atomcamp

Data Science Bootcamp

Become a Data Scientist in 6 Months!

  • Online
  • 6 Months
  • 1st March

About this Program

This is a 6-month Bootcamp to enable participants to learn relevant data skills and launch their careers in the field. The program is meant for those who are aiming to switch into a data science career as well as those who want to incorporate data science training into their current jobs/careers to remain competitive. 
The defining characteristic of this program is that it goes beyond training and imparting key skills. It will also enable you to communicate effectively, think critically, and problem solve. Exclusive mentorship and career guidance will be provided to participants. 
The Graduates of the Bootcamp will not have just learnt tools and methods but will have become data scientists – competent, confident, and ready to work. We look forward to working with you over the next 6 months as we master data science and the ways it is revolutionizing the future of work.
 

Key Features

Customizable Learning Paths

Expert Instructors

Hands-on Experiential Learning

Help and Support from Teaching Assistants

Networking and Social Connections

LinkedIn Coaching & Resume Building

Interview Preparation

Industry Ready Portfolio

1-on-1 Coaching and Mentorship

Job Search Guidance and Support

CURRICULUM

MONTH 1

Learning Outcomes

1. Become familiar with and demonstrate clarity in understanding of basic data and statistical concepts.
2. Enhance understanding of mathematical foundations and operations for data science.
3. Become proficient in data cleaning, data processing, and data management with Microsoft Excel. 
4. Be able to carry out basic data analysis in Excel and work with Pivot Tables.

Data Literacy
• Data Types
• Data Life Cycle

Mathematical Foundations
• Fundamental Statistics
• Probability
• Descriptive Statistics

Excel
• Data Cleaning
• Data Preparation

Excel Advanced
• Pivot Tables
• Basic Analysis

MONTH 2

Learning Outcomes

  1. Become familiar with Python, and use Python interactively and with a script.
  2. Learn to store, access, and manipulate data in lists.
  3. Use functions, packages, and methods to leverage the code that Python. developers have written.
  4. Learn to work with powerful tools in the NumPy and other arrays and get started with data exploration.

Python Basics for Data Science

Python Lists

Functions and Packages

NumPy

Participants will be given the option to choose between 2 streams:

Data Analytics

MONTH 3

Learning Outcomes

  1.  Become familiar with the use of Tableau for data visualization, and be able to work with data extracts and time series
  2.  Be able to use aggregations and granularity.
  3. Be able to create charts and interactive dashboards to present data.
  4. Create table calculations, tree map charts, and storylines

Tableau Basics, Time Series, Aggregations, and Filters

Maps, Scatterplots, and Dashboards

Table Calculations, Calculated Fields, and Storytelling

Advanced Dashboards, Publishing Dashboards Online

MONTH 4

Learning Outcomes

  1. Identify a subset of data from a column or set of columns and write an SQL query to limit to those results.
  2. Use SQL commands to filter, sort, and summarize data.
  3. Create an analysis table from multiple queries using the UNION operator.
  4. Manipulate strings, dates, and numeric data using functions to integrate data from different sources into fields with the correct format for analysis.

Selecting and Retrieving Data with SQL

Filtering, Sorting, and Calculating Data with SQL

Subqueries and Joins in SQL

Modifying and Analyzing Data with SQL

MONTH 5

Learning Outcomes

  1. Become familiar with introductory regression analysis.
  2. Learn spatial analysis with GIS.

Machine Learning for Data Analytics

Machine Learning for Data Analytics

Introduction to GIS and Spatial Data Analysis

Data Queries and Map Making/Cartography

Machine Learning Engineer

MONTH 3

Learning Outcomes

  1. Get acquainted with the meaning of Machine Learning along with relevant case studies
  2. Delve into Feature Engineering
  3. Get familiar with Statistical Models and Tree Based Models
  4. Create your first professional portfolio project

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

Statistical Models, Tree Based Models
• Bayes’ Theorem
• Naive Bayes
• Decision Tree

Portfolio Building
• Portfolio Project 1

MONTH 4

Learning Outcomes

  1. Learn how to deal with Regression Models
  2. Learn about Neural Networks
  3. Create your second professional portfolio project
  4. Get an insight into Unsupervised Learning

Regression Models
• Linear Regression
• Logistic Regression
• SVM

Neural Network:
• Perceptron
• Artificial Neural Networks
• Intro to Deep Neural Nets

Portfolio Building
• Portfolio Project 2

Unsupervised Learning
• K-Means Clustering
• Hierarchical clustering
• Principal Component Analysis

MONTH 5

Learning Outcomes

  1. Gain familiarity with major NLP concepts.
  2. Create your third professional portfolio project.
  3. Learn Computer Vision.
  4. Revise and Consolidate Concepts and Projects.

NLP
• Basic Text Processing
• NLTK & SpaCy
• Intro to Transformers

Portfolio Building
• Project Portfolio 3g

Computer Vision
• Basic Image Processing
• CNN and its advanced variants

Review
• Review Concepts & Projects

Career Counseling & Job Market Preparation

Combined

MONTH 6

Learning Outcomes

  1. Develop your personal brand and revamp your LinkedIn profile and resume.
  2. Learn about online and offline networking strategies and how to navigate networking events with confidence.
  3. Learn unconventional methods of job hunting.
  4. Learn effective interview strategies and how to answer behavioral as well as domain related questions.

LinkedIn and Resumes

Networking and Mock Networking Event

Job Hunting

Interview Workshops and Mock Interviews

Trainers

Meet our experienced data science instructors. Our curriculum is created and taught by Data Scientists with years of real-world industry experience.

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.

I'm the CFO!!!

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.

Usman Shahbaz

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.

I'm the CFO!!!

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.

Pricing Plans

Standard Monthly Plan

PKR 20,000 per month
  • Standard Monthly Program (opting for one stream)

Monthly Plan for atomcamp alumni & women

PKR 15,000 per month
  • Monthly Plan for atomcamp alumni & women

Lumpsum Payment (Advance Payment)

PKR 80,000
  • for 6 Months

Frequently Asked Questions

Are the live sessions recorded for viewing later?

Yes, all sessions are recorded and will be available for the participants to view at a later date.

Are there any discounts available?

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.

What background is needed to join this Bootcamp?

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.

Will a certificate be awarded for completion of this Bootcamp?

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.

How many hours per week is the required commitment for this Bootcamp?

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.

What kind of jobs can I apply to after attending the Bootcamp?

Based on your skillsets, here are some job roles that you can target. Further information will be provided during our classes and speaker series.
· Data Engineer
· Machine Learning Engineer
· Data Analyst
· Data Scientist
· Product Analyst/Business Analyst