session at LIBA) Once a month

Course Plan

Hybrid Model – 120+ Hours (Classroom + Virtual)

Admission Process

  • Fill out the application form

  • Our expert team will review your application

  • Admission letter will be sent to your email ID

  • Pay the Course Fee

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, Freshers

  • Five plus Programming tools & Languages

  • 120+ hours (Virtual and Classroom Sessions)

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

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 Veer

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