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Data Science Training

Lead by Industry Professionals!

About Data Science Training

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


By the end of the course, you will have enough knowledge and hands-on expertise in Python and Knime to use and apply them in the real world around you. Also, you will be able to get prepared for at least 5 certifications of Data Camp and Cognitive AI Certification.

View Course Outline Reserve your Seat



7th Feb, 2019


8 weeks (Mon – Fri)


07:30PM to 09:30PM


10 Seats Left


Meet the Instructors

Meet the trainers of this course who are Data Scientists & are highly experienced!


Ali Raza Anjum

Data Science, Big Data, CVMA, Machine Learning, Trainer

He is also a Gold Medalist from NUST. He has been working in the domain of Datawarehousing, Data Science, Big Data and Customer Value Management since last 7 years...


Ali Abbas

Data Scientist | Data Science Instructor | Big Data Engineer

A data enthusiast able to efficiently leverage advanced knowledge statistical inference and machine learning to furnish insights and enable data driven business processes.

Course Outline

Week 0

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 Data frames in Pandas

Statistical Flow

Types of Variables

Hands-On Assignments of Python


Week 1

Interactive Discussions on Last Week’s Assignments

Population vs Sample

Types of Study Design

Types of Sampling

Random Assignment

Correlation vs Causation

Blocking vs Confounding Variables

Hands-On Data Wrangling on Python

Data Cleaning in Python

String operations in Data Wrangling

Object Types Conversion in Data Wrangling

Data Aggregation using Group By, Pivot and Melt

Dealing with Multi-indexing in Data Wrangling

iloc vs loc for Subsetting Data frame

Hands-on Assignments of Data Wrangling

Week 2

Interactive Discussions on Last Week’s Assignments

What is Probability?

Conditional Probability (Disjoint Events + General Addition Rule).

Disjoint vs. Independent Events


Probability Trees & Bayesian Inference with their examples

Unsupervised Learning


Kmeans, Kmodes, Kmedians

Silhoute Indexes & Clustering Quality

Hierarchical clustering

Association Rules

Apriori Algorithm

Support, Confidence, Lift, Leverage, Conviction

Hands-On Assignments of Clustering & Associa

Week 3

Interactive Discussions on Last Week’s Assignments
Network Graph Theory
Social Network Analysis by Network Graph
Visualizing Association Rules
Guidelines of Hands-On Project
ADS Preparation of Project started by Students

Week 4

Interactive Discussions on Last Week’s Assignments

Network Graph Theory

Social Network Analysis by Network Graph

Visualizing Association Rules

Statistical Inference

Probability Distributions (Normal, Binomial, Poison)

Variability & Central Limiting Theorem

Confidence Interval & Confidence Level

Hypothesis Testing & Null Hypothesis

Expected Values

T Statistics

P Statistics

Guidelines of Hands-On Project

ADS Preparation of Project started by Students

Week 5

Project ADS Discussions

Supervised Learning.

Linear Regression.

Python Square, Square Sum of Regression, Least Square

Multivariate Regression.

Residual Plots

Cross-Validation using K-folds

Overfitting vs Under fitting


Logistic Regression

Confusion Matrix

True Positive, True Negative, False Positive, False Negative

Precision, Accuracy, Recall, F Measure

Classification on Project ADS by Students

Week 6

Project ADS Discussions

Deciding Classification Threshold by Precision Recall Curve

K Nearest Neighbours

Decision Trees

Information Gain, Gini Index, Gain Ratio

ID3, Cart, C4.5

Random Forest

Grid Search CV of Random Forest Hyper-parameters

What is Boosting

GBM vs XGBoost

XGBoost on Python

Grid Search CV of XGBoost Hyper-parameters

Classification on Project ADS by Students


Week 7

Project & Presentation

Self-learning Path Guidance



Following is price for this extensive training on Data Science

Price for an Individual
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


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

Class Days: Monday – Friday

Timings: 07:30 PM to – 09:30 PM

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.

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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