SHAHRUKH ALAM KHAN

https://www.linkedin.com/in/shahrukh-a-2086b5157 https://public.tableau.com/profile/shahrukh.alam#! https://github.com/shahrukh-ak EDUCATION:

SCHOOL: Purdue University (CGPA: 3.6/4) Hammond, IN, USA Education: Master’s in Electrical & Computer Engineering January 2019 – May 2021 Coursework: Data Mining and ML, Machine Learning, Big Data, Statistical Computing, Computer Network Security, Software Design, Practical Deep Learning, Data Visualization Techniques

SCHOOL: NED University of Engineering and Technology (1st Division) Karachi, Pakistan Education: Bachelors in Computer and Electronics Engineering Nov 2013- Nov 2017 SKILLS:

PROGRAMMING LANGUAGES: Python, R, SQL, C/C++ OS: Linux, Windows, Mac Software and Tools: MS Excel, Tableau, R-Studio, Jupyter Notebook, GitHub, Sublime Text 3, Alteryx, Data bricks, MySQL workbench Certifications:

IBM BADGES: Big data Foundation, Hadoop Foundation, Hadoop Administration, Hadoop Programming, Spark, Kafka, Hive, Pig, Flume and Sqoop. Coursera: Google Cloud Platform Big Data and Machine Learning Fundamentals, Neural Network and Deep Learning, Configuration Management with Cloud. Work Experience:

Silicon Tech Solutions (San Ramon, CA, USA)- Data Science Intern Jun 2020- Aug 2020

• Created, designed, and operated with SQL databases and adopted professionally tested SQL best practices using MySQL.

• Imported, managed, and cleaned data from a wide variety of data sources and use regular expressions for data management.

• Gained theoretical insights about relational databases while working with sophisticated real-life databases to develop business intuition while solving tasks with big data and create databases from the scratch. Used Python, SQL, and Tableau together through Software integration.

• Applied data preprocessing techniques such as removing null/duplicate values, data cleaning, one-hot encoding, normalizing, etc. Performed linear and logistic regressions in Python and carried out cluster and factor analysis.

• Practiced insert, update, and delete records from databases and worked with constraints and relating data tables as daily tasks.

• Answered specific business questions by using SQL’s aggregate functions and handled complex SQL joins. Worked on advanced topics in programming like SQL’s triggers, sequences, local and global variables, indexes, and more. BeulahWorks, LLC (Valparaiso, IN, USA)- Data Analyst Jan 2020- May 2020

• Applied machine learning techniques such as logistic regression, linear regression, clustering, etc.

• Connected Python and SQL to transfer data from Jupyter Notebook to Workbench and visualize data in Tableau.

• Merged coding skills and business acumen to solve complex analytical problems as a proficient SQL user by writing flawless and efficient queries.

• Used ETL tools like Alteryx to extract, transform, and load data.

• Used SQL queries to extract meaningful insights from the data and then used the information to create interactive dashboards and stories in Tableau for a non-technical audience and higher management. Develop a business intuition while coding and solving tasks with big data.

• Used state-of-the-art Deep Learning frameworks such as Google’s Tensor Flow. Interpreted the exercise outputs in Jupyter Notebook and Tableau. Purdue University (Hammond, IN, USA)- Graduate Research Assistant (Big Data Research Assistant) Jan 2019- Dec 2019

● Worked on the research topic under the supervision of a senior professor Dr. Sandeep Inti from PNW that how big data and data science can be used in multiple areas of the Construction industry and how it benefits the industry.

● Took several considerations of the most efficient and productive data science use cases in the construction industry such as Predictive Analysis, Design issue prediction, warranty analysis, analyzing construction project risk, accurate project planning, and budgeting, etc.

● Run statistical analysis tests and performed hypothesis testing, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models, and sci-kit-learn, Deep learning with Tensor Flow.

● Applied deep neural networks DNN by tuning the number of layers and tried convolution neural network CNN to identify the images of bridges, houses, etc. Green Ways Energy (Karachi, SD, PK)- Data Analyst Nov 2017- Nov 2018

• Run statistical and Machine Learning (ML) models to improve accuracy for estimating and predicting power production.

• Collected weather data from the machines and predicted power production per day.

• Improved Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross-validation, testing, and how hyperparameters could improve performance. Checked the probability of the failure of inverters.

• Checked the significance of variables using R that affect the solar panels energy production such as location, irradiance, height, panel quality, weather, etc.

• Run different correlation tests to check the correlation between variables such as Spearman, Pearson, and Kendall’s Tau tests, etc. K-Electric (Karachi, SD, PK)- Data Science Intern Dec 2015- Aug 2016

• Processed raw data into a suitable format for a range of data mining algorithms. Analyzed data through statistical and graphical summarization.

• Utilized statistical software R to perform different types of computational tasks (prediction, classification, clustering, etc.)

• Used machine learning models to predict load values in future. Checked significance of variables that can be useful for efficient transmission of electricity.

• Created charts like pie charts, bar plots, stacked area charts, dashboards, and others on Excel, R, and Tableau to analyze consumption and load calculations. Selected Projects: (Much more on GitHub)

• Analyzing Diamond Price based on 4C’s: The model predicts the price of diamonds. Did several T-test to conclude which factor affecting the price the most.

• Classification of Scenery Images Using CNN: Applied convolution neural network (CNN) on colored image dataset to classify scenery images that consist of 4 classes i.e. buildings, mountains, rivers, and forests using Python achieving 76% accuracy in results.

• Analysis of GDP of USA Products: Fitted linear regression model on the GDP of US products and made scatter plot for data visualization. Determined coefficient of determination and improved the model accuracy from 96.7% to 99.5% by analyzing the residual plots and Box-Cox transformation. Leadership and Activities:

Teaching Assistant (Grader)- Purdue University Northwest Aug 2019- Dec 2019 Managed 25+ undergraduate students, conducted lectures, tested codes, lead students on projects related to programming & engineering. Being a teaching assistant at Purdue boosted my confidence, enriched my abilities to interact, communicate with people and build relationships with them. Hammond, IN, USA

+1-219-***-****

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