Data Science for Non Coders - Dice Analytics

Data Science for Non Coders

An Applied Training on Data Science for Non-Techies. 

In this live training you will explore how to get into Data Science with a non-technical background. You would be practicing on handy tools such as KNIME and others obviated by programming to work on advance models of Machine Learning & Data Science. 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.

View Course Outline Reserve your Seat



To be announced soon!


8 weeks


To be announced soon!


Limited Seats Available!

Meet the Instructors!

Meet the trainer of this course who is a highly experienced Data Science expert!


Mr. Ali Raza Anjum

Data Science, Machine Learning & Big Data Consultant & Trainer - Dice Analytics

Mr. Ali Raza Anjum has 10+ years of experience in well renowned telecom companies of Pakistan. His key expertise are in domain of Data Science, Machine Learning, Big Data, Data Visualization, Data driven Marketing & Customer value management. He is currently working with World Bank and analyzing data driven patterns and facts.

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: Used 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


Following is price for this extensive training on Data Science for Non Techies

Price for an Individual participant
Rs 30,000 per person
Group of Two
7% Discount for a group of two people
Rs 27,900 per person
Group of Three
10% Discount for a group of Three people
Rs 27,000 per person
Group of Four
15% Discount for a group of four people
Rs 25,500 per person



Instructor-led sessions

Real-life case studies




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: 07:00 PM – 09:00 PM
Days: Mon – Fri

Who are the Instructors?

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.

How much internet speed is required?


How will I perform hands-on tools?

For executing the practicals included in the Data Science for Non Techies Training, you will set-up tool on your machine. The installation manual for tool prep will be provided to help you install and set-up the required environment.

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.

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