Intern - Data Analytics

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The IT Summer Intern is an entry level position at BRG Sports where student will receive knowledge and training required to contribute as a valuable IT resource at BRG Sports. IT summer Intern will receive real-world work and add value to BRG IT team during their time at the BRG Sports.                               

Summer Intern will be required to support all related business projects and members of the IT team responsible for the implementation of all new business processes and project implementations. 

 

Qualifications:

For an Analytics/Data Science Intern position, companies typically look for a combination of technical skills, educational background, and some familiarity with data-related tools. Here are some common requirements:

 

Technical Skills:

• Programming languages: Knowledge of languages like Python, R, or SQL is essential. Python is especially popular for data science tasks.
• Data manipulation and analysis: Familiarity with libraries like Pandas, NumPy, or similar is often expected for handling data.
• Data visualization: Experience with tools like Tableau, Power BI, or libraries like Matplotlib, Seaborn (for Python) may be important.
• Statistical knowledge: An understanding of basic statistics (mean, median, standard deviation, distributions, hypothesis testing) is essential.
• Machine learning (optional): Some companies may look for knowledge of basic machine learning algorithms (e.g., linear regression, decision trees, clustering) though it may not always be a strict requirement for an internship.

 

Soft Skills:

• Problem-solving: Ability to approach and solve analytical challenges logically.
• Communication: Since you'll likely need to present insights or work with cross-functional teams, good communication skills are often necessary.
• Collaboration: Ability to work in teams and with data scientists, engineers, or business stakeholders.

 

Experience (may vary):

• Previous internships or projects: Any hands-on experience with data analysis or related projects (even personal projects) could be a big plus.
• Familiarity with databases: Knowledge of database management systems (e.g., MySQL, PostgreSQL, etc.) or cloud platforms can be helpful but might not be mandatory.

 

Certifications (optional but beneficial):

• Data Science bootcamps or certifications (like from Coursera, edX, or DataCamp) could be an advantage, especially if you don’t have direct work experience.

Responsibilities:

Common Responsibilities for Data Analytics Interns:

1. Data Collection & Cleaning:

• Gather data from multiple sources (e.g., databases, APIs, spreadsheets, etc.).
• Clean and preprocess data to ensure accuracy and consistency (e.g., handling missing data, removing outliers).
• Organize raw data into structured formats to be analyzed (data wrangling).

3. Data Analysis:

• Analyze datasets to identify trends, patterns, and insights.
• Use statistical methods to interpret data and validate results.
• Generate summary statistics (e.g., mean, median, standard deviation) to describe data distributions.

5. Data Visualization:

• Create visualizations (charts, graphs, dashboards) to present data insights clearly and effectively.
• Use tools like Tableau, Power BI, or Python libraries (e.g., Matplotlib, Seaborn) for visual representation.

7. Reporting & Documentation:

• Assist in preparing reports or presentations to communicate findings to stakeholders.
• Document methodologies, findings, and insights clearly for future reference.
• Help with creating standard operating procedures (SOPs) or documentation for data-related processes.

9. Supporting Decision-Making:

• Collaborate with other teams (e.g., marketing, product, finance) to help them make data-driven decisions.
• Help with forecasting, trend analysis, or identifying key business metrics.
• Perform ad-hoc analyses as requested by team members or managers.

11. Automation & Tool Development (sometimes):

• Assist in automating data collection or reporting processes.
• Build or enhance simple tools/scripts (e.g., using Python or SQL) to improve efficiency.

13. Learning & Development:

• Stay up-to-date on the latest analytics tools and methodologies.
• Continuously learn from feedback, mentorship, and real-world analytics problems.

 

Potential Advanced Tasks (for more experienced interns or as you progress):

• Predictive Modeling: Building and testing machine learning models (e.g., regression models, classification).
• Data Warehousing: Interacting with or optimizing data storage systems like SQL databases, cloud services, or data lakes.
• A/B Testing: Designing and analyzing experiments to test different business strategies or product features.

 

Educational Background:

• Current Enrollment: interns are students currently pursuing a degree in fields such as Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• Some experience with data: Even though it's an intern role, a basic understanding of data analysis or some relevant coursework might be required.

ExperienceRequired* Current Enrollment: interns are students currently pursuing a degree in fields such as Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

Preferred* Some experience with data: Even though it's an intern role, a basic understanding of data analysis or some relevant coursework might be required.

Equal Opportunity Employer

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