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Top Data Analyst Internship Skills

Below we've compiled a list of the most important skills for a data analyst internship. We ranked the top skills based on the percentage of data analyst internship resumes they appeared on. For example, 17.2% of data analyst internship resumes contained data analysis as a skill. Let's find out what skills a data analyst internship actually needs in order to be successful in the workplace.

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

1. Data Analysis

high Demand

Here's how Data Analysis is used in Data Analyst Internship jobs:
  • Sequence data analysis using Python to identify and characterize allele specific expression in poultry infected with avian influenza from RNA-seq data.
  • Cleaned Data and performed basic data analysis using SAS Performed the linear regression and time-series analysis to evaluate the proposed capital expenditures
  • Implemented statistical data analysis Big Data analysis of CEDS emission output data to ensure the data correctness and integrity.
  • Developed a data analysis algorithm to approximate merchant's actual location from credit card and debit card transaction database.
  • Directed for all aspects of application configuration, implementations, integration, data analysis, and validation testing.
  • Completed data analysis and data mining of past information to recommend improvements to current and future business processes.
  • Participated in extensive data manipulation, cleansing and processing complex data analysis in support of management.
  • Performed data analysis using mathematical decision making, conceptual thinking, and problem solving skills.
  • Redesigned and implemented procedures for consolidating large data, improving efficiency when generating new reports.
  • Performed highly anticipated results from data analysis of suicide and criminal offenders in North Carolina.
  • Participate in ongoing decisions concerning data collections, study design, methodology and data analysis.
  • Performed data analysis & mining on BestRecom's web user-profile/demography and browser history data.
  • Formulated company bidding strategy through data analysis of 10 federal opportunities on GovWin website.
  • Performed inventory data analysis in Excel to identify potential buyers in business acquisition project.
  • Provided data extraction and information mining support of prior information systems for data analysis.
  • Assisted in completing data analysis, data display and formulating data analysis report.
  • Assisted investigating physicians and statisticians with data analysis, contributing to medical research.
  • Performed statistical data analysis for personal and business insurance lines using SAS.
  • Maintained and developed dashboards, performed data analysis and maintained Access databases.
  • Collected large data, consolidated and compiled data from diversified channel.

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2. 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 Analyst Internship jobs:
  • Worked in Agile/Scrum environment and developed Python scripts for automating web interactions and database validations.
  • Presented final intern project involving Python web scraping and visualizations of basketball shot data.
  • Increased forecast accuracy by finding natural product groupings using Python and Scikit-Learn.
  • Developed an inventory analysis web application in Python to generate graphical reports.
  • Visualize family financial data by Python.
  • Circumvented a potential negative economic profit after I deduced it using Python to analyze company's price points and revenue stream.
  • Design a self-correcting and self-updating mechanism of Hive WUM and IUM tables, and implemented it using Python and Hive queries.
  • Researched, analyzed, organized and digitized all kinds of real estate leasing information using Excel, Python and SQL.
  • Used Python to crawl investor data and automate manual processes which reduced redundancy and increased efficiency by 50%.
  • Created and developed analysis program using Java, C++, and Python to analyze research results from scholarly articles.
  • Used text mining techniques in Python (TFIDF, NLTK) to categorize data and finding high frequency words.
  • Used Tableau for visualization of data and Python to ETL data from various sources like JIRA, GitHub etc.
  • Created a new database with Microsoft Access and optimizing production system with applying data mining techniques with Python.
  • Integrated data of all the HK stocks fund using Python, including size, price, and growth.
  • Used Python to develop scripts that can clean data and display the data in more sensible way.
  • Implemented Map-Reduce jobs in Python for processing and cleaning thousands of log files from several log servers.
  • Connected Hive with Python, get statistical summaries through data visualization in preparation for modeling process.
  • Used Python and SQL to manipulate, analyze, extract and accumulate data from different databases.
  • Gather, format, and analyze data for marketing & sales teams using MySQL and Python.
  • Developed scraping scripts using the BeautifulSoup library in Python to extract synonyms and related word data.

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3. Analytics

high Demand

Here's how Analytics is used in Data Analyst Internship jobs:
  • Translated complex analytics into clear concise actionable communication and influenced product team through the presentation of data-based recommendations.
  • Used data analytics in operations to improve efficiency and service delivery based on business metrics.
  • Developed ranking mechanisms to rank the answer based on behavioral analytics and streamlined data.
  • Worked on detecting fraudulent transactions and pricing analytics by studying customer behavior & patterns.
  • Collaborated with analytics team to inspect international settlements and foreign exchange transactions.
  • Developed Tableau based analytics platform to analyze the arrival data for maintenance.
  • Marketed products and conducted analytics tracking using social advertising platforms.
  • Performed data analytics project to enable data-driven decisions and insights.
  • Assist multiple business divisions achieve growth by providing data analytics.
  • Provide quality assurance for the Custom Research and Analytics Department
  • Performed diagnostic and predictive analytics under machine learning.
  • Worked directly with the CTO to analyze user behaviors, trends, key insights and growth opportunities using Google Analytics.
  • Used R and MySQL to clean and standardize raw business data, initiated the data analytics attempt of the organization.
  • Advance Kinetic Social's analytics platform, implement mining techniques, communicate findings in final report, and create dashboards.
  • Developed meaningful data analytics and a strategy in cost savings for the client's trucking fleets based on GPS locations.
  • Automated weekly marketing performance analysis by monitoring marketing campaign, tracking Google AdWords, and utilizing Google Analytics with Excel.
  • Used BI tools such as QlikSense and Tableau for better graphical representation of data as well as for analytics.
  • Tracked and reported key performance metrics, traffic behaviors and campaign performance of web sites using Google Analytics.
  • Researched and reported on different social media data providers like DataSift to use in future projects and analytics.
  • Assisted data architects and Enterprise Analytics in the areas of inventory, site activity tagging and product.

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4. 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 Analyst Internship jobs:
  • Created meaningful visualizations/reports of department data in R and Excel for executives.
  • Developed statistical and time-series models using R software with graphical interfaces.
  • Cleaned real estate industrial data on both commercial and residential properties using Excel and R for database.
  • Used R to mine large data sets and create predictive models to forecast lead generation on dealership websites
  • Designed forecasting models developing evaluation metrics in R for material requirement and dross control in Coke Oven.
  • Analyzed data in 3 Harvard Business School Case Studies to address concerns, using R Programming.
  • Visualized data on the map and displayed dynamic changes using R ggplot2, lattice and animation package
  • Analyzed the daily real estate data with Excel and R, generated daily and monthly reports.
  • Carried on data drilling and customer behavior analysis with R based on an established CSV file.
  • Build new batch product with focus on five-way technique of data visualization using R and Tableau.
  • Used R and Microsoft Excel for box plotting, descriptive statistics and other statistical inferences.
  • Used R language for the general analysis of the company's customer preference portfolio.
  • Used Excel, R and SAS to analyze model fitting and improved model performance.
  • Analyzed customer web behavior using Google Analytic, SAS, and R etc.
  • Developed scripts in R to collect historical weather and crime data in Memphis.
  • Executed forecasting and regression models on residual value data using R and SAS.
  • Cooperated with IT team to integrate R code into a user-friendly Web App
  • Analyzed 20 GB CMMB volunteer information using R language and Excel.
  • Evaluated Tableau and R to analyze student data to make graphs.
  • Used both R and SQL to evaluate and model the data.

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5. Data Extraction

high Demand

Here's how Data Extraction is used in Data Analyst Internship jobs:
  • Performed numerous data extraction and data integration requests involving SQL scripts.
  • Performed data extraction of company s supply chain inventory resulting in the sale of approximately 13 million in unused equipment.
  • Processed the data cleaning, entry to data warehouse, data extraction and manipulation using SQL.
  • Work with World Class Manufacturing pillars to provide resource support with data extraction, entry and analysis
  • Created reports for stakeholders, performed data extractions and data presentations and automated the weekly reports.
  • Created the SSIS package to handle data extraction, transformation and loading.
  • Implemented ETL process for data extraction & integration from flat files, CSVs, SAP DB, spread sheets etc.
  • Completed document translation, metadata extraction, and geospatial input of data through a variety of media.
  • Formulated procedures for data extraction, transformation and integration of Healthcare Data using SQL Server.
  • Analyzed the extracted semantic metadata and give feedback to the data extraction engine developement team.
  • Extract data from existing data stores and performing ad-hoc queries.
  • Used Hadoop HDFS for data extraction and created ETL workflows to transform big data.

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6. 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 Analyst Internship jobs:
  • Utilized SAP and other business applications to validate and analyze data between various retail systems for data integrity
  • Retrieve useful information, analyze data and visualize data.
  • Created a program to help clients analyze data faster in a more automated process using VBA in Microsoft's Excel program.
  • Participated into group discussions and used SAS and SPSS to analyze data and generate statistics reports and charts as requested.
  • Used SAS to analyze data from the Behavioral Risk Factor Surveillance System (BRFSS) on dementia treatment receiving condition.
  • Analyze data with Excel, and two other software and databases provided by the company with several statistical methods.
  • Analyze data with standard statistical methods, interpreting the results, and providing a written summary of data analyses.
  • Gathered data from the organization's web content and used quantitative and qualitative research methods to analyze data.
  • Process and analyze data in accordance with client requests using in-house software and techniques for high-volume data 2.
  • Create custom reports and dashboards, which help clients to access and analyze data in a better way.
  • Partnered with executive management to analyze data, create processes, and implement solutions in field inspection methods.
  • Use of VBA programming to automate reports which analyze data, creates graphs and extracts data for analysis.
  • Simulated various VBA function via code creation and leveraged macros to store, organize, and analyze data.
  • Analyze data from multiple systems to identify and report on possible opportunities and ensure data integrity.
  • Collect and analyze data of focus industries including IT, Logistics, Aviation and Manufacturing.
  • Research on domestic and foreign corporations and securities, using different procedures to analyze data.
  • Retrieve and analyze data using SQL, Excel, and other data management systems
  • Document business processes and analyze data to align with changing business need.
  • Analyze data and produce reports of various statistics to present to management.
  • Analyze data and classify corporate entities accordingly for use in enterprise software.

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7. Data Visualization

high Demand

Here's how Data Visualization is used in Data Analyst Internship jobs:
  • Used Data Visualization best practices to produce various highly interactive dashboards within Tableau.
  • Mastered data visualization with Tableau and gave English presentations for non-technical audience.
  • Performed Data Visualization in Tableau and created dashboards.
  • Designed data visualization for market analysis report.
  • Created weekly reports by mapping production data to compare supply and demand via data visualization charts in MS Excel.
  • Generated reports, charts, tables, listings and graphs using SAS Enterprise Miner for data visualization.
  • Initiated use of Tableau as supplement to Google Data Studio to create client-facing reports and data visualization.
  • Used Excel to make Pivot Tables and Charts for data visualization and wrote semi-annual report for H12016.
  • Developed presentations, documentation using data visualization from WEKA & Tableau to show insights to customers.
  • Simplified and classified clients' history data for data visualization purpose.
  • Utilized Microsoft Excel, Radian6, and Googlewhacker to develop integrative data visualizations with info graphics.
  • Provided data visualizations using Tableau to business users based on ad-hoc and regulatory reporting requests
  • Conducted interviews, coded qualitative data, analyzed qualitative and quantitativedata, created data visualization
  • Strategized budget allocation of $550,000 for salary acceleration program through data visualization insights.
  • Communicated insights efficiently and effectively using data visualization with Rstudio and ggplot2.
  • Developed links between SAS, Excel, and Microsoft Visio in order to optimize the efficiency of data visualization.
  • Displayed current talent structure and analysis results with the leverage of data visualization tool such Tableau and Excel.
  • Implemented decision tree to find significant attributes and used Tableau, D3.js for data visualization.
  • Worked with the data visualization team to evaluate and analyze highly complex datasets.
  • Conducted thorough research on data visualization tools and recommended Klipfolio for its functionality and value for money.

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8. Data Collection

high 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 Analyst Internship jobs:
  • Developed and implemented data collection systems and other strategies that optimize statistical efficiency and data quality using SAS and Excel.
  • Determine and recommend additional data collection and data requirements.
  • Developed and maintained SOP, data collection and analysis
  • Maintain data collection process with advance technological equipment.
  • Optimized data collection procedures and generated reports.
  • Performed data collection and reporting of key elements for quality restaurants in Chicago through Yelp and social media outlets.
  • Oversee the data collection, analysis, and reporting Energy Management System of the overall campus.
  • Follow the guidelines of the data collection manual and guidelines developed for each school/teacher or official.
  • Acquired data from organization data collection system and managed the database at Fort Worth Pregnancy Center.
  • Collaborated with members of the SME's for process improvement of data collection & methodology.
  • Assist graduate students with their research paper through data collection and statistical analysis.
  • Participated in clinical data collection, validation, analysis, and product support.
  • Assisted with data collection, data entry and analysis of health screening data.
  • Develop and implement data analyses, data collection systems, and other strategies.
  • Optimized one step of data collection and improved efficiency by 15%.
  • Designed data collection programs for specific retailers tailored to client needs.
  • Interacted with data collection companies to ensure proper collection.
  • Enabled fast-track data collection and translation into real statistics by developing data collection workflow.
  • Developed an interface between graphic application software and real time data collection devices
  • Reviewed procedures and worked with development team to create analytical tools in streamline the data collection and quality assurance processes.

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9. BI

high Demand

Here's how BI is used in Data Analyst Internship jobs:
  • Revamped BI governance process by assisting teams to develop a KPI dashboard to track and report performance on a real-time basis.
  • Prepared Trends & Patterns analysis reports from various data sources like ERP, BI, Databases and Market Research.
  • Gained hands-on experience working with Power BI Dashboard Services by digging valuable insights from unstructured raw data of customers.
  • Designed highly insightful and informative dashboards with the help of Tableau, SSRS and Oracle BI tools.
  • Developed several Top Tier Client based reports, scorecards in Microsoft BI Reporting Environment (SSRS).
  • Performed end-to-end Q&A test on Business Intelligence Tool and Mobile BI Application.
  • Aided supply chain visibility and forecast accuracy using T-SQL and Power BI dashboards.
  • Created sustainable and advanced visualizations using Microsoft Power BI to help audit planning and riskassessment processes.
  • Explored the reason of profit decline by visualizing big data and building micro BI with EXCEL & R; 3.

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10. 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 Analyst Internship jobs:
  • Collaborated with statistics department to develop algorithms with SAS, and participated team meeting to analyze clinical trial protocol.
  • Managed various data projects implemented in SAS that investigated long-term statistical trends existing within charter network.
  • Developed statistical model with SAS and excel; communicated with company representative and group members.
  • Developed SAS macros to implement automated data loading and statistic reporting schemes.
  • Created and edited SAS programming template for population analysis.
  • Performed data validation and manipulation by SAS.
  • Appended energy field data to the Census Summary File and displayed the data as map format using software Tableau and SAS.
  • Worked with team of analysts on identifying model inefficiencies and highlighting cost saving opportunities for improving the business efficiency using SAS.
  • Collected, processed and analyzed state test assessments received in raw form using SAS to fuel the analysis and reporting projects.
  • Proposed a new tier pricing for Arrival Plus credit card to increase the net profit in P&L using SAS.
  • Produced SAS outputs in RTF, PDF, CSV and HTML formats using Output Delivery System (ODS) facility.
  • Create and modify SAS macros for data cleaning, validation, analysis and report generation to make programming tasks easier.
  • Generated high quality reports in the form of listing, HTML, RTF and PDF formats using SAS ODS.
  • Innovated regression models in SAS to identify the relationship between total sales and given features from company data warehouse.
  • Provided developing and maintenance of SAS programs and SQL database to report to the manager on a weekly basis.
  • Performed variable selection, validation and reduction to test influential factors in mortgage-backed asset pricing model, using SAS.
  • Utilized Microsoft Excel, SAS, and SPSS in creating tables and graphs to evaluate current and historical data.
  • Worked on data manipulation, cleansing, and processing using the understanding for decision making using Excel & SAS.
  • Developed time series regression models in SAS to forecast product and service sales, expenses, and profits.
  • Converted Census Summary File 1&2 into SAS data, and analyzed their subject and geographic content.

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11. Pivot Tables

high Demand

A pivot table is a technique used in data processing to arrange and rearrange statistics to prioritize useful information. The aim of a pivot table is to summarize the findings and interpretations of the data extracted. Pivot tables take information from a database or spreadsheet to report sums, average, and other such statistics. This technique is integral to data analysis since it turns the data to view it from different lenses and perspectives.

Here's how Pivot Tables is used in Data Analyst Internship jobs:
  • Train employees on the use of pivot tables and tips and tricks in MS Excel for efficient data entry and analysis.
  • Used advanced Excel Functions to create spreadsheets & Pivot Tables with clean data to store it at multiple secure locations.
  • Worked on excel to create pivot tables, charts for sales data and assisted sales team with analysis of data
  • Analyzed data for different time intervals using Pivot Tables, Marcos, VLOOKUP in MS-Excel to observe trends.
  • Maintained campaign data with Excel; created pivot tables, charts, and graphs to explain trends.
  • Utilized Excel pivot tables to quickly identify discrepancies in a set of several hundred records of securities.
  • Trained technical staff to use pivot tables, construct correlation matrices, and perform regression analysis.
  • Created pivot tables and SSRS reports to be used by business users to make strategic decisions.
  • Analyzed mail marketing data results with pivot tables to help determine which mailers were most significant.
  • Demonstrated proficiency in Microsoft Excel by utilizing advanced formulas, pivot tables and charts.
  • Created SQL queries and converted them into pivot tables or other tables within excel.
  • Designed pivot tables and trend chart in Excel for monthly gateway volume summaries.
  • Pulled Data Sets into excel, manipulated data using pivot tables etc.
  • Created pivot tables to organize and manage present and potential client information.
  • Constructed pivot tables to develop odds ratio to evaluate potential customers.
  • Manipulated data using pivot tables, and pivot charts.
  • Managed, updated and manipulated report orientation and structures with the use of advanced Excel functions including Pivot Tables and V-Lookups.
  • Contributed to the Marc Jacobs team by writing tutorials regarding IT solutions, using v-lookup and pivot tables.
  • Analyzed data sets, performing ad-hoc analysis and data manipulation through the use of pivot tables.
  • Used advanced Excel V-lookup commands, formulas, pivot tables to produce spreadsheets and reports.

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12. Logistic Regression

high Demand

Here's how Logistic Regression is used in Data Analyst Internship jobs:
  • Performed data analyses including general and generalized linear regression, survival analysis, propensity score matching and inverse probability weighting.
  • Developed 93% accurate Prediction model for Tax collections using Logistic Regression statistical model.
  • Implemented statistical modeling techniques like decision tree and logistic regression.
  • Performed linear regression analysis, correlated and uncorrelated chi-square test.
  • Build a logistic regression model for hypothesis-driven research.
  • Analyze statistical data using linear regression and time series, and represent statistic graphics, reports and presentations.
  • Created models such as logistic regression, boosted trees classifier to predict the fraud transactions.
  • Look for confounding and effect modification in the context of logistic regression.
  • Developed a predictive model that estimates cancellation rate through logistic regression and machine learning, upgrading the calculation of customer longevity value
  • Used General Linear Regression to fit and forecast the data, predicting different test-scores boundaries and variance analyzed student performance data
  • Used logistic regression, clustering, decision tree modelling and multivariate modeling to provide valuable analytical insights on real estate property.

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13. 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 Analyst Internship jobs:
  • Loaded data from various sources into Hadoop Distributed File System (HDFS) and Hive along with performing data processing.
  • Created and managed reports using SSRS and used Hadoop as a big data tool.
  • Applied data mining algorithms for massive data sets in Hadoop File Systems.
  • Conducted MapReduce job in Big Data Hadoop framework.
  • Set up distributed computing environment using Hadoop and implemented parallel applications to speed up multi-linear regression.
  • Extracted application log data for every machine in each data center stored in Hadoop using Pig scripts for analysis and reporting.
  • Investigated trends in Big Data industry using case studies in MapReduce, Cassandra, Hadoop, MongoDB, Splunk
  • Processed data using Hadoop, Hive, HBase, BigQuery on EC2/google cloud platform.

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14. 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 Analyst Internship jobs:
  • Investigated big data tools for potential implementation within the department.
  • Identified performance trends, derived insight, proposed solutions, and implemented data and ETL solutions on Big data.
  • Automated extraction of open source financial information from big data platforms to perform data and variable construction.
  • Analyzed data and predicted the tendency by using D3, knowledge about big data and statistics.
  • Learned about linked data and bubble heap algorithm and was charged in building big data platform
  • Analyzed big data level Linux Secure log info with RegEx, and basic Shell Commands.
  • Analyzed big data using Excel and developed metrics tracking the status of various projects.
  • Sifted through Big Data storage to identify inflection points in sales data.
  • Analyze the feasibility of building Big Data center.
  • Web Programmer at Sojern Underwent training sessions on HBASE, Big Data, Pig script.

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15. Data Quality

average Demand

Here's how Data Quality is used in Data Analyst Internship jobs:
  • Identified and interpreted patterns and trends, assessed data quality and eliminated irrelevant data.
  • Assist with the prioritization and implementation of data quality improvement initiatives as needed.
  • Developed and oversaw data quality improvement projects.
  • Used SSIS to migrate data from sources servers into target servers, cleaning data and ensuring the data quality.
  • Cleanse and standardize collected data and existing data in the database to enhance data quality over time.
  • Detected frauds and predicted trends by statistical monitoring of metrics to introduce 5 new data quality checks.
  • Performed work according to the data quality and production standards as defined in policies and procedures.
  • Maintained the business database and used ETL functions to leverage data quality check and data reconciliation.
  • Enhance data quality over time on new and existing data through continual data clean-up efforts.
  • Manage data from dashboards and socialize results to key stakeholders to improve data quality.
  • Enhance data quality checks by identifying and defining metrics, thresholds, benchmarks.
  • Involved in setting up processes and standards for Data Corrections, Data Quality.
  • Used simple cluster sampling method to review data quality and ensure the efficacy.
  • Manage and improve data quality of Emergency Medical Services (EMS) database.
  • Created a standard operating procedure around Materials Master Data Quality Standards in SAP.
  • Improved data quality, leading to increased productivity and effectiveness of sales team.
  • Manage data augmentation, clean up and data quality issue resolution.
  • Perform work according to the data quality and production standards.
  • Developed a number of SQL scripts to validate data quality.
  • Cleaned up 7,000+ duplicate records to improve data quality.

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16. Pl/Sql

average Demand

PI/SQL is a powerful database programming language specifically designed and developed to encompass Structured Query Language (SQL) statements within its procedural syntax. All program units are organized and stored inside the Oracle database servers, allowing both SQL and PI/SQL languages to operate on the same server.

Here's how Pl/Sql is used in Data Analyst Internship jobs:
  • Reduced query retrieval time by automating manual tasks using stored procedures, functions, triggers and PL/SQL scripts.
  • Developed PL/SQL procedures for processing business logic in the database and use them as a Stored Procedure Transformation.
  • Used SQL Scripts, Stored Procedures, Functions, Database Triggers and Packages, PL/SQL and related technologies.
  • Build data driven reports, stored procedures, query optimization using SQL and PL/SQL knowledge.
  • Created complex procedures, functions using PL/SQL.
  • Enforced business rules using PL/SQL in Database Triggers/Stored Procedures/Functions.

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Online Courses For Data Analyst Internships

One of the best ways to acquire the skills needed to be a data analyst 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 analyst interns benefit from having skills like data analysis, python, and analytics, we found courses that will help you improve these skills.

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Complete Data Wrangling & Data Visualisation With Python
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Fundamentals of Data Analysis for Big Data
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Statistics & Data Analysis: Linear Regression Models in SPSS
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Beginner and Intermediate Data Analytic Methods for Testing Main Effects & Interactions with SPSS and the PROCESS Macro...

Data Science: Data Mining & Natural Language Processing in R
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Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples...

R Programming: Advanced Analytics In R For Data Science
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Statistics / Data Analysis: Survey Data and Likert Scales
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The Data Science Course 2021: Complete Data Science Bootcamp
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Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning...

Regression Analysis / Data Analytics in Regression
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Gain Important and Highly Marketable Skills in Regression Analysis - Tame the Regression Beast Today!...

Statistics / Data Analysis in SPSS: Inferential Statistics
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Increase Your Data Analytic Skills Highly Valued And Sought After By Employers...

Data Science with Databricks for Data Analysts
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This specialization is intended for data analysts looking to expand their toolbox for working with data. Traditionally, data analysts have used tools like relational databases, CSV files, and SQL programming, among others, to perform their daily workflows. In this specialization, you will leverage existing skills to learn new ones that will allow you to utilize advanced technologies not traditionally linked to this role - technologies like Databricks and Apache Spark. By the end of this speciali...

The Data Analyst Course: Complete Data Analyst Bootcamp 2021
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Complete Data Analyst Training: Python, NumPy, Pandas, Data Collection, Preprocessing, Data Types, Data Visualization...

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Applied Statistical Modeling for Data Analysis in R
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Complete Data Science Training with Python for Data Analysis
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Beginners python data analytics: Data science introduction: Learn data science: Python data analysis methods tutorial...

Data Science and Machine Learning Bootcamp with R
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Foundations: Data, Data, Everywhere
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This is the first course in the Google Data Analytics Certificate. These courses will equip you with the skills you need to apply to introductory-level data analyst jobs. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you'll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key...

Core Spatial Data Analysis: Introductory GIS with R and QGIS
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Become Proficient In Spatial Data Analysis Using R & QGIS By Working On A Real Project - Get A Job In Spatial Data!...

Learn SQL Basics for Data Science
coursera

This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, Delta Lake and more. These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis...

Modern Big Data Analysis with SQL
coursera

This Specialization teaches the essential skills for working with large-scale data using SQL. Maybe you are new to SQL and you want to learn the basics. Or maybe you already have some experience using SQL to query smaller-scale data with relational databases. Either way, if you are interested in gaining the skills necessary to query big data with modern distributed SQL engines, this Specialization is for you. Most courses that teach SQL focus on traditional relational databases, but today, more...

20 Most Common Skill For A Data Analyst Internship

Data Analysis17.2%
Python10%
Analytics8.8%
R5.5%
Data Extraction3.9%
Analyze Data3.4%
Data Visualization3.4%
Data Collection3.4%

Typical Skill-Sets Required For A Data Analyst Internship

RankascdescSkillascdescPercentage of ResumesPercentageascdesc
1
1
Data Analysis
Data Analysis
17.2%
17.2%
2
2
Python
Python
10%
10%
3
3
Analytics
Analytics
8.8%
8.8%
4
4
R
R
5.5%
5.5%
5
5
Data Extraction
Data Extraction
3.9%
3.9%
6
6
Analyze Data
Analyze Data
3.4%
3.4%
7
7
Data Visualization
Data Visualization
3.4%
3.4%
8
8
Data Collection
Data Collection
3.4%
3.4%
9
9
BI
BI
3%
3%
10
10
SAS
SAS
2.8%
2.8%
11
11
Pivot Tables
Pivot Tables
2.2%
2.2%
12
12
Logistic Regression
Logistic Regression
2.1%
2.1%
13
13
Hadoop
Hadoop
2%
2%
14
14
Big Data
Big Data
1.8%
1.8%
15
15
Data Quality
Data Quality
1.6%
1.6%
16
16
Pl/Sql
Pl/Sql
1.6%
1.6%
17
17
VBA
VBA
1.6%
1.6%
18
18
Data Management
Data Management
1.6%
1.6%
19
19
Java
Java
1.5%
1.5%
20
20
Financial Statements
Financial Statements
1.5%
1.5%
21
21
Spss
Spss
1.4%
1.4%
22
22
Javascript
Javascript
1.3%
1.3%
23
23
Complex Data
Complex Data
1.2%
1.2%
24
24
Identify Trends
Identify Trends
1.2%
1.2%
25
25
ETL
ETL
1.1%
1.1%
26
26
SQL
SQL
1.1%
1.1%
27
27
KPI
KPI
1%
1%
28
28
PHP
PHP
0.9%
0.9%
29
29
Matlab
Matlab
0.8%
0.8%
30
30
Twitter
Twitter
0.7%
0.7%
31
31
Facebook
Facebook
0.7%
0.7%
32
32
Sharepoint
Sharepoint
0.7%
0.7%
33
33
Quantitative Analysis
Quantitative Analysis
0.7%
0.7%
34
34
Market Research
Market Research
0.7%
0.7%
35
35
Business Process
Business Process
0.7%
0.7%
36
36
Sales Data
Sales Data
0.6%
0.6%
37
37
Large Amounts
Large Amounts
0.6%
0.6%
38
38
API
API
0.6%
0.6%
39
39
Data Warehouse
Data Warehouse
0.5%
0.5%
40
40
Large Datasets
Large Datasets
0.5%
0.5%
41
41
GIS
GIS
0.5%
0.5%
42
42
Data Manipulation
Data Manipulation
0.4%
0.4%
43
43
Data Integrity
Data Integrity
0.4%
0.4%
44
44
Stata
Stata
0.4%
0.4%
45
45
Ssis
Ssis
0.4%
0.4%
46
46
ERP
ERP
0.4%
0.4%
47
47
A/B
A/B
0.4%
0.4%
48
48
Html
Html
0.4%
0.4%
49
49
Vlookup
Vlookup
0.3%
0.3%
50
50
Client Data
Client Data
0.3%
0.3%

89,451 Data Analyst Internship Jobs

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