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• 06 Months

• Internship

We are looking for fresher interns to join our team and contribute to live projects focused on data analytics. As a Data Analytics Intern, you will have the opportunity to gain valuable hands-on experience in various aspects of the field, including data analysis, data visualization, and data-driven decision-making. This internship offers the potential for future full-time employment based on exceptional performance.

As a Data Analytics Intern, you will learn practical aspects on the following topics:

1. Data Collection and Cleaning.
2. Data Analysis Techniques.
3. Data Visualization Tools
4. Advanced Excel Functions
5. Power BI and Tableau
6. Statistical Analysis

1. Familiarity with computer application
2. Basic Knowledge of Excel
3. Familiarity with Power BI and Tableau
4. Analytical and problem-solving skills
5. Basics of statistical analysis

1. Letter of Recommendation
2. Certificate of Internship Completion
3. Hands-on experience on Live Projects.
4. Training & Mentorship from Industry Expert.

1. Assist in data collection, preprocessing, and cleaning tasks to ensure data quality and reliability.
2. Collaborate with the data science team to develop and implement machine learning models and algorithms.
3. Conduct exploratory data analysis to uncover patterns, insights, and trends that contribute to business objectives.
4. Support the development of predictive models and statistical analyses to solve business problems and optimize processes.
5. Assist in the evaluation and selection of appropriate data science tools, frameworks, and libraries.
6. Help maintain and update existing data pipelines and workflows to ensure efficient data processing and analysis.
7. Contribute to the documentation of data science projects, including code, methodologies, and findings.
8. Stay up to date with the latest advancements in data science and machine learning techniques.

1. Currently pursuing a Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, or a related field.
2. Solid understanding of data science fundamentals, statistical analysis, and machine learning concepts.
3. Proficiency in programming languages such as Python or R, and familiarity with data manipulation libraries (e.g., pandas, NumPy).
4. Experience with data visualization tools (e.g., Matplotlib, Tableau) to communicate insights effectively.
5. Strong problem-solving skills and the ability to work with large datasets to derive actionable insights.
6. Knowledge of SQL and experience working with relational databases.
7. Familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow) is a plus.
8. Excellent communication skills and the ability to work effectively in a team environment.
9. Attention to detail and the ability to manage multiple tasks and deadlines.

1. Gain practical experience in data science and machine learning through hands-on projects.
2. Collaborate with experienced professionals and mentors who will provide guidance and support.
3. Exposure to cutting-edge tools and technologies used in the field of data science.
4. Opportunity to contribute to real-world projects and make an impact on the company's success.
5. Networking opportunities with professionals in the industry.
6. Possibility of a full-time position based on performance and availability.

1. Develop Big data solutions for Fortune-500 customers
2. Work on Big Data Ingestion, Pre-processing, Storage, Parsing, Analyzing and Visualization
3. Study large sets of data to turn information into business insights
4. Get End-to-end ownership of deliverables - POC to production
5. Work with senior executives of top brands and provide expert recommendations
6. Work with a team of exceptionally talented and dedicated Data Scientists and Engineers
7. BYOP - Bring your own process. We like new things.

1. Bachelors or Masters Degree in Computer Science/Information Technology/Statistics/Mathematics or relevant streams
2. 2-3 years of hands-on experience
3. Proficiency in Big Data ecosystems like Azure, Amazon, Google Cloud
4. Experience with Analytics tools like Google Analytics, Firebase, Tableau, Power-BI, Microstrategy
5. Experience in distributed computing frameworks: Apache Hadoop 2.0 (YARN; Map Reduce & HDFS) and associated technologies -- one or more of Hive, Sqoop, Flume, Zookeeper, etc.
6. Experience with Kafka / Apache Spark (Streaming, SQL, MLLib)/ Apache Apex / Apache Storm
7. Experience with predictive & prescriptive statistical analysis
8. Problem-solving: Ability to break the problem into small problems and think of relevant techniques which can be explored & used to cater to those
9. Experience working within a Linux computing environment and use of command-line tools
10. Experience and interest in Machine Learning is a plus

1. Solve challenging real world problems using bleeding edge technology and tools.
2. Work on machine learning based solutions around multiple domains like Sales Forecasting, Customer Propensity, Price Optimization, Classification Models, Recommender Systems, Sentiment Analyzer Models, Contextual Conversation Modelling.
3. Get End-to-end ownership of deliverables - POC to production.
4. Work directly with customers & users - understand their needs and provide expert recommendations.
5. Work with a team of exceptionally talented and dedicated Data Scientists and Engineers.
6. BYOP - Bring your own process. We like new things.

1. Bachelors or Masters Degree in Computer Science/Information Technology/Statistics/Mathematics or relevant streams.
2. 2+ years hands-on experience.
3. Experience in Regression, Machine Learning, Deep learning etc.
4. Expert level proficiency in at least one of R and Python.
5. Ability to discover effective solutions to complex problems. Strong skills in data-structures and algorithms.
6. Experience of working on a project end-to-end: problem scoping, data gathering, modeling, insights, and visualizations.
7. Problem-solving: Ability to break the problem into small problems and think of relevant techniques which can be explored & used to cater to those.
8. Intermediate to advanced knowledge of machine learning, probability theory, statistics, and algorithms.