Data visualization: Matplotlib, Seaborn, Plotly, Tableau, Power BI, QuickSight

Languages: Python, R, SQL, JavaScript, SAS

Databases: SQL Server, MySQL, PostgreSQL, Redshift Methodologies: SDLC, Agile, SMART, Waterfall, RUP, RAD, EPM Operating Systems

Core Competencies:

Windows, Linux

Logic and analysis, statistics and probability, pattern and trend identification, da mining and data QA, database design and management, risk managemen quantitative methods, data warehousing, experimental design &analysis

• 3 years of IT experience and technical proficiency as a Data Analyst in ETL, SQL coding, Data Modelling, and Data Warehousing involved with Business Requirements Analysis, development, testing, documentation, and reporting.

• Implementation of Software Development Life Cycle (SDLC) methodologies like Agile/SCRUM, Rational Unified Process (RUP), Waterfalling Data warehouses and Data marts in various industries, and Cross Industry Standard Process for Data Mining (CRISM-DM).

• Strong experience in Data Analysis, Profiling, Data Migration, Data Conversion, Data Quality, Data Integration and Metadata Management Services, Configuration Management, and SMART strategy.

• Knowledge of Python packages like NumPy, Pandas, Matplotlib, SciPy, and Scikit-Learn.

• Good understanding of Relational Database Design, Data Warehouse/OLTP to OLAP concepts, and methodologies with start schema in the dimension and fact table

• Proficiency in multiple databases like MySQL with analytics lifecycle as problem framing, sensemaking (cleaning, munging, combining, normalizing, reshaping, slicing, and dicing), analytic model development, result action, and analytics product lifecycle management.

• Extensive experience working with Business Intelligence data visualization tools specializing in Tableau Desktop and Tableau Server.

• Experience with Python to develop analytic models and solutions.

• Proficient in data visualization tools Tableau and Python to create visually powerful and actionable interactive reports and dashboards.

• Experience in various phases of the Software Development life cycle, analytics lifecycle, and decision lifecycle (Analysis, Requirements gathering, Designing) with expertise in documenting requirements, functional specifications, Test Plans, Source Target mappings, and SQL Joins.

• Excellent Tableau skills and expertise in building and publishing customized interactive reports and dashboards with customized parameters and user filters using Tableau (9.x/10.x).

• Experience creating technical metadata for the underlying physical data model of the Source systems and Data warehouse.

• Good experience with Source and version control systems like JIRA and Asana

• Strong analytical, problem-solving, and diagnostic skills, with the experience to drive business initiatives from conception to realization.

• The type of analytics used is descriptive, diagnostic, forensic, predictive, prescriptive, and cognitive by temporal focus past, present, or future with technologies to use reports, queries, alerts, statistical analysis, forecasting, prediction, and optimization.

• Colorado State University, Master of Science in Data Analytics 2022-present

(GPA-3.95)

• Springboard, Certification Data Analytics Career Track 2021

• Giresun University, Bachelor of Science in Physics Skills

DIDEM B. AYKURT

Phone:310-***-**** Email: ad3hho@r.postjobfree.com Location-WA Summary

Education

Education

Work

Experience

Colorado State University-Global April 2022- Present Master of Science in Data Analytics

• Prepared reports with SAS Studio, SAS Enterprise Miner, Phyton, R, and Power BI on financial statements (ROI, NPV, IRR), interpreted consumer behavior, and reported market opportunities and conditions to help meet business needs. Analyzed product behavior during the time and provided customer reviews and quality. Developed a pricing optimization algorithm that identified the relative price of each product and scaled them to absolute prices using historical data.

• The Kimball DW/BI and data warehouse architecture to identify the tables and their relations to create and extract required data.

• Create Target Data mappings for the individual reports.

• Prepared test cases to validate data and look and feel of the reports.

• Document all data mapping and transformation processes based on the business requirements in the Functional Design documents.

• Pre-process data to identify the features with missing data by writing Python, R, SAS Studio, and SAS Enterprise Miner scripts to perform data exploratory analysis.

• Writing scripts to implement Predictive Modelling algorithms.

• Interpret the results of the Predictive Models and document them in a project report. Amazon, WA Oct 2021- Mar 2022

Business Analyst

• Development and maintenance of new and existing artifacts focused on analyzing requirements, metrics, and reporting dashboards that put the right person in the right place, at the right time, and at the correct cost.

• Pull data from Redshift with SQL to compare and test all existing Quick Sight dashboard field results.

• Creating mock-ups of Dashboards and individual data visualizations in MS Excel.

• Create a dashboard on Quick Sight to show how to plan headcount growth so that business leaders can make critical decisions about their employee populations.

• My reports provide the capability for leaders to understand where their headcount is projected to grow and make prescriptive adjustments to that growth plan to ensure people, location, timing, and cost are optimally planned.

• Writing SQL queries to extract data from the HR data marts per the requirements and loading on the data net.

• Experienced working in Agile Methodologies with Asana. Springboard, WA Aug 2020 – Sept 2021

Data Analyst

• Python Tools: Pandas, NumPy, Seaborn, Matplotlib, SciPy. Stats, DEF, Scikit-Learn, Statsmodel.

• Built a churn prediction model that achieved above 80% accuracy on test data using supervised machine learning algorithms based on user demographics and behavior data.

• Performed data wrangling to correct all outliers and missing data values, handled big data sets, and utilized exploratory data analysis to hypothesize potential predictors and engineer new features.

• Model the training data to several regression algorithms: Random Forest, RFE (Recursive Feature Elimination), Linear and Logistic Regression (Original data, Standardized data, essential features), and ROC (Receiver Operating Characteristic).

• Developed Tableau data visualization using Scatter Plots, Geographic Map, Pie Charts, Bar Charts, and Density Chart.

• Performed time series analysis project with historical product data and sentiment analysis with data to build a sales growth predictor. The model achieved 55% accuracy in predicting quarterly sales revenue.

• Collected and analyzed financial data for data cleansing, preparation, and creating tables before Work

Experience

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