Upcoming Events

No Events on The List at This Time

 

Data Science

Training lead by Industry Professionals!

Presenting Data Science & Machine Learning Training!

In this course 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 the course, 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 Reserve your Seat

Schedule

Starting

6th November’19

Duration

8 weeks (Mon-Fri)

Timings

07:00PM to 09:00PM

Remaining

Limited Seats Left!

Meet the Instructors!

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

qodef-fullwidth-slider

Mr. Muhammad Zubair

Big Data | Data Scientist | Telecom Analyst | Trainer

Mr. Muhammad Zubair is a NUST graduate and has been associated with industry since last 7 years. He is currently working as Big Data and Data Science specialist with Telenor and also a Data Science & Machine Learning training at Dice Analytics. His key expertise are in domains of Machine Learning, Data Mining, Python & Data Science.

qodef-fullwidth-slider

Mr. Omair Arshad

Data Scientist at Telenor

Omair Arshad has been associated with the field of BI, Data Analytics and Data Science since last 6 years. Currently working as Data Science specialist in Telenor involved in projects like Churn Prediction, Recommendation Engine and Customer Segmentation model for different Telenor Digital Products.

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).
PMF vs PDF vs CDF
Probability Trees & Bayesian Inference with their examples.
Unsupervised Learning
Clustering
K-Means algorithm
Elbow Analysis, Internal Indexes, Silhoute Score
Cluster Profiling using Radar Chart
Feature Scaling
DBSCAN Algorithm
Cluster Validation using DBCV
Project-1 Assigned to Students

Week 4

Interactive Discussions on Project
Hierarchical clustering
Average vs Complete vs Ward linkage
Dendogram Creation and Reading clusters
External Indexes, Adjusted Rand Index
Hierarchical clustering Use Cases
Association Rules
Apriori Algorithm
Support, Confidence, Lift, Leverage, Conviction
Visualizing Association Rules
Network Graph Theory
Social Network Analysis by Network Graph
Hands-On Assignments of Clustering & Assiociation
Project-2 Assigned to Students

Week 5

Interactive Discussions on Project
Supervised Learning.
Train Test Splitting
Overfitting vs Underfitting
Cross Validation using K-folds
Linear Regression
Gradient Descent
Multivariate Regression
Residual Plots
R square, Adjusted R Square
Polynomial Regression
Lasso Regularization
Ridge Regularization
Project-3 Assigned to Students

Week 6

Interactive Discussions on Project
Classification
Logistic Regression.
Confusion Matrix.
True Positive, True Negative, False Positive , False Negative.
Percision, Accuracy, Recall, F Measure.
ROC Curve, AUC, TPR, FPR
Deciding Classification Threshold by Precision Recall Curve
K Nearest Neighbors
Decision Trees.
Information Gain, Gini Index, Gain Ratio.
Random Forest.
Grid Search CV of Random Forest Hyper-parameters
Project-4 Assigned to Students

Week 7

Interactive Discussions on Project
Whats is Boosting
Whats is Bagging
Adaboost on Python
XGBoost on Python
Multi-Classification and Analyzing its Confuson Matrix
Time Series Analysis
Time Series Trend Correlationand Seasonality
Resampling techniques for Time Series
Time Series Forecasting Techniques
Naive, Simple Average, Moving Average
Simple Exponential Smoothing, Holt Linear, Holt Winter
ARIMA, SARIMA

Week 8

Data Science Test
Project & Presentation
Self learning Path Guidance

Tools

Pricing

Following is price for this extensive training on Data Science

Individual
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

 

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: 07:00 PM to – 09:00 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.

Reserve your Seat

You can reserve your seat  by filling the form below

 

Are you a: 
StudentWorking Professional

 

 

 





X