Shubham Kumar Sharma

I am a big believer in data science and always play bullish for all the hidden things revealed by data. I am a mechanical engineer turned data scientist. I am delving into Machine Learning, Data Visualization and Deep Learning and currently working as a Data Scientist. I can be found on practically every developer portfolio under the pseudonym @shubhamtheds. Aside from that, I share everything I learn about data science as I go, so feel free to follow me on all of my social media accounts.

My core areas of interest include machine learning, predictive analytics, and data analytics. I am also well-versed in programming languages such as Python, Git, and SQL. I am always eager to explore new data-driven technologies and to develop my skills as a data scientist.

• Developed data-driven models used to analyze large datasets and interpret customer behaviors and trends.
• Created predictive models used to forecast future outcomes and provide data-driven insights.
• Utilized machine learning algorithms to develop models that accurately interpret data.
• Designed and implemented experiments to evaluate model performance and accuracy.
• Developed visualizations and dashboards to present complex data in an easy-to-understand format.
• Collaborated with data engineers, product managers, and other stakeholders to ensure data accuracy and integrity.
• Investigated data anomalies and irregularities to identify potential areas of improvement.
• Worked with cross-functional teams to develop strategies for data-driven decision making.

DataCamp is an interactive learning platform that provides engaging, bite-sized courses to beginners and learners looking to actively learn new data science topics. Its courses and track programs are designed to help individuals gain new skills and explore career paths, ranging from non-coding essentials such as BI tools to data science and machine learning. Furthermore, DataCamp offers hands-on learning experiences, including data skill assessments to track progress and personalized recommendations.