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CURRICULUM
Data Literacy
• Data Types
• Data Life Cycle
Excel
• Data Cleaning
• Data Preparation
Excel Advanced
• Pivot Tables
• Basic Analysis
Data Analytics and Visualization on Tableau
Selecting and Retrieving Data with SQL
• Statistical Foundation
Subqueries and Joins in SQL
• Statistical Foundation
Filtering, Sorting, and Calculating Data with SQL
• Mathematical Foundation
Modifying and Analyzing Data with SQL
• Mathematical Foundation
Introduction to Python
• Setting up a development environment
• Basic Python syntax and data types
Basic Operators and Expressions
• Basic operators and expressions
• Control structures: if else case statements
Loops
• For and while loops
• Break and continue statements
Functions
• Defining and calling functions
Working with Data
•Lists and tuples
•Dictionaries and sets
•File handling
Modules: Numpy & Matplotlib
• Modules and the import statement
• Plotting
Exception Handling
• Handling exceptions
• Raising exceptions
• Custom exceptions
Review and Practice
Introduction to ML
• What is ML? (Demystifying the buzz words such as ML, AI, DS and DL)
• Building Blocks for ML & Real World Examples
• Exploratory Data Analysis
Feature Engineering
• Data Scaling
• Identifying important features
• Creating new features
Bagging and Boosting
• Bayes’ Theorem
• Naive Bayes
• Decision Tree
GIS & Remote Sensing
Regression Models
• Linear Regression
• Logistic Regression
• SVM
Neural Network:
• Perceptron
• Artificial Neural Networks
• Intro to Deep Neural Nets
Unsupervised Learning
• K-Means Clustering
• Hierarchical clustering
• Principal Component Analysis
AutoML
• Auto train the model, fine-tune it,
and evaluate it on a given dataset
• Learn libraries like PyCaret and H2O
NLP
• Basic Text Processing
• NLTK & SpaCy
• Intro to Transformers
Computer Vision
• Basic Image Processing
• CNN and its advanced variants
Job Market Preparation
• Resume Building for Data Scientists
• Interview Preparation
• Portfolio Building & Demonstration
Job Market Preparation
• Resume Building for Data Scientists
• Interview Preparation
• Portfolio Building & Demonstration
Key Features
Career Services Throughout the Bootcamp
Testimonials
Hear what our atoms say
Testimonials
Trainers
Meet our experienced data science instructors. Our curriculum is created and taught by Data Scientists with years of real-world industry experience.
Aymen is a Data Scientist with 4+ years of professional industry and training experience with expertise in analytics, modern technologies, consulting, and problem solving. She has done her BS Electrical and electronics Engineering from Bilkent University in Turkey, has a Masters in Data Science from IBA, and currently teaches Machine Learning at atomcamp.
Hussain has delivered data science solutions to global clients including Barclays, OCBC, KPMG, Royal Fidelity, and multiple Government as well as Intelligence organizations. He has taught Machine Learning and Deep Learning at SZABIST.
Sidra Cheema professionally belongs to the technology industry, having worked for over 7+ years in consulting and solutioning which included data analysis and data science.
Usman’s core specialties include Advanced Analytics, Machine & Deep Learning, and Optimization. He has worked in various industries including financial, retail, FMCG, consumer electronics and telecommunications in Australia. Currently, he is leading the data science team in nbn Australia , a leading broadband company in the country. He is also pursuing his PhD in Machine Learning.
Minahil Raza’s work is focused on data analytics and energy and she has worked with organizations such as Asian Development Bank, the World Bank, London School of Economics, CERP and International Growth Centre.
Yahya Bajwa is an experienced professional with a demonstrated history of working in the management consulting industry. He holds a Master of Public Policy (MPP) degree and a Graduate Certificate in Data Science from the Gerald R. Ford School of Public Policy, University of Michigan.
Machine Learning and Computer Vision Leader, specialized in deep learning and active learning with domain expertise in Climate Tech, Facial Identity, Medical Imaging and self-driving vehicles.
Experienced in delivering computer vision products and APIs for use cases including image recognition and classification, object detection and segmentation in 2D and 3D images/videos – application ranging from climate, satellite, medical, facial identity, self-driving vehicles, retail and fashion.
Earn a Verified Certificate of Completion
Earn a data science certificate, verifying your skills. Step into the market with a proven and trusted skillset.
Frequently Asked Questions
Yes, all sessions are recorded and will be available for the participants to view at a later date.
Yes, if you are from a disadvantaged background, have taken a course with atomcamp previously, or are a woman, you may be eligible for a 25% scholarship/financial aid. Please contact team@atomcamp.com for further details relevant to your profile.
While it is understandable that people from technical backgrounds will find it easier to grasp some of the concepts, this program is designed to be accessible to individuals from all sorts of backgrounds. The only requirement is that you be willing to work hard and are motivated. This Bootcamp is for anyone who is curious about data science and willing to explore, segment, analyze, and understand their data in order to make better data-driven decisions. If you need further support in making a decision with regards to joining the Bootcamp, please reach out to team@atomcamp.com or zumerzia10@gmail.com to set up a meeting.
Yes, you will be awarded a certificate if you have 80% attendance and have completed all assignments and projects to the satisfaction of the instructors.
The total class hours are 6 per week. Aside from that, you will be assigned 6 hours’ worth of homework. Only commit to the Bootcamp if you are willing to put in this much effort.
Based on your skillsets, here are some job roles that you can target. Further information will be provided during our classes and speaker series.<br>
· Data Engineer<br>
· Machine Learning Engineer<br>
· Data Analyst<br>
· Data Scientist<br>
· Product Analyst/Business Analyst