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Top Data Science Internship Skills

One of the most important hard skills a research intern can possess is knowledge of programming languages such as Python. Programming skills are highly marketable, and will give research interns the edge up on other candidates. It's also important for research interns to have the hard skill of data analysis, and developing predictive models.

When it comes to soft skills, data science interns benefit from strong written and verbal communication skills, especially in data science internships involving writing. Data science interns need to be great at taking direction as well, so good listening skills are also crucial.

Below we've compiled a list of the most important skills for a data science internship. We ranked the top skills based on the percentage of data science internship resumes they appeared on. For example, 16.9% of data science internship resumes contained python as a skill. Let's find out what skills a data science internship actually needs in order to be successful in the workplace.

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The six most common skills found on Data Science Internship resumes in 2020. Read below to see the full list.

1. Python

high Demand

Python is a widely-known programming language. It is an object-oriented and all-purpose, coding language that can be used for software development as well as web development.

Here's how Python is used in Data Science Internship jobs:
  • Created multiple scripts in Python to perform data manipulation and analysis as well as calculations on energy production and data visualization.
  • Developed and deployed additional NLP applications (language identification and named entity recognition) with Python and Docker.
  • Implemented a distributed and asynchronous Python web scraping application for enriching retailer product data on millions of items.
  • Implemented User-User and Item-Item Collaborative Filtering in python to customize recommendation topics and articles to individuals and segments.
  • Developed a hierarchical text classification model in python to classify websites into multilevel/ multiple taxonomy.
  • Implemented a machine learning algorithm for data analysis and loan application fraud detection using Python.
  • Collaborated with database administrators by writing Python scripts for automation and data warehouse maintenance.
  • Implemented a particular density-based clustering algorithm in Python which outperforms other algorithms.
  • Designed and implemented natural language processing exercises using NLTK and Python.
  • Researched mine terminology and used python module to analyze natural language.
  • Conducted investigative projects utilizing Python for data analysis and machine learning.
  • Worked to develop deliverable API for document classification in Python.
  • Developed python scripts for anomaly detection using statistical measures.
  • Created tool to extract relevant news on Twitter by performing content analysis of tweets using Python and the Twitter API.
  • Performed several data analyses in Python to extract significant health trends about smoking, knee pain and mental health.
  • Implemented both LDA model and neural network based on Word2Vec in Python to extract keyword on job posting documents.
  • Project 1: Creation of background python code for MindFabric, a user-friendly online machine learning pipeline building application.
  • Used Java and Python for writing scripts, web scraping and built a collaborative filtering based recommendation system.
  • Computed polarity of user reviews by building a sentiment analysis tool using Python, NLP, and Machine Learning
  • Reported irregularities and corrective measures to be taken; developed python scripts to standardize data cleaning process.

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2. R

high Demand

R is a free software environment and a language used by programmers for statistical computing. The R programming language is famously used for data analysis by data scientists.

Here's how R is used in Data Science Internship jobs:
  • Analyzed internal WebBoard trends using R statistical software; identified significant trends, social network characteristics, and structure recommendations.
  • Designed an interactive visualization interface to generate financial data summary and model output with R Shiny and Tableau.
  • Created and documented survival analysis model using R and SmartBrief's databases to analyze subscriber engagement.
  • Authored and developed proprietary R package to maintain and distribute survey analysis tools.
  • Analyze students' academic achievement and financial aid consequence utilizing R and Excel
  • Conduct appropriate statistical analyses of data using R, including multivariate regressions.
  • Transformed parametric maximum likelihood estimations models in to R codes.
  • Leveraged Excel and R for data analysis and visualization
  • Utilize Microsoft Office applications, Tableau Software, and R to analyze training data and make strategic decisions about player management.
  • Used R for data cleaning activities and predictive modeling; in addition to social network analysis of a new product.
  • Cleaned numerical and categorical data in R and SQL, and extracted features from text data applying basic NLP techniques.
  • Used R Shiny to produce interface apps for those models, especially for the constrained parametric multivariate random effects model.
  • Developed predictive models (supervised and unsupervised) for a Medicare use case in R, to predict patient cost.
  • Developed linear and logistic models using R and SAS; Predicted sales using machine learning algorithms (MATLAB).
  • Developed Linear Regression models in R to predict student/Teacher growth and improved accuracy by 3-4% using transformation techniques.
  • Performed Twitter Sentiment Analysis using R on 1000 recent tweets with LabCorp in order to improve customer service.
  • Queried database for information with SQL and applied OpenRefine and R to clean and integrate the messy data.
  • Clean a variety of complex data using R, SPSS, advanced excel functions, and VBA macros.
  • Established an R package with the EM algorithm and feature functions for latent class clustering and regression.
  • Used the R package SuperLearner to create an ensemble of classification algorithms to predict readmission within 30 days

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3. Analytics

high Demand

Here's how Analytics is used in Data Science Internship jobs:
  • Created client focused product demonstrations and presentations utilizing Oracle s proprietary big data and advanced analytics platform.
  • Implemented SAS analytics to determine service improvements, cost effectiveness and marketing tactics.
  • Performed segmentation for promotional marketing using predictive analytics to classify the patients.
  • Developed in-house analytics platform for visualizing and analyzing customer use data.
  • Perform predictive analytics including regressions and assist in building statistical models.
  • Performed descriptive analytics using Tableau (trend analysis; customer segmentation)
  • Observed and implemented basics of default analytics and business intelligence.
  • Provide assistance in integrating analytics working with marketing decisions.
  • Implemented SAS analytics to develop sensor data validity.
  • Created hospitality analysis manual for audit analytics guide.
  • Implemented Random Forest model for predictive analytics.
  • Design and implement data mining algorithms, data cleaning procedures and data analytics, including machine learning and big data applications.
  • Analyzed and presented a case study on Energy Star product awareness in Kansas and Missouri, under the data analytics team.
  • Streamline internal analytics process that generates report trends, identifying potential risks and weak areas that the company should target.
  • Worked directly with the marketing analytics team to find areas for improvement in GM's data profiling process.
  • Operated social media analytics tool, Simply Measured, to track posts for Kid's Choice Sports.
  • Used predictive analytics to seek solutions and improve business operations as part of a data science team.
  • Provide data analytics detailing the quantity, aggregate dollar amount, and frequency of qualified bookings.
  • Derived deep insights about the brand and customers using client products by utilizing social media analytics.
  • Optimized LinkIt navigator models using Excel analytics techniques to reduce run-time by approximately 15%.

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4. Data Analysis

high Demand

Here's how Data Analysis is used in Data Science Internship jobs:
  • Performed data analysis of maintenance records that allowed an optimization of the maintenance procedures during the reactor shut-down.
  • Completed data entry, data reduction, and data analysis of experimental results obtained during human subject testing.
  • Provided data analysis and research in support of Energy Acuity's international renewable energy project database.
  • Worked in fast-paced environment to deliver 5-6 policy and procurement-focused data analysis projects per month.
  • Performed APP user data analysis in Excel by applying regression analysis and other statistical methods.
  • Conducted various experiments while obtaining experience in practical data analysis and industry.
  • Performed data analysis on collected data for supervisors and fellow staff members.
  • Conducted semiweekly meetings with clients to present data analysis and modeling results.
  • Performed data analysis of dose solutions and solubility assays.
  • Presented data analysis results and insights to internal customers.
  • Developed data visualizations to present results of data analysis.
  • Performed various data analysis to predict business trends.
  • Upgraded database queries adding data analysis information.
  • Assisted data analysis in clinical trials for new treatment testing initiating the final decision of the success of a new treatment.
  • Assisted quality engineers with data analysis, explanations, pivot tables, and PowerPoint Presentations for warranty meetings with General Motors.
  • Prepared slides and presented findings to mangers and clients to help them to understand the patterns and numbers from data analysis.
  • Completed initial validation of data-sets and make it ready to use by other departments within the organization for data analysis.
  • Participated in launching a new web service project integrating fetching data, data processing, data analysis into a hub.
  • Assessed key performance trends by translating large data sets into actionable business insights that identify where to invest marketing dollars.
  • Used C++ to implement data processing and sorting algorithms to increase ease of access and simplify data analysis.

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5. Machine Learning Algorithms

high Demand

Machine learning algorithms involve the engines of machine learning. It consists of the algorithms that turn a data set into a model.

Here's how Machine Learning Algorithms is used in Data Science Internship jobs:
  • Implemented various machine learning algorithms to identify an anomaly.
  • Apply machine learning algorithms in order to add features to the value stream of the firm.
  • Implemented machine learning algorithms to help optimize Facebook Ad campaigns.
  • Utilize Machine Learning algorithms and statistic methods to find the correlations between medications and diseases, medications and patients age.
  • Added a recommendation tab which was made by collecting clickstream data and applying machine learning algorithms such as clustering, logistic
  • Refined huge data using machine learning algorithms and Apache Solr to create CSV files and charts efficiently

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6. Big Data

high Demand

Big data is the collection of a huge volume of data and is growing exponentially with time. This type of data can not be dealt with and stored by conventional data management tools and applications.

Here's how Big Data is used in Data Science Internship jobs:
  • Accelerated the progress of projects for the whole data team in SPC and encouraged staff to deal with Big Data technically.
  • Use HTML, CSS, and JavaScript to optimize AT&T s internal Big Data website for mobile viewing.
  • Designed and developed the proactive maintenance feature for IoT devices using big data tools like Apache Kafka and Spark.
  • Worked closely with product owners and data workers / analysts to understand business objectives for Big Data platform.
  • Developed an application for AWS to automatically deploy the necessary Big Data tools in EMR & EC2 Instances.
  • Assisted the Big Data Consulting team and collaborated with data scientists and management on use cases for clients.
  • Project: I-GPS Application for Analysis and Visualization of Patient-Level Big Data Related to RA and NASH.
  • Managed all Big Data initiatives including email campaign, social media monitoring, and brand marketing.
  • Collaborated with AEG s Big Data team to develop marketing materials for company online presence.
  • Brainstorm for the new Big Data Algorithm in the future.
  • Created and maintained statistical models applied to big data.
  • Performed trend and statistical analysis on large airline datasets-Big data.
  • Studied the Big Data Development and Management Platform (DACP) and Cloud Crawler Platform (with Pyspider) of enterprise2.
  • Developed Big data application to stream and store terabytes of data using Big data tools such as Flume and Oozie.
  • Evaluated use cases for Hadoop and other Big Data technologies.
  • Worked on Hadoop Big Data Ecosystem and its Components.
  • Learned Hadoop and HDFS platform for big data and Used Hive and Pig to pull data sources together.
  • Negotiated with Mediatrac Big Data Analytics for the future cooperation.
  • Assisted with creating a Big Data Platform on Apache Hadoop, Spark, Storm, YARN, HIVE, and Kafka.Life.Church
  • Unstructured Text Analytics using IBM Watson Creating model for Natural Language Processing Helping Clients use Big Data to develop business strategy

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7. Predictive Models

high Demand

Here's how Predictive Models is used in Data Science Internship jobs:
  • Analyzed customer transaction habits providing predictive models and summaries to identify new potential customers.
  • Participated in the development of predictive models with back-testing and cross-validation.
  • Led development of predictive models to classify flight revenue generation.
  • Spearhead the company's use of predictive models to combat issues including global migration, churn, and accurate sales forecasting.
  • Build predictive models of likelihood of conversion, ad receptivity, and exposure; feature engineering in SQL.
  • Designed predictive models to compute the probability of a prospect enrolling in any given program.
  • Evaluated new internal and external data elements and technologies to be included in predictive models.
  • Integrated our predictive models with existing infrastructure to run in parallel on the cloud.
  • Conduct market research to create predictive models for presentation to clients.
  • Developed predictive models in order to analyze data sets.

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8. Data Visualization

high Demand

Here's how Data Visualization is used in Data Science Internship jobs:
  • Developed data visualization modules for the data collected from electrical, mechanical and chemical tests on batteries.
  • Performed data-driven consumer segmentation using k-means clustering algorithm in R. * Generated data visualization in Tableau.
  • Conducted secondary market research including: industry trend, data visualization, and competitor analysis.
  • Developed and executed advanced statistical analysis as part of a data visualization challenge competition.
  • Modified UI components and added new functionality to OpenGraphiti, a data visualization framework.
  • Submitted weekly reports to clients with actionable advice presented using interactive Tableau data visualizations.
  • Designed interactive and easy to understand data visualization from market data for non-technical team.
  • Performed statistical analysis and data visualization on KPI to give reports.
  • Created geographical data visualizations and graphical representations of the data.
  • Designed and developed Tableau dashboards for data visualization tasks.
  • Applied Data Visualization Techniques to generate visual results.
  • Explored data visualization software options for the University.
  • Developed rich visualization on each step of model building, including data visualizations of large amount of structured data.
  • Designed interactive data visualizations using Tableau, which were published in UNDP s Africa Human Development Report 2016.
  • Project on 'Substance Abuse among Youth' with the aim of spreading awareness through data visualization.
  • Create research segments to be aired on a financial news network featuring data visualizations and inferences.
  • Performed data visualization using bar graphs, heat maps, pie charts etc.
  • Applied data visualization in Tableau to check the water usage trend.
  • Created data visualization dashboard for the Indian stock market using Tableau.
  • Created presentations based on data visualization data to exemplify the power of data visualization on managerial decisions.

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9. Java

high Demand

Java is a widely-known programming language that was invented in 1995 and is owned by Oracle. It is a server-side language that was created to let app developers "write once, run anywhere". It is easy and simple to learn and use and is powerful, fast, and secure. This object-oriented programming language lets the code be reused that automatically lowers the development cost. Java is specially used for android apps, web and application servers, games, database connections, etc. This programming language is closely related to C++ making it easier for the users to switch between the two.

Here's how Java is used in Data Science Internship jobs:
  • Developed Java Auto-correction algorithm using Artificial Intelligence and Probabilistic Machine Learning techniques for SAP Ana, a digital assistant for enterprise.
  • Established SVM classifier and discovered 8 different people moving patterns by defining and computing phone users' trajectory features in Java.
  • Created web scraping tools in Java to find all filling station locations in the US and store them in SQL Server.
  • Build a recommendation system {Collaborative Filtering Technique} using Mahout, Java that recommends Tests to Patients.
  • Performed joins, group by and other operations in MapReduce by using Java and PIG.
  • Coded java file conversion program converting text files to KML files visualized in Google Earth.
  • Viewed files and hardware using DOS, UNIX, and JAVA Script.
  • Developed discriminative features to boost the classification accuracy, using the domain specific language written in Java.
  • Constructed RESTful web services to get processed data from MongoDB utilizing Java.
  • Involved in data preprocessing of DNS data using Java Map Reduce.
  • Implemented the algorithm in Java integrated with Gurobi 5.0.
  • Aimed to make simulations compatible with Java and JavaScript tools (JNeuroML, NeuroConstruct, Geppetto).
  • Implemented custom UDFs, UDAFs and UDTFs in Java for Hive to support data processing and querying.

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10. Hadoop

high Demand

Hadoop is an open-source software and procedures framework that is free for anyone to use on the internet. Hadoop aids in big data operations. It allows massive data storage, applications to be run on commodity hardware, and can easily manage to run various tasks occurring at the same time.

Here's how Hadoop is used in Data Science Internship jobs:
  • Played a critical role in collecting data from different data sources and data system like SAP, JDE and Hadoop.
  • Surveyed capabilities of IBM Watson, MapReduce, Hadoop, HDFS, IBM BigInsights as part of a study project.
  • Analyzed internet data stream on Hadoop platform and studied users' behavior for e-business website in China.
  • Developed an app for recommending healthier food options for the customer, using Microsoft Azure and Hadoop.
  • Collected the New York Yellow Cab taxi trip data and pushed into the Hadoop File System.
  • Utilized Hadoop and MapReduce to generate dynamic queries and extracted data from HDFS.
  • Involved in extracting the data from various sources into Hadoop HDFS for processing.
  • Utilized the Hadoop system and other distributed computing architecture of SAS Products.
  • Run concurrently using Celery task queue and Hadoop distributed file system.
  • Handled configuration implementation for the maintenance of the Hadoop cluster.
  • Analyzed massive network traffic data using Hadoop Map-Reduce and Hive.
  • Worked on the Hadoop ecosystem - Pig.
  • Improved the performance of ingesting data (ETL) into Hadoop by 92% by implementing Apache Flume and Sqoop.
  • Worked with Data warehousing team to migrate data from SQl server to Hadoop by creating ETL jobs.
  • Installed, configured and deployed a 10 node Cloudera Hadoop cluster for development, production and testing.
  • Created VM with Ubuntu Worked on Hadoop Gen1 in Standalone mode.
  • Deployed the Hadoop clusters for SAS Visual Analytics under Linux environment.
  • Maintained Vertica & Hadoop clusters to handle an average of 60 months (~1.5 TB) of healthcare claim data.
  • Configured and indexed huge data of call transcripts from Hadoop into Elasticsearch and visualize using Kibana.
  • Created a technical comparative analytical report of implementing algorithms on single node and multi-nodeclusters of apache Hadoop and apache spark.

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Jobs With Trending Skills

11. C++

high Demand

C++ is a general-purpose programming language that is used to create high-performing applications. It was invented as an extension to the C language. C++ lets the programmer have a high level of domination over memory and system resources. C++ is an object-oriented language that helps you implement real-time issues based on different data functions

Here's how C++ is used in Data Science Internship jobs:
  • Develop network feature extraction tool using C++ Increased prediction accuracy from 74% to 80% Obtained biologically meaningful network features
  • Boggle -- Implement boggle board game with C++, ternary tries/ multi-tree data structure and using DFS algorithm.

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12. Analyze Data

high Demand

Analyze data or data analysis refers to the practice of studying, organizing, and transforming data to make it more useful. It also includes the cleansing of non-useful information which helps in better decision making regarding any particular matter. Analyze data is a practice that is used widely in the field of business, social sciences, and science.

Here's how Analyze Data is used in Data Science Internship jobs:
  • Analyze databases to establish understanding of company infrastructure.
  • Work in Excel and SPSS to analyze data based on surveys for clients.
  • Applied mathematics, statistics and computer programs (SPSS) to analyze data.
  • Analyze data to identify and interpret trends and patterns in complex data sets.
  • Use of various software programs to collect and analyze data.
  • Research and analyze data related to current Delivery System Reform Incentive Payment Program (DSRIP) reports.
  • Assist in data mining project Analyze data and report results
  • Demonstrated strong analytical writing and ability to analyze data in an appropriate manner for the intended audience.
  • Analyze data with corporate representatives to implement strategic marketing initiatives, increasing the organizations visibility.
  • Designed Matlab programs to analyze data collected from Ozone Sonde launches for easy distribution of the data to relevant authorities.
  • ed organize and analyze data for a team of researchers within the Mayo Clinics Physiology and Biomedical Engineering program.
  • Design and implement Data Collection procedure Design the Table and Database structure using MS SQL2008R Analyze data using SQL 2008R2

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13. SAS

high Demand

SAS stands for Statistical Analysis System which is a Statistical Software designed by SAS institute. This software enables users to perform advanced analytics and queries related to data analytics and predictive analysis. It can retrieve data from different sources and perform statistical analysis on it.

Here's how SAS is used in Data Science Internship jobs:
  • Utilized statistical methods to forecast sales volume based on customer purchasing history data using SAS.
  • Adopted statistical modeling techniques in SAS to evaluate customer lifetime value with cohort analysis.
  • Performed regression analysis and developed data models using the SAS programming language.
  • Designed, developed and unit-tested SAS program code, functions and scripts to model trends in different methods of cancer detection.
  • Performed data transmission, cleansing, edit check, validation and mapping data between SAS and Excel data formats.
  • Created SAS and SQL programs using company database to create and analyze workable data sets for new marketing initiatives.
  • Utilized SAS and SQL extensively for collecting, validating and analyzing the raw data received from the client.
  • Project work was completed utilizing various programs and tools including SAS Enterprise Guide and SAS Contextual Analysis.
  • Conducted analyses on the data that was collected from these studies using the statistical package SAS.
  • Write queries in SAS and SQL data bases for updating insurance and clinical data sets.
  • Devised multiple SAS programs to facilitate data collection, cleaning, modification and output processes.
  • Time series analysis of milk sales in the event of contamination crisis using SAS.
  • Analyzed data on childhood asthma from the Healthy Homes Project using SAS version 9.2.
  • Queried large data sets using SAS PROC SQL to put together reports for clients.
  • Processed, Cleaned, Integrated and Verified data used for analysis using SAS.
  • Fulfilled an experience sharing session on SAS usage guide to team members.
  • Performed Twitter Sentimental analysis using SAS and SQL to decide Twitter campaigns.
  • Worked with the SAS (Statistical Analytic System) database system.
  • Use SAS tools to complete an analysis while cleaning data.
  • Write SAS code to analyze and structure educational assessment data.

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14. Powerpoint

high Demand

Powerpoint is an application developed by Microsoft which allows users to create slides of important information to present. It is used mainly for school presentations and businesses. It is commonly used and regarded as the "gold standard" in the field of presentation applications.

Here's how Powerpoint is used in Data Science Internship jobs:
  • Identified and communicated new trends to upper level management & presented insights using PowerPoint.
  • Presented data periodically to DoSomething.org chief officers (PowerPoint)
  • Presented the project at the ComEd intern fair using a combination of poster board and PowerPoint deck.
  • Set up and created work stations to assist audio learning by testing concepts displayed by PowerPoint.
  • Created and presented a PowerPoint project on the analysis and results of my experiments.
  • Presented research project in PowerPoint format at the National Institute of Health and UCSF.
  • Researched information, created PowerPoint's, and presented presentations.
  • Implemented technology into the classroom through an online book tool, PowerPoint and the use of iPads during group-work.
  • Analyzed and reported on sales data using Excel and PowerPoint.Updated databases.
  • Worked extensively with Microsoft Excel in order to better utilize existing information Developed Powerpoint presentation to management regarding extrapolations of data.
  • Presented recommendations for Strategy to clients such as Helio, Kinecta Bank and others using PowerPoint and Excel reports.

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15. Tensorflow

average Demand

Tensorflow is an open-source framework that acts as a free library and learning software for machine learning, developed by Google researchers to run machine learning, deep learning, and other kinds of learning. The applications of TensorFlow are vast but a majority of its focus is directed towards the training and inference of deep neural networks.

Here's how Tensorflow is used in Data Science Internship jobs:
  • Applied Tensorflow package to product the Siamese Recurrent Networks, trained and developed model.
  • Claim Damage Estimate: use deep learning tool TensorFlow for image classification on damaged cars.
  • Lead TensorFlow based deep learning workshops for CME employees.
  • Build up Convolutional Neural Networks under TensorFlow framework for image recognition.
  • Implemented neuronal networks to detect tampering activity using Keras and TensorFlow.
  • Developed a neural network model, using tensorflow, to automate buzz comments detection at an accuracy above 92%.

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16. Data Collection

average Demand

Data collection means to analyze and collect all the necessary information. It helps in carrying out research and in storing important and necessary information. The most important goal of data collection is to gather the information that is rich and accurate for statistical analysis.

Here's how Data Collection is used in Data Science Internship jobs:
  • Obtained clinical data collection forms and source documentation required for donation related activities.
  • Administer assessment data collection for eligibility and needs assessment and others as required.
  • Designed data collection database from surveys and questionnaires provided to participants.
  • Assist in experiment preparation and data collection and management.
  • Cooperated with school officials to organize daily data collection.
  • Collected relevant data locally and from data collection system to determine if damaged components are able to be repaired or replaced.
  • Gained experience in the field of conservation, using GIS technology for data collection of three highly invasive plant species.
  • Research and data collection on universities and institutions for briefs created for high level visitors to the British Consulate.
  • Appointed as team leader to pick up and return equipment and provide guidance to other interns during data collection.
  • Identify and input new contacts into CRM using various data collection techniques including web-based research.
  • Gained insight on data collection and its relevance to program design for the SHAPE Program.
  • Implement innovative systems for data collection, storage, and management of user details.
  • Mentored 3 high school interns regarding research design and data collection methods.
  • Observe process of database management and data collection in Microsoft SQL Server.
  • Assist in data collection and data entry within the Prevention Research Center.
  • Reviewed data collection for completeness and accuracy using MCIS and Excel.
  • Performed research and data collection for Phase I Environmental Site Assessments.
  • Conducted data collection, and analysis for a Dept.
  • Helped in the revamping of data collection infrastructure.
  • Traffic data collection, traffic model forecasting.

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Online Courses For Data Science Internships

One of the best ways to acquire the skills needed to be a data science internship is to take an online course. We've identified some online courses from Udemy and Coursera that will help you advance in your career. Since data science interns benefit from having skills like python, r, and analytics, we found courses that will help you improve these skills.

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IBM Data Science
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Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills and experience. It's a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certific...

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Introduction to Data Science
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Interested in learning more about data science, but don't know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field. This Specialization will introduce you to what data science is and what data scientists do. You'll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisi...

Data Science: Foundations using R
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Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data. The five courses in this special...

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Data Science Fundamentals with Python and SQL
coursera

Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field. The specialization consists of 4 self-paced online courses that will provide you with the foundational skills required for Data Scie...

20 Most Common Skill For A Data Science Internship

Python16.9%
R9.1%
Analytics6.5%
Data Analysis5.4%
Machine Learning Algorithms5.2%
Big Data4%
Predictive Models4%
Data Visualization3.8%

Typical Skill-Sets Required For A Data Science Internship

RankascdescSkillascdescPercentage of ResumesPercentageascdesc
1
1
Python
Python
16.9%
16.9%
2
2
R
R
9.1%
9.1%
3
3
Analytics
Analytics
6.5%
6.5%
4
4
Data Analysis
Data Analysis
5.4%
5.4%
5
5
Machine Learning Algorithms
Machine Learning Algorithms
5.2%
5.2%
6
6
Big Data
Big Data
4%
4%
7
7
Predictive Models
Predictive Models
4%
4%
8
8
Data Visualization
Data Visualization
3.8%
3.8%
9
9
Java
Java
3.1%
3.1%
10
10
Hadoop
Hadoop
3%
3%
11
11
C++
C++
2.8%
2.8%
12
12
Analyze Data
Analyze Data
2.7%
2.7%
13
13
SAS
SAS
2.4%
2.4%
14
14
Powerpoint
Powerpoint
2%
2%
15
15
Tensorflow
Tensorflow
1.9%
1.9%
16
16
Data Collection
Data Collection
1.8%
1.8%
17
17
Matlab
Matlab
1.7%
1.7%
18
18
AWS
AWS
1.3%
1.3%
19
19
BI
BI
1.3%
1.3%
20
20
Pandas
Pandas
1.2%
1.2%
21
21
Logistic Regression
Logistic Regression
1.2%
1.2%
22
22
Github
Github
1.1%
1.1%
23
23
Javascript
Javascript
1.1%
1.1%
24
24
Customer Service
Customer Service
1%
1%
25
25
Numpy
Numpy
1%
1%
26
26
A/B
A/B
0.9%
0.9%
27
27
Exploratory Analysis
Exploratory Analysis
0.9%
0.9%
28
28
SQL
SQL
0.9%
0.9%
29
29
Linux
Linux
0.8%
0.8%
30
30
Facebook
Facebook
0.8%
0.8%
31
31
NLP
NLP
0.7%
0.7%
32
32
Data Management
Data Management
0.7%
0.7%
33
33
Data Quality
Data Quality
0.7%
0.7%
34
34
Data Processing
Data Processing
0.7%
0.7%
35
35
PHP
PHP
0.7%
0.7%
36
36
ETL
ETL
0.6%
0.6%
37
37
Pivot Tables
Pivot Tables
0.6%
0.6%
38
38
Research Projects
Research Projects
0.5%
0.5%
39
39
Twitter
Twitter
0.5%
0.5%
40
40
Spss
Spss
0.5%
0.5%
41
41
Html
Html
0.5%
0.5%
42
42
VBA
VBA
0.4%
0.4%
43
43
API
API
0.4%
0.4%
44
44
Large Amounts
Large Amounts
0.4%
0.4%
45
45
Matplotlib
Matplotlib
0.4%
0.4%
46
46
GIS
GIS
0.4%
0.4%
47
47
CSS
CSS
0.4%
0.4%
48
48
Sharepoint
Sharepoint
0.4%
0.4%
49
49
SVM
SVM
0.3%
0.3%
50
50
Data Warehouse
Data Warehouse
0.3%
0.3%

90,618 Data Science Internship Jobs

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