Data Science for Non-Coders! - Dice Analytics

Data Science for

Don't Know Coding? No problem!

Data Science & Machine Learning is now effortless for Anyone!

In this live training you will explore how to get into Data Science with a non-technical background. You would be practicing on visual programming & drag-and-drop tools like KNIME to build Data Science & ML Models such as KNIME. This complete training will help you master all three elements of Data Science – Statistics, Tools, and Business Knowledge.


We’ll be covering the complete process of Data Science from Data Integration, Data Manipulation, Descriptive Analytics and Visualization to Statistical Analysis, using drag & drop tools and Machine Learning models, using Predictive Modeling, Regression Algorithms & Linear Regression.


By the end of this training, you will have enough knowledge and hands-on expertise in different tools of Data Science to use and apply them in the real world around you.


Training Outcomes

   Assignments & Quizzes

  Industry oriented project

  Get access to recorded lectures

  75% Hands-on!

  Dice shareable certificate

View Course Outline

Our Approach for Zoom Live Classes

After establishing a reputable Physical Training Model, based on our stellar records and customer earned trust we gradually progressed to establish the same reputation in the Live Training Model. We are committed to empower you by making our trainings accessible, interactive, and well curated specifically to your objective.

live training process 2


Starting Date

31 December ’22


8 weeks

Class Timings

Sat & Sun (11 AM – 4PM)



Meet the Instructor!

Meet the trainers of this course who are highly experienced Data Science experts!


Saad Naveed

Senior Data Scientist

Saad Naveed is associated with the industry for over 4 years now with experience in multiple domains. Currently, he is working as a Senior Data Scientist at Lebara Mobile KSA. He has an extensive experience in the field of Data Science, AI and had worked on several projects. Some of the use cases include designing and development of customer churn prediction, next best offer(nbo), customer lending, and default models using advanced analytical tools within the fintech domain. Prior to his current role, he worked within the fintech (esypaisa), ride-hailing, logistics, and e-commerce industry where he worked on several projects such as Loan default prediction, Customer Segmentation, Geo Clustering for efficient Marketing and Sales Purposes, etc.


Asad Abbas

Manager Product Growth - Digital Products

Asad Abbas is a Chemical Engineer turned MBA, who leverages the learnings from both in his day-to-day work. He is currently associated with HBL as Manager Product Growth - Digital Products, and has previously worked in growth hacking domain at Telenor. He has keen interests in growth marketing and consumer insights, employing analytical techniques and MarTech platforms to get results.



The following tool will be covered during the training through hands-on exercises

Course Outline

Week 1: Introduction

  • What is Data Science
  • Introduction to Big Data, Data Science, and Predictive Analytics
  • Fundamentals of Data Mining
  • Python/R a Myth
  • Data Based Decision Making and Industrial Practices
  • Difference Between Conventional Marketing and Data Science
  • Career Growth Tracks
  • Data Visualization Tools Overview
  • Marketing Intelligence Tools and AI
  • Drag & Drop Tools of Data Science
  • SAP Predictive Analytics Basic Understanding
  • What is CRISP-DM Methodology?
  • KNIME Installation
  • KNIME Hands One & Tool Understanding
  • KNIME Interface Walkthrough
  • What are Node and Workflows?

Week 2: Exploring KNIME

  • Import/ Export from Files to Databases
  • What are KNIME Extensions?
  • Exploring the KNIME Hub
  • Data Exploration & Understanding
  • Understanding Workspace
  • Creation of Personalized Workspace
  • Feature Engineering for Variables
  • Cleaning Data with Missing Value Node
  • Performing Univaraite Analysis & Bivariate Analysis
  • Different Methods for Data Sampling
  • Types of Data Sampling
  • Organizing Workflows through Comments & Annotations
  • Data Visualization (Box Plot, Graphs, etc)
  • Normal Distribution
  • Basics of Connecting Table (Joins)
  • Study Design and Scope of Data Science
  • Introduction to Kaggle and Anaconda

Week 3: Data Segmentation

  • Introduction to Supervised Learning
  • Approaches for Blending and Merging of Data
  • Filtering your data on basis of your row and column values
  • Modularization of Nodes into Meta Nodes
  • Understanding Joiners and Rule Engine Nodes
  • Understanding Data Aggregations
  • How does Feature Engineering Works?
  • Creating Multiple (~100) Variables from POS data using KNIME
  • Understanding Pre & Post Analysis for Value Upgradation
  • RFM Segmentation for Customer Segmentation & Loyalty
  • RFM Segmentation and its role in feature engineering
  • Insights Creation from a data set
  • Project #1: Wrapping up RFM Segmentation

Week 4: Clustering using Machine Learning

  • What is Machine Learning?
  • Understanding Unsupervised Learning
  • Limitations and Requirements of Unsupervised Learning
  • Understand Exploratory Data Analysis
  • Find hidden patterns using ML
  • Unsupervised Learning
  • Basics of Clustering
  • Understanding KMeans
  • Identify optimal number of K by Elbow Analysis
  • Introduction to Radar chart, pie chart, etc
  • Anomaly Detection in data and its removal
  • Feature Scaling and why does it matter?
  • Cluster Validation by Silhouette Coefficient
  • Multiple iterations for detecting optimal K
  • Benefits of Unsupervised learning
  • Project #2: Clusters Profiling

Week 5: Predictive Modeling

  • Supervised Learning Basics
  • Regression Algorithms
  • Logistic Regression Algorithm
  • Correlation Matrix Understanding
  • Understanding Performance Metrics
  • Confusion Matrix
  • Use Case: Fraud Evaluation
  • Understanding Precision, Accuracy, Recall, F Measure
  • Receiver Operating Characteristics
  • ROC Curve, AUC, TPR, FPR
  • KNN Model Complexity
  • Hands-On Lab: Building a Classifier
  • Churn Prediction Model to maintain service experience
  • Hands-On Activity: Determining the best split for Classification Models, Evaluation and Cross Validation
  • Industrial Practices and Guest Lecture

Week 6: Regression

  • Understand Value Functions
  • Revise straight line equations
  • Linear Regression
  • R-Squared and Adjusted R-Squared
  • Residual Plots and their Intuition
  • Multivariate Regression
  • Outliers Detection in Regression Data
  • Over-fitting & Under-fitting
  • Understand how to use best fit for decision making
  • Bias-Variance Trade-off
  • Types of Bias and Errors
  • Regularization
  • Set Parameters within a dataset
  • Industrial Practices for using regression model
  • Hands-On Lab: Building a Regression Model
  • Hands-On Activity: Evaluating Performance, Finding Maxima and Minima, Gradient Descent, Visualizing Features and Parameter

Week 7: Decision Making Tools

  • Decision Trees
  • Creation of Decision Tree in KNIME
  • Make class labels for features in Decision Trees
  • Evaluation optimal choice using Decision Trees
  • Importance of Gini Index for a model
  • Random Forest
  • Construction of multitude of decision trees
  • Association Rules
  • Application of Association Rule
  • Target variable based on several input variables
  • Support & Lift
  • Bagging, Boosting and Ensemble Methods
  • Recommendation Engine Understanding
  • Used Cases and Project Discussion

Week 8: Use Cases Discussion

  • Overview of Different Projects
  • Anomaly Detection
  • Churn Prediction
  • Credit Risk Modelling
  • Market Basket Analysis
  • Fraud Detection
  • Outlier Detection in Medical Claims
  • Social Media Clustering
  • Discussion on Final Project
  • Use of ML and AI in daily life
  • How to apply your learnings in real life situations
  • Application and usage of KNIME at your workplace
  • Answering business questions
  • Solve business problems using data

Pricing Details


Online Banking details will be shared by our representatives after you reserve your seat

  • Individual Pricing
    • PKR 30,000 Per Person
    • Total Charges for the Training
    • Book a Seat


Frequently Asked Questions

Who should attend the course?

Graduate or Masters Students with non technical background who want to start their career in the Data Science domain

People who are working in the non tech departments and want to advance their career in the field of Data Science

Executive who want to build a Data Science department in their start-ups/organizations

What is the timing of the course?

Duration: 8 weeks
Timings: 11:00 AM – 4:00 PM
Days: SAT & SUN

Who are the Instructors?

Asad Abbas 

Saad Naveed

How much hands-on will be performed in this course?

Since our courses are led by Industry Experts so it is made sure that content covered in course is designed with hand on knowledge of more than 70-75 % along with supporting theory.

What are the PC requirements?

For this Professional course, you need to have a PC with minimum 4GB RAM.

What if I miss any of the lectures?

Don’t worry! We have got you covered. You shall be shared recorded lectures after each session, in case you want to revise your concepts or miss the lecture due to some personal or professional commitment.

What sort of projects will be part of this Live Training?

This Certification Training course includes multiple real-time, industry-based projects, which will hone your skills as per current industry standards and prepare you for the future career needs.

Will I get a certificate after this course?

Yes, you will be awarded with a course completion certificate by Dice Analytics. We also keenly conduct an annual convocation for the appreciation and recognition of our students.

Can I get a job after this course?

Since our instructors are industry experts so they do train the students about practical world and also recommend the shinning students in industry for relevant positions.

Reserve your Seat

You can reserve your seat  by filling the form below