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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

Schedule

Starting

To be announced soon!

Duration

8 weeks (Mon – Fri)

Timings

07:30PM to 09:30PM

Remaining

.

Instructors!

Meet the instructors of Data Science Course

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Data Science | Big Data | CVMA | Machine Learning | Trainer
Ali Raza Anjum

Mr. Ali Abbas is a Gold Medalist from NUST. He has been working in the domain of Data warehousing, Data Science, Big Data and Customer Value Management since last 7 years!

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Data Scientist | Data Science Instructor | Big Data Engineer
Ali Abbas

Mr. Ali Abbas is a data enthusiast able to efficiently leverage advanced knowledge statistical inference and machine learning to furnish insights and enable data driven business processes!

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Big Data | Data Scientist | Telecom Analyst | Trainer
Muhammad Zubair

Mr. Muhammad Zubair is working in Telenor from last 3 years. He is expert in the field of Python, Data Science & Big Data and he is the 1st Runner-Up in Data Science Hackathon!

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Data Enthusiast | Machine Learning | Technical Assistant
Abdullah Paracha

Mr. Abdullah Paracha is a Data Enthusiast and he is working in the field of Data Science and Machine Learning from last 1 years!

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 Dataframes in Pandas
Hands-On Assignments of Python

Week 1

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 2

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 3

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 4

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 5

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 6

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 7

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
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.

Reserve your Seat

You can reserve your seat  by filling the form below

 

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