Data Analytics
Course
Use large volumes of data to find unseen patterns, derive meaningful insights and make business decisions.
Enrollment
Pre-register yourself for the next cohort
Program Duration
6-9 months
Learning Format
Online, self-paced course
What You Will Learn
- Acquire an in-depth understanding of the current trends and procedures employed by an operational level data analyst of in his/her daily works
- Comprehend important analytical techniques i.e. analysis, data cleaning and visualization along with imperative tools such as SQL, spreadsheets, Tableau and R programming
- Apply methodologies for data cleaning and data organization for analytical objectives. Perform analysis employing computations with the help of spreadsheets, R programming and SQL
- Showcase data statistics and findings in dashboards and visual presentations with the implementation of visualization platforms
Program Offerings
Practical Skills
You will learn practical skills in emerging careers that make you more valuable and appealing to employers
Projects
You will acquire the knowledge to create portfolio-worthy projects showcasing your skills
Mentorship
Experts working in the industry will mentor you in becoming successful professionals with leadership skills
Course Syllabus
Module 1
- Week 1
Introduction
Module 2
- Week 2
Analytical Thinking
Module 3
- Week 3
Data Life Cycle, Data Analysis Process, Data Analysis Toolbox
Module 4
- Weeks 4, 5 & 6
Course Work Discussion
Module 5
- Week 7
Databases, Organizing and Protecting Data
Module 6
- Week 8
Engaging the Data Community, Data Integrity
Module 7
- Week 9
Clean Data, Assignment Prep
Module 8
- Week 10
Cleaning Data with SQL, Verify and Report Cleaning Results
Module 9
- Week 11
Adding Data to Resume + Course Challenge
Module 10
- Week 12
Organizing Data to Begin Analysis, Formatting and Adjusting Data
Module 11
- Week 13
Aggregating Data for Analysis, Performing Data Calculations
Module 12
- Week 14
Visualizing Data, Creating Data Visualizations with Tableau
Module 13
- Week 15
Crafting Data Stories, Developing Presentations and Slideshows
Module 14
- Week 16
Programming and Data Analytics, Programming using R Studio
Module 15
- Week 17
Working with Data in R, More About Visualizations, Aesthetics and Annotations
Module 16
- Week 18
Documentation and Reports, Learn About Capstone Basics
Module 17
- Week 19
Building Your Portfolio + Using Your Portfolioo
Module 18
- Weeks 20 & 21
Putting Your Certificate to Work
Module 19
- Weeks 22 & 23
Introduction to Probability and Data with R
Module 20
- Weeks 24 & 25
Inferential Statistics
Module 21
- Weeks 26 & 27
Linear Regression and Modelling
Module 22
- Weeks 28 & 29
Bayesian Statistics
Module 23
- Weeks 30
Statistics with R Capstone (Final Project)
Module 24
- Weeks 31
Job - Interview Mocking and Training
Module 7
- Week 9
Clean Data, Assignment Prep
Module 8
- Week 10
Cleaning Data with SQL, Verify and Report Cleaning Results
Module 9
- Week 11
Adding Data to Resume + Course Challenge
Module 10
- Week 12
Organizing Data to Begin Analysis, Formatting and Adjusting Data
Module 11
- Week 13
Aggregating Data for Analysis, Performing Data Calculations
Module 12
- Week 14
Visualizing Data, Creating Data Visualizations with Tableau
Module 13
- Week 15
Crafting Data Stories, Developing Presentations and Slideshows
Module 14
- Week 16
Programming and Data Analytics, Programming using R Studio
Module 15
- Week 17
Working with Data in R, More About Visualizations, Aesthetics and Annotations
Module 16
- Week 18
Documentation and Reports, Learn About Capstone Basics
Module 17
- Week 19
Building Your Portfolio + Using Your Portfolioo
Module 18
- Weeks 20 & 21
Putting Your Certificate to Work
Module 19
- Weeks 22 & 23
Introduction to Probability and Data with R
Module 20
- Weeks 24 & 25
Inferential Statistics
Module 21
- Weeks 26 & 27
Linear Regression and Modelling
Module 22
- Weeks 28 & 29
Bayesian Statistics
Module 23
- Weeks 30
Statistics with R Capstone (Final Project)
Module 24
- Weeks 31
Job - Interview Mocking and Training
Module 7
- Week 9
Clean Data, Assignment Prep
Module 8
- Week 10
Cleaning Data with SQL, Verify and Report Cleaning Results
Module 9
- Week 11
Adding Data to Resume + Course Challenge
Module 10
- Week 12
Organizing Data to Begin Analysis, Formatting and Adjusting Data
Module 11
- Week 13
Aggregating Data for Analysis, Performing Data Calculations
Module 12
- Week 14
Visualizing Data, Creating Data Visualizations with Tableau
Module 13
- Week 15
Crafting Data Stories, Developing Presentations and Slideshows
Module 14
- Week 16
Programming and Data Analytics, Programming using R Studio
Module 15
- Week 17
Working with Data in R, More About Visualizations, Aesthetics and Annotations
Module 16
- Week 18
Documentation and Reports, Learn About Capstone Basics
Module 17
- Week 19
Building Your Portfolio + Using Your Portfolioo
Module 18
- Weeks 20 & 21
Putting Your Certificate to Work
Module 19
- Weeks 22 & 23
Introduction to Probability and Data with R
Module 20
- Weeks 24 & 25
Inferential Statistics
Module 21
- Weeks 26 & 27
Linear Regression and Modelling
Module 22
- Weeks 28 & 29
Bayesian Statistics
Module 23
- Weeks 30
Statistics with R Capstone (Final Project)
Module 24
- Weeks 31
Job - Interview Mocking and Training
Instructors
Umair Rafique
Umair Rafique is the Lead Data Scientist at Marketlytics, a leading Digital Analytics consultancy in Karachi. He has been leading Data Science projects at Marketlytics for over 3 years, during which he has worked for multiple clients from Fortune 100.
His expertise lies in Statistical Analysis, Modelling, Machine Learning and Recommender Systems. Apart from that, he has been training aspiring Data Scientists in Statistical Inference and Regression Modelling with KarachiAI for the last 3 years.
Muhammad Hamza
Hamza is presently working as a Lead Data Scientist at Motive. With over 5 years of experience in the Data Science Industry, with past roles of Data Scientist at Daraz and Machine Learning Engineer at Gaditek. He has designed and deployed different data driven solutions including recommendations and predictive churn models in past.
He has teaching experience of over 2 years where he has taught courses related to Data Analytics, Data Engineering and Data Science.
Frequently Asked Questions
Our LEC Agreement allows you to pay PKR0 upfront, and your tuition fee in fixed. You will pay back on monthly payments after you're hired into a qualifying position.
Yes, a professional course certificate will be awarded at the end of the course.
Alumni Success
Muhammad Tayyab
Cohort 2
Hired as a Data Science Intern at
Faran Ahmad
Cohort 2
Hired as a Salesforce Data Quality Analyst at
