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Be a Data Scientist!

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

17th Oct 20

Duration

8 Weeks (Saturdays)

Timings

09 AM to 06 PM

Address

Management House, 95 J/1, Johar Town, Lahore

Charges

30,000 PKR

Remaining

Registrations Open!

Meet the Instructors!

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

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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.

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Mr. Ahmed Niazi

Data Scientist | Data Analyst | Deep Learning Engineer

Mr. Ahmed Niazi is currently associated with Huawei Pakistan and has worked as a Data Scientist at MTBC. He holds his expertise in Deep Learning, Machine Learning & Python and has acquired multiple certifications in Artificial Intelligence & its applications Credit Scoring, Image Classification, NLP and Mathematics involved in Neural Networks.

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
Classification
Logistic Regression.
Confusion Matrix.
True Positive, True Negative, False Positive , False Negative.
Precision, Accuracy, Recall, F Measure.
ROC Curve, AUC, TPR, FPR
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
Clustering
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

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: Saturdays Only

Timings: 09am-6pm

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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