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Be a Data Scientist!

An Interactive Live Training

Presenting an Applied Training on Data Science & Machine Learning.

In this live 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 forĀ  certifications of Data Camp and Cognitive AI.

View Course Outline Reserve your Seat

Our Approach for Live Classes

After establishing a reputable Physical Training Model, based on our stellar records and customer earned trust we gradually progressed to establish the same reputation in the Live Training Model. We are committed to empower you by making our trainings accessible, interactive, and well curated specifically to your objective.

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Schedule

Starting

8th August 2020

Duration

8 weeks (Sat-Sun)

Timings

11:00 AM to 03:00 PM

Remaining

Limited Seats Available!

Meet the Instructors!

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

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Mr. Muhammad Zubair

Big Data | Data Scientist | Telecom Analyst | Trainer

Muhammad Zubair is NUST graduate and has been associated with industry since last 8 years. He is currently working as Expert Data Scientist with Jazz and also Data Science & Machine Learning trainer/consultant at Dice Analytics. His key expertise are in domains of Python, Data Science, Machine Learning, Telecom Analytics & People Analytics

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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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Ms. Fatima Bukhari

Data Scientist | Data Analyst

Ms. Fatima Bukhari brings in 5+ years of diverse experience in Data Science domain. She is currently associated as a Data Scientist & Machine Learning Engineer with Red Buffer. She is also working on different projects with World Bank as a Data Scientist and serves as a DS lecturer at Szabist. Ms. Fatima is a NUST graduate and hold expertise in Python, Data Science, Machine Learning, Data

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

Features

Instructor-led sessions

Real-life case studies

Assessments

Certification

Tools

Pricing

Following is price for this extensive training on Data Science & Machine Learning

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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
Timings: 11 am to 6 pm
Days: Sat-Sun

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.

How much internet speed is required?

3GB

How will I perform hands-on tools?

For executing the practicals 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.

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 seatĀ  by filling the form below

 

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