Data Science Course using Codeless Methodology

Course Overview

This Data Science program using codeless methodology introduces you to statistics and mathematics. It also covers important topics related to machine learning algorithms like linear and logistic regressions, decision trees, naive bytes, and principal component analysis. As you progress you will overcome concepts related to deep learning algorithms like Artificial neural networks (ANN) and Convolutional neural networks (CNN) etc.


Data Science using codeless methodology is best suited for Beginners, working Professionals and Business Managers.


The Course Instructor is an Engineering Post Graduate from IIT Chennai and an MBA from IIM Kozhikode. He is a practicing Data Scientist with an overall experience of 25+ years in diverse domains such as Public Sector, Finance, Retail, Biometrics, Health, Manufacturing, and Energy Sectors. He currently works on building Machine Learning and Al solutions in sectors such as Retail, Manufacturing, and Energy sectors.

Course Curriculum

    Module 1

  • Introduction to Big Data, Data Science, ML & AI
  • Types of Data
  • Classification of Data
  • Balanced and Unbalanced data
  • Structured, un-structured and semi-structured data
  • Stages of Analytics
  • CRISP DM Project Management Methodology
  • Module 2

  • Concepts in Maths, Probability and Statistics.
  • Exploratory Data Analysis
  • Measures of Central Tendancy
  • Measures of Dispersion
  • Heteroscedasticity
  • Visualization Techniques
  • Module 3

  • Random Variables
  • Probability Distributions
  • Poission, binomial, normal distribution
  • Central limit theorem
  • Formulating Hypothesis
  • Inferential Statistics
  • Module 4

  • Introduction to Supervised Learning
  • Principles of regression
  • Modelling quality metrics
  • Understanding overfitiing and underfitiing
  • Modelling Quality Metrics
  • Confusion Matrix
  • Roc analysis
  • Module 5

  • Introduction to Unsupervised Learning
  • Distance Measures
  • Clustering
  • K-Means Clustering
  • Module 6

  • Support vector mechanics
  • Concepts of entropy
  • Information Gain
  • Decision Trees
  • Random Forests
  • Ensemble techniques
  • Module 7

  • Introduction to dimensional reduction
  • Eigen Values
  • Orthogonality
  • Principal component analysis
  • Linear Discriminant Analysis
  • Module 8

  • Association Rules
  • Market basket Analysis
  • Lift and Confidence
  • Apriori Algorithm
  • Collaborative Algorithm
  • Recommendation Engine
  • Module 9

  • Introduction to Neural Networks Perception
  • Multilayer Perception
  • Building Blocks of Neural Networks
  • Module 10

  • Artificial Neural Networks
  • Convolutional Neural Networks
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Program highlights

Learning facilitation
by experts

case studies

Key Features

  • 24/7 Support
  • Get IIITM Gwalior Certified
  • 16 week Exclusive program
  • Assignments
  • Project Work
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    Job oriented
    Placement success rate
    Years of
    Recruiting Top
    IT firms

    What our students say

    Madhuri N

    P Krishna SandeepSoftware Engineer, PopcornApps

    Throughout the duration of the Sudaksha Java programme, I did a lot of hard work and put focussed efforts in my learning and completing assignments and case studies. All these things gave me great scope to implement my learnings and made me confident about my skills and abilities. Subsequently I got a placement in popcornapps as software developer and I think my placement is the result of my hard work and focussed efforts.

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    Ramya Kalidindi,Software Developer, PopcornApps

    Initially I found the atmosphere at Sudaksha very new to me but being an adaptable girl, I adjusted myself to it in no time. Very soon I mingled with everyone and started learning. My motto was to understand, practice and learn. I followed it and with the help of my trainers, I successfully improved my skills quite a lot.

    Madhuri N

    V Sudharani

    I passed B Tech in 2016. As I am from CSE branch I had a little bit of knowledge in Java. But after joining Sudaksha I implemented every module in coding. I developed projects by myself in Sudaksha by understanding and utilizing every topic, not like B Tech projects. While doing our projects we came to know different technologies and tools. I am a self learner. After learning a technology concept in a class, I would understand it by myself though practicing and hands on.

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    Srilakshmi Grandhi

    I passed BTech in EEE in 2014. I am very much interested in software development field but I didn’t know any pertinent technology. One of my seniors suggested me to join sudaksha. When I joined sudaksha I had zero knowledge of Javaand after finishing with learning at sudaksha, I have good knowledge about Java. Learning Java in three months is not an easy task. But it became true in sudaksha.

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    Mounika Kodali

    As I’m from CSE background I had little bit knowledge on Java but, to achieve my dream that knowledge was not sufficient. To enhance my skills I needed a perfect platform. From one of my cousins I came to know about Sudaksha and joined Java course. Actually it was my first place where I developed three projects without any fear. I got two opportunities and finally I’m into Megasoft Pvt Ltd. I thank Sudaksha for helping me achieve the life what I had always dreamt for.

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