Data Science Internship Discription:
At Cloud Counselage Pvt. Ltd., as a Data Science Intern, the candidate will get a chance to engage in a professional work environment with exposure to talented and skilled mentors. This position will afford you practical experience in some of the most important aspects of data science including data analysis, machine learning, and more through exposing you to actual implementation.
Data Science Internship Details:
- Role: Data Science & Machine Learning; Other
- Industry Type: consulting Information Technology
- Department: Data Science & Analytics
- Employment Type: Full-time, permanent Another major factor that determines the employment status of the candidates is full-time/part-time and permanent/ temporary basis.
- Role Category: _machine learning & data science.
Responsibilities:
- Data Collection & Preprocessing: Collect relevant data from internal and external sources whereby the data collected should be cleaned up for normalization and pre-processing to enhance the data quality and integrity.
- Exploratory Data Analysis (EDA): Perform exploratory data analysis to depict dataset data trends, insights, and patterns which would facilitate further model development.
- Machine Learning Model Development: Design and implement models based on machine learning which are adapted for analytical predictive tasks and pattern identification. This involves choosing of right algorithms and proper model tuning for the right results to be produced.
- Model Evaluation & Improvement: Consistently assess the models with the right measures and modify the models whenever the given measures are compromised for better performance.
- Collaboration with Engineers: Collaborate with software engineers to place the developed machine learning models into production platforms, which can support the currently implemented applications.
- Communication of Findings: Provide technical as well as nontechnical visual as well as written summaries of results and findings so that all team members can work on them effectively.
- Staying Updated: Continuously update when it comes to tools, techniques, trends, and advancements in data science, machine learning, and artificial intelligence so that solutions proposed are state of the art, and optimized for use.
Requirements:
- To pursue a Bachelor’s or Master’s degree in Computer Science and Statistics, Mathematics, Engineering, and other related fields.
- Alternatively, should be familiar with programming languages such as Python or R.
- Knowledge about a data manipulation libraries such as Pandas, NumPy or TensorFlow.
- Awareness of statistical procedures and of machine learning techniques.
- Effective high-order thinking skills and critical reasoning ability.
- Desirable traits such as good communication and interpersonal skills.
- Self-motivation and ability to perform an individual task as well as be a part of a team.
- That means it is recommended that the candidates have had prior experience in the field of data science preferably through internships or projects.
Key Skills:
- Computer Science Fundamentals: Proficient in computer science fundamentals regarding variety and data structures and algorithms, algorithms, and programming design fundamentals necessary to perform data manipulation and model building.
- Data Analysis: That is the capacity to process data, and analyze data with use of statistical analysis and other analytical tools to draw useful conclusions that would be of immense benefit in the management decision-making process.
- Machine Learning: Familiarity in using machine learning algorithms for problems like classification, regression as well as clustering and best frameworks including TensorFlow or even Scikit-learn.
- Python: Specialization in Python, one of the data science languages in handling data, building models and automating activities with tools like Pandas, NumPy and Matplotlib.
- Data Quality Management: The emphasis on the data quality by paying considerable attention to data accuracy, data completeness, and data consistency at the stage of data acquisition and preparation.
- Pattern Recognition: Proficient in data mining techniques that helps to find patterns and outliers that are of great importance in predictive modeling.
- Analytical Skills: Outstanding problem-solving skills which are well illustrated when solving such issues that are related to data where the candidate can define the details and even come up with solutions.
- Interpersonal Skills: Basic oral as well as written communication skills that will aid in presenting technical information to multidisciplinary groups who may have little or no technical knowledge.
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