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Master Data Science!

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

30th Nov, 2019

Duration

8 Weeks (Saturdays)

Timings

10:00AM to 06:00PM

Address

FAST – NUCES Phase 1 Hayatabad, Peshawar

Charges

25,000 PKR

Remaining

Limited Seats Available!

Meet the Instructor!

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

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Mr. Albab A. Khan

Data Scientist, Machine Learning Engineer

Mr. Albab has been working in Data Science and Machine Learning for Telecom Sector, Government Organizations and Research Labs. He is the author of various International Conference Publications as well as Journal Publications in Data Science, Machine Learning and Biomedical Image Processing. His key expertise are in domains of Machine Learning, Data Mining, Data Driven Marketing and Data Prediction.

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Mr. Shoaib Khan

Data Scientist, Machine Learning Engineer

Mr. Shoaib Khan brings 7+ years of experience of BI consultancy, data modeling & visualization and currently working with telecom sector as Applied Analytics Specialist and BI Consultant and Trainer with Dice Analytics. He is certified in Teradata, Tableau, Power BI, MicroStrategy, AI and Python Programing. His key expertise comprises of end-to-end development of churn and sales prediction models, Algorithm Development and Advance Analytics.

 

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
Hands-On Assignments of Python

Week 2

Interactive Discussions on Last Weeks Assignments
Hands-On Data Wrangling on Python
Data Cleaning in Python
String operations in Data Wrangling
Object Types Convertion in Data Wrangling
Data Aggregation using Group By, Pivot and Melt
Dealing with Multi-indexing in Data Wrangling
iloc vs loc for Subsetting Dataframe
Row vs Column Concatenation
Multi-Indexing and Index Slicing
Iteration through Dataframe
Types of Variables
Data Visualizations (Scatter plot, Histogram, Bar plots, Line plots, Heat maps)
Hands-on Assignments of Data Wrangling

Week 3

Interactive Discussions on Last Weeks Assignments
Box plot
Skewness, Modality
Data Centricity (Mean, Modes, Median, STD, Variance, Interquantile Range).
Data Transformation (Log, Natural Log, Min Max )
Outliers Detection
Outliers Detection 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 Assignments of EDA

Week 4

Interactive Discussions on Last Weeks Assignments
What is Probability.
Conditional Probability (Disjoint Events + General Addition Rule).
Disjoint vs. Independent Events.
PMF vs PDF vs CDF
Probability Trees & Bayesian Inference with their examples.
Unsupervised Learning
Clustering
Kmeans,DBSCAN
Silhoute Indexes & Clustering Quality
Association Rules
Apriori Algorithm
Support, Confidence, Lift, Leverage, Conviction
Hands-On Assignments of Clustering & Assiociation

Week 5

Interactive Discussions on Last Weeks Assignments
Network Graph Theory
Social Network Analysis by Network Graph
Visualizing Association Rules
Supervised Learning.
Linear Regression.
Python Square, Suquare Sum of Regression, Least Square,
Multivariate Regression.
Residual Plots
R square, Adjusted R Square, Graduen
Cross Validation using K-folds
Overfitting vs Underfitting
Guidelines of Hands-On Project
ADS Preparation of Project started by Students

Week 6

Project ADS Discussions
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
Classification on Project ADS by Students

Week 7

Project ADS Discussions
Whats is Boosting
GBM vs XGBoost
XGBoost on Python
Grid Search CV of XGBoost Hyper-parameters
Multi-Classification and Analyzing its Confuson Matrix
Classification on Project ADS by Students
Time Series Analysis
Time Series Trend Correlationand Seasonality
Resampling techniques for Time Series
Time Series Forecasting Techniques
Naïve, 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 25,000 per person
Group of Two
7% Discount for a group of two people
Rs 23,250 per person
Group of Three
10% Discount for a group of Three people
Rs 22,500 per person
Group of Four
15% Discount for a group of four people
Rs 21,250 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: Saturdays Only

Timings: 10:00 AM to – 18:00 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

 

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