Data Science Specialization

R programming is the 'golden child' of data science. If you want to become a top-tier data scientist, then learning R is crucial. With an estimated 2+ million users, R is a powerful scripting language used by data scientists all over the world. Whether you’re experienced or brand new to programming, this course is a stepping stone for you to learn data science through R programming.

About This Course

In this comprehensive course, you'll master the foundations of R programming language. You'll learn basic data types, operations, control structures, loop functions, data splits and additional resources related to data science. Upon the successful completion of this source you'll be able to get, clean, and explore data by conducting reproducible research through data programming in R. This course is 100% online. Notes are provided and you're also given access to group learning and Q/A community.

What you will learn

Learning data science through R programming is easy. A solid foundation guarantees you an entry into more complex data science learning pathways. This introductory course sets you on a rewarding career of data science by teaching you some core data concepts such as:

Course Delivery Mode
The training will be delivered online via our alnafi.com portal, which is designed to cater 50 Million Nafi members. The portal has all the key features like:
- Watching and tracking videos
- Progress monitoring,
- Attempting quizzes,
- Submitting assignments,
- Asking questions from the teachers and mentors

Course Title	Duration	Tools	Learning Outcomes
Introduction of Data Science	1 – 1.5 month	-	Student will able to understand what problems DS can solve; basic structure of data science life cycle and tools that needs to be learned.
R Programming	1 – 1.5 month	R, RStudio, Git, Github	Student will able to write code in R programming language and can start data science projects.
Data Collection and Data Wrangling	1 – 1.5 month	Dplyr, Tidyr, Tidyverse, R, RStudio, Git, Github	Student will able to write code in R programming language and can start data collection, cleansing and data transformation.
Exploratory Data Analysis	1 – 1.5 month	base graphs, lattices, ggplot2, R, RStudio, Git, Github, Kaggle	Student will able to perform data analysis and start participating on Kaggle at basic level.
Machine Learning: Supervised Learning	1 month	Regression, Classifications, Model Evaluation Techniques, caret, forests, R, Studio, Git, Github, Kaggle	Student will able to create supervised learning models, evaluate them and start participating in Kaggle competitions at basic level.
Machine Learning: Unsupervised Learning	1 month	Clustering, Dimension Reduction,Model Validation,R, RStudio, Git, Github, Kaggle	Student will able to create unsupervised learning models, evaluate them and start participating in Kaggle competitions at basic level.
Capstone Project	1 month	R, RStudio, Git, Github, Kaggle	Student will able to process complete data science lifecycle based on supervised or unsupervised learning models, evaluate them and are ready to participate in Kaggle competitions.
Data Visualization	0.5 – 1 month	D3, Plotly, R, RStudio, Git, Github, Kaggle	Student will be equipped with knowledge of basic ingredients of data visualization and storytelling.

Course Title

Duration

Tools

Learning Outcomes

Introduction of Data Science

1 – 1.5 month

Student will able to understand what problems DS can solve; basic structure of data science life cycle and tools that needs to be learned.

R Programming

1 – 1.5 month

R, RStudio, Git, Github

Student will able to write code in R programming language and can start data science projects.

Data Collection and Data Wrangling

1 – 1.5 month

Dplyr, Tidyr, Tidyverse, R, RStudio, Git, Github

Student will able to write code in R programming language and can start data collection, cleansing and data transformation.

Exploratory Data Analysis

1 – 1.5 month

base graphs, lattices, ggplot2, R, RStudio, Git, Github, Kaggle

Student will able to perform data analysis and start participating on Kaggle at basic level.

Machine Learning: Supervised Learning

1 month

Regression, Classifications, Model Evaluation Techniques, caret, forests, R, Studio, Git, Github, Kaggle

Student will able to create supervised learning models, evaluate them and start participating in Kaggle competitions at basic level.

Machine Learning: Unsupervised Learning

1 month

Clustering, Dimension Reduction,Model Validation,R, RStudio, Git, Github, Kaggle

Student will able to create unsupervised learning models, evaluate them and start participating in Kaggle competitions at basic level.

Capstone Project

1 month

R, RStudio, Git, Github, Kaggle

Student will able to process complete data science lifecycle based on supervised or unsupervised learning models, evaluate them and are ready to participate in Kaggle competitions.

Data Visualization

0.5 – 1 month

D3, Plotly, R, RStudio, Git, Github, Kaggle

Student will be equipped with knowledge of basic ingredients of data visualization and storytelling.

Data Science Specialization

Sana Rasheed

Trainer

Dr. Zeeshan Ul Hassan Usmani

Founder & Trainer

Languages Available:
Urdu

No. of Courses :
08

Rating

Reviews :
137

Students Enrolled :
146

About This Course

What you will learn

What Skills Will You Gain?

Earning Potentials

Course Delivery Mode

Course Curriculum

Resident Pakistani

Resident Pakistani

Overseas Pakistanis

Other Nationalities

Western Union

Social proof: reviews

2-R Programming

MUHAMMAD JAVED IQBAL

3-R Programming

Rashida Parveen

4- Introduction to Data Sciences

Umer Shabbir

11- Introduction to Data Sciences

mubeen khan

12-Introduction to Data Sciences

Fida Muhammad

13-Introduction to Data Sciences

Ali Haidar

1-Machine Learning - Supervised Learning

MUHAMMAD SIRAJ HUSSAIN

2-Machine Learning - Supervised Learning

Arif UR Rehman

3-Machine Learning - Supervised Learning

Taha Ahmad Mirza

1-Data Collection & Data Wrangling

Ghulam Nabi

2-Data Collection & Data Wrangling in Urdu

Muhammad Akbar