Data Science & Machine Learning - Dice Analytics

Data Science &
Machine Learning

Get Hybrid Training from Top-notch Experts!

Presenting an Applied Hybrid Training on Data Science & Machine Learning.

In this hybrid training you will learn about machine learning algorithms and its applications. Further you will also be guided how to use the machine learning algorithms in Python. This course will cover data sets from multiple domains and how to apply Machine Learning algorithms on the available data, how to get value out of Machine Learning algorithms, and how to present the output of those algorithms.


By the end of this training, you will have enough knowledge and hands-on expertise in Python to use and apply them in the real world around you. Also, you will be able to get prepared for  certifications of Data Camp and Cognitive AI.

View Course Outline Pricing Book a Seat



04 March

Duration & Timing

8 Weeks

Sat – Sun (11AM-4PM)


Urdu / Hindi


Limited Seats Available!

Pinned Location for Onsite Mode

Dice Analytics, Blue Area, Islamabad

(Click above for location)

Training Mode

Hybrid (Onsite + Online LIVE)

Meet the Instructors!

Meet the trainers of this course who are Data Science Experts!


Ahmed Niazi

Data Scientist @ Telenor

Ahmed Niazi is currently associated with Telenor Pakistan and has worked as a Data Scientist at MTBC & Huawei. He holds his expertise in Deep Learning, Machine Learning & Python and has acquired multiple certifications in the domain. He also possesses a strong grip over concepts like Classification, Regression, Clustering, Rule Mining and the Stats involved in the domain.


Anees Akhter

Data Scientist @ Zong

Anees Akhter is a Data Scientist with over 5 years of experience in leading Telcos of Pakistan for building and managing end-to-end Machine Learning pipelines. He has a passion for building unique and creative solutions to complex problems. His work focuses on innovating in areas such as Customer Value Management, Network Cost Optimiztion, Network Performance Optimization and Natural Language Processing.

Course Outline

Week 1

Basics of Data Science Flow
Anaconda Installation
Intro to Jupyter Notebook
Intro to Python
Python Objects & Data Structure
Subsetting (Strings, Lists, Dictionaries)
Python Comparison Operators
Python Statements
Methods & Functions
Importing Data in Python
NumPy & Pandas Basics in Python
Subsetting Dataframes in Pandas
Data Aggregation using Group By, Pivot and Melt
Hands-On Assignment of Python

Week 2

Interactive Discussions on Last Weeks Assignments
Types of Variables
Data Visualizations (Scatter plot, Histogram, Bar plots, Line plots, Heat maps)
Data Centricity (Mean, Modes, Median, STD, Variance, Interquantile Range).
Box plot
Data Transformation (Log, Natural Log, Min Max )
Data Cleaning in Python
Visualization on Matplotlib
Visualization on Seaborn
Exploratory Data Analysis of Titanic dataset
Feature Engineering
Techniques of Filling Missing values in EDA
Correlation Matrix
Hands-on Assignment of EDA

Week 3

Interactive Discussions on Last Weeks Assignments
What is Probability.
Conditional Probability (Disjoint Events + General Addition Rule).
Dependence vs Independence
Probability Trees & Bayesian Inference with their examples.
Machine Learning Basics
Machine Learning Playground
Supervised Learning.
Train Test Splitting
Overfitting vs Underfitting
Cross Validation using K-folds
Linear Regression
Gradient Descent, Ordinary Least Squares
Project-1 Assigned to Students

Week 4

Interactive Discussions on Project
Multivariate Regression
Residual Plots, R square, Adjusted R Square
Polynomial Regression
Model Complexity, Model Selection
Lasso Regularization
Ridge Regularization
Logistic Regression.
Confusion Matrix.
True Positive, True Negative, False Positive , False Negative.
Precision, Accuracy, Recall, F Measure.
Project-2 Assigned to Students

Week 5

Interactive Discussions on Project
Decision Trees.
Information Gain, Gini Index, Chi Square
Random Forest.
Grid Search CV of Random Forest Hyper-parameters
What is Boosting
What is Bagging
AdaBoost on Python
Multi-Classification and Analyzing its Confusion Matrix
Unsupervised Learning
K-Means algorithm
Elbow Analysis, Internal Indexes, Silhouette Score
Project-3 Assigned to Students

Week 6

Interactive Discussions on Project
External Indexes, Adjusted Rand Index
Cluster Profiling using Radar Chart
Feature Scaling
DBSCAN Algorithm
Cluster Validation using DBCV
Hierarchical clustering
Average vs Complete vs Ward linkage
Dendrogram Creation and Reading clusters
External Indexes, Adjusted Rand Index
Hierarchical clustering Use Cases
Association Rules
Apriori Algorithm
Support, Confidence, Lift, Leverage, Conviction

Week 7

Interactive Discussions on Project
Visualizing Association Rules
Network Graph Theory
Social Network Analysis by Network Graph
Dimensionality Reduction Concept
Principal Component Analysis (PCA)
Principal Vectors/Components
Composite Features
Maximal Variance
Info Loss and Principal Component Analysis
Image Classification using PCA
Model Deployment Basics
Flask App Introduction
Model Deployment on Flask App

Week 8

Data Science Test
Project & Presentation
Self learning Path Guidance



‘Payment in Installments’ available after confirming enrollment. Online Banking details shall be shared via our representatives after you reserve your seat

  • Individual Pricing
    • PKR 30,000 Per Person
    • Total Charges for the Training
    • Book a Seat
  • Group of 2
  • Group of 3
  • Group of 4 or more
    • PKR 25,500 Per Person
    • 15% Off for Group of 4 or more
    • Book a Seat

Reserve your Seat

You can reserve your seat  by filling the form below

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    Frequently Asked Questions

    Who should attend the course?

    Graduate or Masters Students with Statistics, CS or Mathematics background who want to start their career in the Data Science domain

    People who are working in the BI domain 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 (Weekends)
    Timings: 11AM – 4PM

    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 Data Science 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.

    How will this training ensure hands-on practice?

    For executing the practical’s included in the Data Science 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.

    What is the location for Onsite mode in Islamabad?

    Launchpad7, First Floor, Al Rehman Chamber, 79 East Fazal-ul-haq road, Blue Area, Islamabad.