SUMMARY:

• Around 2 years of experience including analysis, development in Python and Data visualization using Tableau and Qlikview.
• Experience with Data analysis libraries like Pandas, NumPy and Data visualization libraries (Matplotlib, Seaborn).
• Experience in working with notebook technologies like Jupyter and Anaconda.
• Worked with Django and Flask web framework.
• Hands - on working with Tableau Analytics and Build Dashboards using Tableau Desktop.
• Worked with QlikView to create dashboards and writing script for calculated fields.
• Experience in RDBMS like MySQL and NoSQL Databases like MongoDB and HBase.
• Hands-on experience with Shell scripting for automation of tasks.
• Knowledge in working with AWS services like S3, EC2 and writing Lambda Functions.
• Good knowledge on Hadoop Cluster Architecture and its components.
• Hands on experience in working with the streaming data using Kafka and batch data processing from RDBMS using Sqoop.
• Good team player and ability to handle multiple tasks in a team environment.

TECHNICAL SKILLS:

Programming Languages: Python, R, C++, C, Bash

Data Analysis: Pandas, NumPy, Scikit: learn, SciPy

Data Visualization: Tableau, QlikView, Matplotlib, Seaborn, Plotly

Web Technologies/Frameworks: Django, HTML5, CSS, Rest APIs, JavaScript, React(familiar)

Databases: MySQL, MongoDB, HBase

Bigdata Technologies: Hadoop, HDFS, Hive, Spark, SparkSQL, Pyspark, Sqoop, Kafka, Spark Streaming.

Cloud Technologies: Amazon Web Services (S3, EC2, Lambda)

Tools:: GIT, JIRA, IntelliJ, PyCharm, WebStorm, Jupyter

PROFESSIONAL EXPERIENCE:

Data Analyst

Confidential, St. Louis, MO

Responsibilities:

• Experience using Tableau administration tool, Tableau Interactive Dashboards and Tableau Suite.
• Provided Support for Tableau developed objects and understand tool Administration.
• Worked in Tableau environment to create dashboards like weekly, monthly, daily reports using Tableau Desktop.
• Develop, Organize, manage and maintain graphs, tables and document templates for the efficient creation of reports.
• Analyzed Customer requirements and prepared design/mapping documents.
• Created Complex Dashboards, Reports using WEBI having multiple metrics and trends.
• Experience in working with SQL quires based on the source to target mapping for Target tables.
• Understanding and practical experience in developing Spark applications with Pyspark.
• Good Hands-on experience with Hive partitioned tables, map join, bucketing and Dynamic Partitioning.
• Strong knowledge on HDFS commands to manage the files and working with streaming services like Kafka and Spark Streaming.
• Knowledge in using various compression techniques like Parquet file format.

Python Developer Intern

Confidential, Boston, MA

Responsibilities:

• Used Pandas, NumPy, Seaborn, Matplotlib and Scikit-learn in Python for developing script for data comparison and visualization.
• Setup storage and data analysis tools in Amazon web services cloud computing infrastructure.
• Experience in working with large datasets and preprocessing it for data comparison.
• Implemented end-to-end systems for Data analytics, Data automation and integrated with Tableau.
• Collected large amounts of data from different sources to provide the data lake for comparison.
• Exposure to different technologies in Big data including Hadoop, Spark, Hive and Kafka.

Python Developer

Confidential

Responsibilities:

• Mainly used Python alongside using various libraries such as Matplotlib for visualization, MySQL for database connectivity, Pyside, Pickle, Pandas data frame.
• Involved in creating Dashboards and reports using Tableau. Created report schedules on Tableau server.
• Created Tableau dashboards using stack bars, bar graphs, scattered plots, geographical maps, heat maps, bullet charts, Gantt charts demonstrating key information for decision making.
• Integrated Tableau dashboards into internal web portal using Java Script API.
• Used Multiple Input Filters, Memory data sorter, Nested Reports, Sequential / Parallel Section, Use of Actuate Foundation Classes, Sub classing and customizing Frames and Data Controls.
• Experience in developing interactive UI and web applications using HTML, CSS and Javascript.
• Expertise in working with the groups, hierarchies and sets. Hands on with the trend lines, statistics and log axes to create detail level summary reports and dashboard using KPI’s.

Technologies Used: Python, Pandas, Matplotlib, Seaborn, NumPy.

Confidential

Responsibilities:

• Accessed data using Confidential API and preprocessed it for the analysis and visualization.
• Preprocessed data using Regex to handle the data quality and tidiness issues.
• Using the Descriptive analytics, answered different questions like popular breeds, names, how well the model performed as the data is generated by the image recognition model.
• Used various pandas’ methods like melt () and pivot () to combine and declutter the data, duplicated () to get the duplicated data.
• Used the Seaborn heatmap to plot the correlation graphs and Matplotlib for histograms and pie charts.

Confidential

Technologies used: Python, Pandas, Matplotlib, Sckit-LearnThe data is taken from UCI machine learning repository.

Responsibilities:

• Preprocessed data by replacing missing values with the mean and using StandardScalar method.
• Performed feature engineering to create the features for the predictive model. There are numerical and categorical features used for the model.
• Divided the data in to training and validation data by using the random samples, which eliminates biasing of model.
• Used different techniques such as KNN, Logistic Regression, Stochastic gradient Decent, Random forest and Naive Bayes.
• From all the model, abased on the ROC curve we determine the effective model for the testing with the new data.

Confidential

Technologies used: Python, Pandas, Scikit-Learn, Matplotlib, Seaborn

Responsibilities:

• Used different pandas’ functions like ProfileReport and Merge.
• Preprocessed data for the analysis and eliminated outliers to get accurate correlation plots.
• Used geoplotlib to plot the geographical data and heatmaps on the map to show the affected areas.
• Plotted graphs using Matplotlib and Seaborn to get the insights from the data.