Data Science
Training lead by Industry Professionals!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
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Schedule
Starting
Coming Soon!
Duration
8 weeks (Saturdays)
Timings
10:00AM to 06:00PM
Remaining
Limited Seats Left!
Instructors!
Meet the instructors of Data Science Course
Data Science | Big Data | CVMA | Machine Learning | Trainer
Shoaib Khan Lodhi
Mr. Shoaib Khan brings 7+ years of experience and currently working with telecom sector as Applied Analytics Specialist and BI and Data Science Consultant and Trainer with Dice Analytics. He is certified Python Programmer. His key expertise comprises of developing algorithms based on deep-dive statistical analysis and predictive data modelling that are used to deepen relationships, strengthen longevity and personalise interactions with customer along with good capabilities.
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! He has been working in the field of Deep Learning, Machine Learning & Data Science in telecom sector. He has in-depth knowledge about Data Engineering, Machine Learning, Deep Learning, TensorFlow & other Data Science and AI tools & technologies.
Big Data | Data Scientist | Telecom Analyst | Trainer
Muhammad Zubair
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.
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. He has done Facial Recognition Based Attendance System, Machine Learning and Data Analytics based projects.
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
|
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
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