Post Job

How to find a job with Data Analytics skills

How is Data Analytics used?

Zippia reviewed thousands of resumes to understand how data analytics is used in different jobs. Explore the list of common job responsibilities related to data analytics below:

  • Examined benefits of data analytics for charitable organizations to improve operations.
  • Performed data analytics on confidential files.
  • Collaborated on data analytics initiatives and on the preparation of Risk and Control Matrices during the planning stages of projects.
  • Created and debugged custom scripts to perform audits requiring data analytics.
  • Developed project charter and researched vendors for data analytics solution for organization's community medical department.
  • Develop actionable, insightful data analytics and reporting for internal BCA customers with business performance, including root cause analysis.

Are Data Analytics skills in demand?

Yes, data analytics skills are in demand today. Currently, 23,079 job openings list data analytics skills as a requirement. The job descriptions that most frequently include data analytics skills are internal audit internship, analytics consultant, and director of analytics.

How hard is it to learn Data Analytics?

Based on the average complexity level of the jobs that use data analytics the most: internal audit internship, analytics consultant, and director of analytics. The complexity level of these jobs is challenging.

On This Page

What jobs can you get with Data Analytics skills?

You can get a job as a internal audit internship, analytics consultant, and director of analytics with data analytics skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with data analytics skills.

Internal Audit Internship

Job description:

An internal audit intern is responsible for performing administrative and clerical duties as needed for the financial department under the supervision of tenured staff or a direct manager. Internal audit interns shadow staff on analyzing and preparing documents, familiarizing office operations, writing financial reports, verifying account statements, and escalating financial discrepancies to management for immediate resolution. An internal audit intern must be detail-oriented, as well as possess excellent analytical and organizational skills to handle tasks efficiently and spot audit inconsistencies.

  • Internal Audit
  • Data Analytics
  • SOX
  • Patients
  • Sarbanes-Oxley
  • Risk Management

Analytics Consultant

Job description:

An analytics consultant is an individual who develops new and innovative analytical solutions that meet the requirements of clients and businesses. Analytics consultants must understand the organization's business strategy by creating analytical solutions and providing analysis on the effectiveness of its marketing campaigns. They must respond to the request of market intelligence executives and provide attractive business solutions. Analytics consultants must also guide properly the industry analysts regarding market share information as well as competition in the local market.

  • Data Analytics
  • Visualization
  • SAS
  • Python
  • Data Analysis
  • Econometrics

Director Of Analytics

Job description:

A director of analytics is an individual who leads the data analytics and data warehousing departments, as well as overseeing all activities and ensuring alignment with the organization's vision and objectives. Directors of analytics are responsible for promoting data-driven decision making and investment planning across the organization. By leading the data analytics and warehousing teams, directors of analytics must build a profile of data and analytics by providing clear data and analytics stories. They also ensure constant improvement in the professional skills of key personnel by guiding them to execute their duties.

  • Data Analytics
  • Python
  • Tableau
  • Visualization
  • Digital Marketing
  • Healthcare

Analytical Manager

Job description:

An analytical manager is an individual who is responsible for providing data or statistical analysis to understand the business objectives of a company. Analytical managers must be technical experts to guide the company regarding Google Analytics, media traffic, and conversion tracking. They produce operational and hazard risk key performance indicators that will provide transparency of the business operations and track the progress of new business initiatives. Analytical managers must also develop financial models for innovative new services.

  • Tableau
  • Python
  • Visualization
  • Data Analytics
  • Power Bi
  • Digital Marketing

Data Science Internship

Job description:

A data science intern is a trainee who wants to gain working experience in the field of data science. Data science interns assist data scientists in collecting, analyzing, and interpret data sets to drive optimization and improvement of product development. As part of the company's trainees, data science interns are required to provide help in developing custom data models and algorithms to apply to data sets. Data science interns must also coordinate with different functional teams to implement models and monitor outcomes.

  • Python
  • Visualization
  • Data Analytics
  • Java
  • Data Visualization
  • R

Business Intelligence Manager

Job description:

Business Intelligence Managers oversee the collection and interpretation of company performance data. They manage and set performance objectives and success indicators. Upon setting these performance indicators, they would then coordinate with the individuals who will directly influence the parameters. Business Intelligence Managers are also responsible for preparing business-related reports. To do this, they have to understand what the audience needs to know. They would then collect all pertinent information and analyze the data set. Once they have the information they need, they interpret the data and provide recommendations to the requestors.

  • Tableau
  • Dashboards
  • Visualization
  • Power Bi
  • Data Analytics
  • Project Management

How much can you earn with Data Analytics skills?

You can earn up to $30,264 a year with data analytics skills if you become a internal audit internship, the highest-paying job that requires data analytics skills. Analytics consultants can earn the second-highest salary among jobs that use Python, $89,840 a year.

Job TitleAverage SalaryHourly Rate
Internal Audit Internship$30,264$15
Analytics Consultant$89,840$43
Director Of Analytics$132,520$64
Analytical Manager$119,134$57
Data Science Internship$42,841$21

Companies using Data Analytics in 2025

The top companies that look for employees with data analytics skills are U.S. Department of the Treasury, Deloitte, and Walmart. In the millions of job postings we reviewed, these companies mention data analytics skills most frequently.

Departments using Data Analytics

The departments that use data analytics the most are business development, engineering, and marketing.

DepartmentAverage Salary
Business Development$104,258
Engineering$96,106
Marketing$89,340

20 courses for Data Analytics skills

Advertising Disclosure

1. Data Analytics (Part Time)

general_assembly

Online

20 hours; 10 weeks, Part-time

Harness Excel, SQL, and Tableau to drive powerful analysis and insights. Build confidence and credibility to apply this versatile skill set to countless jobs. This course is offered in person and live online, in a remote classroom setting...

2. Intro to Data Analytics

general_assembly

Online

2 hours; 1 week, 2 hours live

Discover if data is a career fit for you Regardless of your industry or role, fluency in the language of data analytics will allow you to contribute to data driven decision making. In this free, two-hour livestream, you’ll learn to understand, analyze, and interpret data so you too can join the data conversation. We’ll cover how to ask the right questions of your data and basic analytic functionality. Then you’ll apply your new found data analytic skills to a real-world dataset, allowing you to develop recommendations based on your findings. All in real time, taught by an industry professional...

3. Introduction to Data Analytics for Business

udacity

Learn to apply data & statistics specifically for business! In this course, you’ll go from data novice to spreadsheet wizard, calculating and forecasting key financial metrics, even making detailed projections off real-life financial data from the New York Stock Exchange...

4. Accounting Data Analytics

coursera

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting. This specialization also develops learners’ skills in machine learning algorithms (using Python), including classification, regression, clustering, text analysis, time series analysis, and model optimization, as well as their ability to apply these machine learning skills to real-world problems...

5. Google Data Analytics

coursera

Prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. There are 483,000 open jobs in data analytics with a median entry-level salary of $92,000.¹\n\nData analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making.\n\nOver 8 courses, gain in-demand skills that prepare you for an entry-level job. You’ll learn from Google employees whose foundations in data analytics served as launchpads for their own careers. At under 10 hours per week, you can complete the certificate in less than 6 months.\n\nUpon completion, you can directly apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, Verizon, and of course, Google.\n\n75% of certificate graduates report a positive career outcome (e.g., new job, promotion, or raise) within six months of completion²\n\n¹Lightcast™ US Job Postings (2022: Jan. 1, 2022 - Dec. 31, 2022)\n\n²Based on program graduate survey, United States 2022...

6. Google Data Analytics

coursera

Prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. There are 483,000 open jobs in data analytics with a median entry-level salary of $92,000.¹\n\nData analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making.\n\nOver 8 courses, gain in-demand skills that prepare you for an entry-level job. You’ll learn from Google employees whose foundations in data analytics served as launchpads for their own careers. At under 10 hours per week, you can complete the certificate in less than 6 months.\n\nUpon completion, you can directly apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, Verizon, and of course, Google.\n\n75% of certificate graduates report a positive career outcome (e.g., new job, promotion, or raise) within six months of completion²\n\n¹Lightcast™ US Job Postings (2022: Jan. 1, 2022 - Dec. 31, 2022)\n\n²Based on program graduate survey, United States 2022...

7. Google Advanced Data Analytics

coursera

Get professional training designed by Google and take the next step in your career with advanced data analytics skills. There are over 144,000 open jobs in advanced data analytics and the median salary for entry-level roles is $118,000.¹\n\nAdvanced data professionals are responsible for collecting, analyzing, and interpreting extremely large amounts of data. These jobs require manipulating large data sets and using advanced analytics including machine learning, predictive modeling, and experimental design.\n\nThis certificate builds on your data analytics skills and experience to take your career to the next level. It's designed for graduates of the Google Data Analytics Certificate or people with equivalent data analytics experience. Expand your knowledge with practical, hands-on projects, featuring Jupyter Notebook, Python, and Tableau.\n\nAfter seven courses, you’ll be prepared for jobs like senior data analyst, junior data scientist, data science analyst, and more. At under 10 hours a week, the certificate program can be completed in less than six months. Upon completion, you can apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, and Verizon.\n\n75% of certificate graduates report a positive career outcome (e.g., new job, promo or raise) within six months of completion2\n\n¹Lightcast™ US Job Postings (Last 12 Months: 1/1/2022 – 12/31/2022)\n\n2Based on program graduate survey, United States 2022...

8. Google Advanced Data Analytics

coursera

Get professional training designed by Google and take the next step in your career with advanced data analytics skills. There are over 144,000 open jobs in advanced data analytics and the median salary for entry-level roles is $118,000.¹\n\nAdvanced data professionals are responsible for collecting, analyzing, and interpreting extremely large amounts of data. These jobs require manipulating large data sets and using advanced analytics including machine learning, predictive modeling, and experimental design.\n\nThis certificate builds on your data analytics skills and experience to take your career to the next level. It's designed for graduates of the Google Data Analytics Certificate or people with equivalent data analytics experience. Expand your knowledge with practical, hands-on projects, featuring Jupyter Notebook, Python, and Tableau.\n\nAfter seven courses, you’ll be prepared for jobs like senior data analyst, junior data scientist, data science analyst, and more. At under 10 hours a week, the certificate program can be completed in less than six months. Upon completion, you can apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, and Verizon.\n\n75% of certificate graduates report a positive career outcome (e.g., new job, promo or raise) within six months of completion2\n\n¹Lightcast™ US Job Postings (Last 12 Months: 1/1/2022 – 12/31/2022)\n\n2Based on program graduate survey responses, US 2022...

9. IoT Data Analytics

udemy
4.1
(89)

Welcome to IoT Data Analytics Course. This is practical course to learn IoT and Data Analytics from the beginning. Learn how to program NOdeMCU (ESP8266), collecting data and data analysis. There are billions of devices in homes, industries, cities, hospitals, cars, and thousands of other places. With the rapid increase of devices, you increasingly need solutions to connect them, and collect, store, and analyze device data. Data in its raw form is not always useful. Data need to be processed to transform into information. In this course, you will learn how to collect and analyse sensor data.  You will learn, data processing, data visualization and machine learning algorithms for predictive analytics. The following are the various topics covered in this training: Introduction to Internet of Things (IoT)Getting started with Arduino ProgrammingLearn to work with NodeMCU (ESP8266  based IoT Board)Collecting Data from sensors locallySending Sensor Data to IoT Cloud (Thingspeak)Introduction to MATLABData AnalysisData VisualizationMachine LearningYou'll get to practice the skills learned during the training, by doing more than five projects on Internet of Things (IoT) and Data analytics. Hands on ProjectsSending Light Sensor Values to IoT CloudSending Temperature and Humidity Values to IoT CloudSensor Data VisualizationEnergy savings with Anomaly Detection using Z-Score AnalysisCorrelation between Temperature and Humidity and RegressionTemperature Prediction using Polynomial RegressionWhat am I going to get from this course?Build IoT projects for sensor data collectionApply the fundamentals of machine learning and statistics to extract value from IoT dataUnderstand different business use-cases for IoT data...

10. Key Technologies in Data Analytics

coursera

This specialization aims to prepare you for a role working in data analytics. The first course is Fundamentals of Data Analysis. You’ll be introduced to core concepts and you’ll learn about the tools and skills required to conduct data analysis.\n\nThe second course, Fundamentals of Cloud Computing, will introduce you to core concepts of cloud computing and the primary deployment and service models. The hands on material offers the opportunity to review and configure a cloud account.\n\nIn Fundamentals of Data Warehousing, you will learn core concepts of data warehousing. You will learn about the primary components and architectures of data warehousing. The hands on material offers the opportunity to review and configure cloud storage options.\n\nIn Fundamentals of Big Data, you will be introduced to concepts, systems and life cycles of big data. The hands on material offers you the opportunity to load data into your cloud account...

11. Survey Data Collection and Analytics

coursera

This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.\n\nFaculty for this specialisation comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection firm Westat, founded by the National Science Foundation and the Interagency Consortium of Statistical Policy in the U.S. to educate the next generation of survey researchers, survey statisticians, and survey methodologists. In addition to this specialization we offer short courses, a summer school, certificates, master degrees as well as PhD programs...

12. Accounting Data Analytics with Python

coursera

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data). The first half of the course picks up where Introduction to Accounting Data Analytics and Visualization left off: using in an integrated development environment to automate data analytic tasks. We discuss how to manage code and share results within Jupyter Notebook, a popular development environment for data analytic software like Python and R. We then review some fundamental programming skills, such as mathematical operators, functions, conditional statements and loops using Python software. The second half of the course focuses on assembling data for machine learning purposes. We introduce students to Pandas dataframes and Numpy for structuring and manipulating data. We then analyze the data using visualizations and linear regression. Finally, we explain how to use Python for interacting with SQL data...

13. Applying Data Analytics in Finance

coursera

This course introduces an overview of financial analytics. You will learn why, when, and how to apply financial analytics in real-world situations. You will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leverages in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant...

14. Applying Data Analytics in Marketing

coursera

This course introduces students to marketing analytics through a wide range of analytical tools and approaches. We will discuss causal analysis, survey analysis using regression, textual analysis (sentiment analysis), and network analysis. This course aims to provide the foundation required to make better marketing decisions by analyzing multiple types of data related to customer satisfaction...

15. Graph Analytics for Big Data

coursera

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects...

16. Data Engineering using AWS Data Analytics

udemy
4.6
(1,720)

Data Engineering is all about building Data Pipelines to get data from multiple sources into Data Lakes or Data Warehouses and then from Data Lakes or Data Warehouses to downstream systems. As part of this course, I will walk you through how to build Data Engineering Pipelines using AWS Data Analytics Stack. It includes services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, EMR, Kinesis, and many more. Here are the high-level steps which you will follow as part of the course. Setup Development EnvironmentGetting Started with AWSStorage - All about AWS s3 (Simple Storage Service)User Level Security - Managing Users, Roles, and Policies using IAMInfrastructure - AWS EC2 (Elastic Cloud Compute)Data Ingestion using AWS Lambda FunctionsOverview of AWS Glue ComponentsSetup Spark History Server for AWS Glue JobsDeep Dive into AWS Glue CatalogExploring AWS Glue Job APIsAWS Glue Job BookmarksDevelopment Life Cycle of PysparkGetting Started with AWS EMRDeploying Spark Applications using AWS EMRStreaming Pipeline using AWS KinesisConsuming Data from AWS s3 using boto3 ingested using AWS KinesisPopulating GitHub Data to AWS DynamodbOverview of Amazon AWS AthenaAmazon AWS Athena using AWS CLIAmazon AWS Athena using Python boto3Getting Started with Amazon AWS RedshiftCopy Data from AWS s3 into AWS Redshift TablesDevelop Applications using AWS Redshift ClusterAWS Redshift Tables with Distkeys and SortkeysAWS Redshift Federated Queries and SpectrumHere are the details about what you will be learning as part of this course. We will cover most of the commonly used services with hands-on practice which are available under AWS Data Analytics. Getting Started with AWSAs part of this section, you will be going through the details related to getting started with AWS. Introduction - AWS Getting StartedCreate s3 BucketCreate AWS IAM Group and AWS IAM User to have required access on s3 Bucket and other servicesOverview of AWS IAM RolesCreate and Attach Custom AWS IAM Policy to both AWS IAM Groups as well as UsersConfigure and Validate AWS CLI to access AWS Services using AWS CLI CommandsStorage - All about AWS s3 (Simple Storage Service)AWS s3 is one of the most prominent fully managed AWS services. All IT professionals who would like to work on AWS should be familiar with it. We will get into quite a few common features related to AWS s3 in this section. Getting Started with AWS S3Setup Data Set locally to upload to AWS s3Adding AWS S3 Buckets and Managing Objects (files and folders) in AWS s3 bucketsVersion Control for AWS S3 BucketsCross-Region Replication for AWS S3 BucketsOverview of AWS S3 Storage ClassesOverview of AWS S3 GlacierManaging AWS S3 using AWS CLI CommandsManaging Objects in AWS S3 using CLI - LabUser Level Security - Managing Users, Roles, and Policies using IAMOnce you start working on AWS, you need to understand the permissions you have as a non-admin user. As part of this section, you will understand the details related to AWS IAM users, groups, roles as well as policies. Creating AWS IAM UsersLogging into AWS Management Console using AWS IAM UserValidate Programmatic Access to AWS IAM UserAWS IAM Identity-based PoliciesManaging AWS IAM GroupsManaging AWS IAM RolesOverview of Custom AWS IAM PoliciesManaging AWS IAM users, groups, roles as well as policies using AWS CLI CommandsInfrastructure - AWS EC2 (Elastic Cloud Compute) BasicsAWS EC2 Instances are nothing but virtual machines on AWS. As part of this section, we will go through some of the basics related to AWS EC2 Basics. Getting Started with AWS EC2Create AWS EC2 Key PairLaunch AWS EC2 InstanceConnecting to AWS EC2 InstanceAWS EC2 Security Groups BasicsAWS EC2 Public and Private IP AddressesAWS EC2 Life CycleAllocating and Assigning AWS Elastic IP AddressManaging AWS EC2 Using AWS CLIUpgrade or Downgrade AWS EC2 InstancesInfrastructure - AWS EC2 AdvancedIn this section, we will continue with AWS EC2 to understand how we can manage EC2 instances using AWS Commands and also how to install additional OS modules leveraging bootstrap scripts. Getting Started with AWS EC2Understanding AWS EC2 MetadataQuerying on AWS EC2 MetadataFitering on AWS EC2 MetadataUsing Bootstrapping Scripts with AWS EC2 Instances to install additional softwares on AWS EC2 instancesCreate an AWS AMI using AWS EC2 InstancesValidate AWS AMI - LabData Ingestion using Lambda FunctionsAWS Lambda functions are nothing but serverless functions. In this section, we will understand how we can develop and deploy Lambda functions using Python as a programming language. We will also see how to maintain a bookmark or checkpoint using s3. Hello World using AWS LambdaSetup Project for local development of AWS Lambda FunctionsDeploy Project to AWS Lambda consoleDevelop download functionality using requests for AWS Lambda FunctionsUsing 3rd party libraries in AWS Lambda FunctionsValidating AWS s3 access for local development of AWS Lambda FunctionsDevelop upload functionality to s3 using AWS Lambda FunctionsValidating AWS Lambda Functions using AWS Lambda ConsoleRun AWS Lambda Functions using AWS Lambda ConsoleValidating files incrementally downloaded using AWS Lambda FunctionsReading and Writing Bookmark to s3 using AWS Lambda FunctionsMaintaining Bookmark on s3 using AWS Lambda FunctionsReview the incremental upload logic developed using AWS Lambda FunctionsDeploying AWS Lambda FunctionsSchedule AWS Lambda Functions using AWS Event BridgeOverview of AWS Glue ComponentsIn this section, we will get a broad overview of all important Glue Components such as Glue Crawler, Glue Databases, Glue Tables, etc. We will also understand how to validate Glue tables using AWS Athena. AWS Glue (especially Glue Catalog) is one of the key components in the realm of AWS Data Analytics Services. Introduction - Overview of AWS Glue ComponentsCreate AWS Glue Crawler and AWS Glue Catalog Database as well as TableAnalyze Data using AWS AthenaCreating AWS S3 Bucket and Role to create AWS Glue Catalog Tables using Crawler on the s3 locationCreate and Run the AWS Glue Job to process data in AWS Glue Catalog TablesValidate using AWS Glue Catalog Table and by running queries using AWS AthenaCreate and Run AWS Glue TriggerCreate AWS Glue WorkflowRun AWS Glue Workflow and ValidateSetup Spark History Server for AWS Glue JobsAWS Glue uses Apache Spark under the hood to process the data. It is important we setup Spark History Server for AWS Glue Jobs to troubleshoot any issues. Introduction - Spark History Server for AWS GlueSetup Spark History Server on AWSClone AWS Glue Samples repositoryBuild AWS Glue Spark UI ContainerUpdate AWS IAM Policy PermissionsStart AWS Glue Spark UI ContainerDeep Dive into AWS Glue CatalogAWS Glue has several components, but the most important ones are nothing but AWS Glue Crawlers, Databases as well as Catalog Tables. In this section, we will go through some of the most important and commonly used features of the AWS Glue Catalog. Prerequisites for AWS Glue Catalog TablesSteps for Creating AWS Glue Catalog TablesDownload Data Set to use to create AWS Glue Catalog TablesUpload data to s3 to crawl using AWS Glue Crawler to create required AWS Glue Catalog TablesCreate AWS Glue Catalog Database - itvghlandingdbCreate AWS Glue Catalog Table - ghactivityRunning Queries using AWS Athena - ghactivityCrawling Multiple Folders using AWS Glue CrawlersManaging AWS Glue Catalog using AWS CLIManaging AWS Glue Catalog using Python Boto3Exploring AWS Glue Job APIsOnce we deploy AWS Glue jobs, we can manage them using AWS Glue Job APIs. In this section we will get overview of AWS Glue Job APIs to run and manage the jobs. Update AWS IAM Role for AWS Glue JobGenerate baseline AWS Glue JobRunning baseline AWS Glue JobAWS Glue Script for Partitioning DataValidating using AWS AthenaUnderstanding AWS Glue Job BookmarksAWS Glue Job Bookmarks can be leveraged to maintain the bookmarks or checkpoints for incremental loads. In this section, we will go through the details related to AWS Glue Job Bookmarks. Introduction to AWS Glue Job BookmarksCleaning up the data to run AWS Glue JobsOverview of AWS Glue CLI and CommandsRun AWS Glue Job using AWS Glue BookmarkValidate AWS Glue Bookmark using AWS CLIAdd new data to the landing zone to run AWS Glue Jobs using BookmarksRerun AWS Glue Job using BookmarkValidate AWS Glue Job Bookmark and Files for Incremental runRecrawl the AWS Glue Catalog Table using AWS CLI CommandsRun AWS Athena Queries for Data ValidationDevelopment Lifecycle for PysparkIn this section, we will focus on the development of Spark applications using Pyspark. We will use this application later while exploring EMR in detail. Setup Virtual Environment and Install PysparkGetting Started with PycharmPassing Run Time ArgumentsAccessing OS Environment VariablesGetting Started with SparkCreate Function for Spark SessionSetup Sample DataRead data from filesProcess data using Spark APIsWrite data to filesValidating Writing Data to FilesProductionizing the CodeGetting Started with AWS EMR (Elastic Map Reduce)As part of this section, we will understand how to get started with AWS EMR Cluster. We will primarily focus on AWS EMR Web Console. Elastic Map Reduce is one of the key service in AWS Data Analytics Services which provide capability to run applications which process large scale data leveraging distributed computing frameworks such as Spark. Planning for AWS EMR ClusterCreate AWS EC2 Key Pair for AWS EMR ClusterSetup AWS EMR Cluster with Apache SparkUnderstanding Summary of AWS EMR ClusterReview AWS EMR Cluster Application User InterfacesReview AWS EMR Cluster MonitoringReview AWS EMR Cluster Hardware and Cluster Scaling PolicyReview AWS EMR Cluster ConfigurationsReview AWS EMR Cluster EventsReview AWS EMR Cluster StepsReview AWS EMR Cluster Bootstrap ActionsConnecting to AWS EMR Master Node using SSHDisabling Termination Protection for AWS EMR Cluster and Terminating the AWS EMR ClusterClone and Create a New AWS EMR ClusterListing AWS S3 Buckets and Objects using AWS CLI on AWS EMR ClusterListing AWS S3 Buckets and Objects using HDFS CLI on AWS EMR ClusterManaging Files in AWS S3 using HDFS CLI on AWS EMR ClusterReview AWS Glue Catalog Databases and TablesAccessing AWS Glue Catalog Databases and Tables using AWS EMR ClusterAccessing spark-sql CLI of AWS EMR ClusterAccessing pyspark CLI of AWS EMR ClusterAccessing spark-shell CLI of AWS EMR ClusterCreate AWS EMR Cluster for NotebooksDeploying Spark Applications using AWS EMRAs part of this section, we will understand how we typically deploy Spark Applications using AWS EMR. We will be using the Spark Application we deployed earlier. Deploying Applications using AWS EMR - IntroductionSetup AWS EMR Cluster to deploy applicationsValidate SSH Connectivity to Master node of AWS EMR ClusterSetup Jupyter Notebook Environment on AWS EMR ClusterCreate required AWS s3 Bucket for AWS EMR ClusterUpload GHActivity Data to s3 so that we can process using Spark Application deployed on AWS EMR ClusterValidate Application using AWS EMR Compatible Versions of Python and SparkDeploy Spark Application to AWS EMR Master NodeCreate user space for ec2-user on AWS EMR ClusterRun Spark Application using spark-submit on AWS EMR Master NodeValidate Data using Jupyter Notebooks on AWS EMR ClusterClone and Start Auto Terminated AWS EMR ClusterDelete Data Populated by GHAcitivity Application using AWS EMR ClusterDifferences between Spark Client and Cluster Deployment Modes on AWS EMR ClusterRunning Spark Application using Cluster Mode on AWS EMR ClusterOverview of Adding Pyspark Application as Step to AWS EMR ClusterDeploy Spark Application to AWS S3 to run using AWS EMR StepsRunning Spark Applications as AWS EMR Steps in client modeRunning Spark Applications as AWS EMR Steps in cluster modeValidate AWS EMR Step Execution of Spark ApplicationStreaming Data Ingestion Pipeline using AWS KinesisAs part of this section, we will go through details related to the streaming data ingestion pipeline using AWS Kinesis which is a streaming service of AWS Data Analytics Services. We will use AWS Kinesis Firehose Agent and AWS Kinesis Delivery Stream to read the data from log files and ingest it into AWS s3. Building Streaming Pipeline using AWS Kinesis Firehose Agent and Delivery StreamRotating Logs so that the files are created frequently which will be eventually ingested using AWS Kinesis Firehose Agent and AWS Kinesis Firehose Delivery StreamSet up AWS Kinesis Firehose Agent to get data from logs into AWS Kinesis Delivery Stream. Create AWS Kinesis Firehose Delivery StreamPlanning the Pipeline to ingest data into s3 using AWS Kinesis Delivery StreamCreate AWS IAM Group and User for Streaming Pipelines using AWS Kinesis ComponentsGranting Permissions to AWS IAM User using Policy for Streaming Pipelines using AWS Kinesis ComponentsConfigure AWS Kinesis Firehose Agent to read the data from log files and ingest it into AWS Kinesis Firehose Delivery Stream. Start and Validate AWS Kinesis Firehose AgentConclusion - Building Simple Steaming Pipeline using AWS Kinesis FirehoseConsuming Data from AWS s3 using Python boto3 ingested using AWS KinesisAs data is ingested into AWS S3, we will understand how data can ingested in AWS s3 can be processed using boto3. Customizing AWS s3 folder using AWS Kinesis Delivery StreamCreate AWS IAM Policy to read from AWS s3 BucketValidate AWS s3 access using AWS CLISetup Python Virtual Environment to explore boto3Validating access to AWS s3 using Python boto3Read Content from AWS s3 objectRead multiple AWS s3 ObjectsGet the number of AWS s3 Objects using MarkerGet the size of AWS s3 Objects using MarkerPopulating GitHub Data to AWS DynamodbAs part of this section, we will understand how we can populate data to AWS Dynamodb tables using Python as a programming language. Install required libraries to get GitHub Data to AWS Dynamodb tables. Understanding GitHub APIsSetting up GitHub API TokenUnderstanding GitHub Rate LimitCreate New Repository for sinceExtracting Required Information using PythonProcessing Data using PythonGrant Permissions to create AWS dynamodb tables using boto3Create AWS Dynamodb TablesAWS Dynamodb CRUD OperationsPopulate AWS Dynamodb TableAWS Dynamodb Batch OperationsOverview of Amazon AWS AthenaAs part of this section, we will understand how to get started with AWS Athena using AWS Web console. We will also focus on basic DDL and DML or CRUD Operations using AWS Athena Query Editor. Getting Started with Amazon AWS AthenaQuick Recap of AWS Glue Catalog Databases and TablesAccess AWS Glue Catalog Databases and Tables using AWS Athena Query EditorCreate a Database and Table using AWS AthenaPopulate Data into Table using AWS AthenaUsing CTAS to create tables using AWS AthenaOverview of Amazon AWS Athena ArchitectureAmazon AWS Athena Resources and relationship with HiveCreate a Partitioned Table using AWS AthenaDevelop Query for Partitioned ColumnInsert into Partitioned Tables using AWS AthenaValidate Data Partitioning using AWS AthenaDrop AWS Athena Tables and Delete Data FilesDrop Partitioned Table using AWS AthenaData Partitioning in AWS Athena using CTASAmazon AWS Athena using AWS CLIAs part of this section, we will understand how to interact with AWS Athena using AWS CLI Commands. Amazon AWS Athena using AWS CLI - IntroductionGet help and list AWS Athena databases using AWS CLIManaging AWS Athena Workgroups using AWS CLIRun AWS Athena Queries using AWS CLIGet AWS Athena Table Metadata using AWS CLIRun AWS Athena Queries with a custom location using AWS CLIDrop AWS Athena table using AWS CLIRun CTAS under AWS Athena using AWS CLIAmazon AWS Athena using Python boto3As part of this section, we will understand how to interact with AWS Athena using Python boto3. Amazon AWS Athena using Python boto3 - IntroductionGetting Started with Managing AWS Athena using Python boto3List Amazon AWS Athena Databases using Python boto3List Amazon AWS Athena Tables using Python boto3Run Amazon AWS Athena Queries with boto3Review AWS Athena Query Results using boto3Persist Amazon AWS Athena Query Results in Custom Location using boto3Processing AWS Athena Query Results using PandasRun CTAS against Amazon AWS Athena using Python boto3Getting Started with Amazon AWS RedshiftAs part of this section, we will understand how to get started with AWS Redshift using AWS Web console. We will also focus on basic DDL and DML or CRUD Operations using AWS Redshift Query Editor. Getting Started with Amazon AWS Redshift - IntroductionCreate AWS Redshift Cluster using Free TrialConnecting to Database using AWS Redshift Query EditorGet a list of tables querying information schemaRun Queries against AWS Redshift Tables using Query EditorCreate AWS Redshift Table using Primary KeyInsert Data into AWS Redshift TablesUpdate Data in AWS Redshift TablesDelete data from AWS Redshift tablesRedshift Saved Queries using Query EditorDeleting AWS Redshift ClusterRestore AWS Redshift Cluster from SnapshotCopy Data from s3 into AWS Redshift TablesAs part of this section, we will go through the details about copying data from s3 into AWS Redshift tables using the AWS Redshift Copy command. Copy Data from s3 to AWS Redshift - IntroductionSetup Data in s3 for AWS Redshift CopyCopy Database and Table for AWS Redshift Copy CommandCreate IAM User with full access on s3 for AWS Redshift CopyRun Copy Command to copy data from s3 to AWS Redshift TableTroubleshoot Errors related to AWS Redshift Copy CommandRun Copy Command to copy from s3 to AWS Redshift tableValidate using queries against AWS Redshift TableOverview of AWS Redshift Copy CommandCreate IAM Role for AWS Redshift to access s3Copy Data from s3 to AWS Redshift table using IAM RoleSetup JSON Dataset in s3 for AWS Redshift Copy CommandCopy JSON Data from s3 to AWS Redshift table using IAM RoleDevelop Applications using AWS Redshift ClusterAs part of this section, we will understand how to develop applications against databases and tables created as part of AWS Redshift Cluster. Develop application using AWS Redshift Cluster - IntroductionAllocate Elastic Ip for AWS Redshift ClusterEnable Public Accessibility for AWS Redshift ClusterUpdate Inbound Rules in Security Group to access AWS Redshift ClusterCreate Database and User in AWS Redshift ClusterConnect to the database in AWS Redshift using psqlChange Owner on AWS Redshift TablesDownload AWS Redshift JDBC Jar fileConnect to AWS Redshift Databases using IDEs such as SQL WorkbenchSetup Python Virtual Environment for AWS RedshiftRun Simple Query against AWS Redshift Database Table using PythonTruncate AWS Redshift Table using PythonCreate IAM User to copy from s3 to AWS Redshift TablesValidate Access of IAM User using Boto3Run AWS Redshift Copy Command using PythonAWS Redshift Tables with Distkeys and SortkeysAs part of this section, we will go through AWS Redshift-specific features such as distribution keys and sort keys to create AWS Redshift tables. AWS Redshift Tables with Distkeys and Sortkeys - IntroductionQuick Review of AWS Redshift ArchitectureCreate multi-node AWS Redshift ClusterConnect to AWS Redshift Cluster using Query EditorCreate AWS Redshift DatabaseCreate AWS Redshift Database UserCreate AWS Redshift Database SchemaDefault Distribution Style of AWS Redshift TableGrant Select Permissions on Catalog to AWS Redshift Database UserUpdate Search Path to query AWS Redshift system tablesValidate AWS Redshift table with DISTSTYLE AUTOCreate AWS Redshift Cluster from Snapshot to the original stateOverview of Node Slices in AWS Redshift ClusterOverview of Distribution Styles related to AWS Redshift tablesDistribution Strategies for retail tables in AWS Redshift DatabasesCreate AWS Redshift tables with distribution style allTroubleshoot and Fix Load or Copy ErrorsCreate AWS Redshift Table with Distribution Style AutoCreate AWS Redshift Tables using Distribution Style KeyDelete AWS Redshift Cluster with a manual snapshotAWS Redshift Federated Queries and SpectrumAs part of this section, we will go through some of the advanced features of Redshift such as AWS Redshift Federated Queries and AWS Redshift Spectrum. AWS Redshift Federated Queries and Spectrum - IntroductionOverview of integrating AWS RDS and AWS Redshift for Federated QueriesCreate IAM Role for AWS Redshift ClusterSetup Postgres Database Server for AWS Redshift Federated QueriesCreate tables in Postgres Database for AWS Redshift Federated QueriesCreating Secret using Secrets Manager for Postgres DatabaseAccessing Secret Details using Python Boto3Reading Json Data to Dataframe using PandasWrite JSON Data to AWS Redshift Database Tables using PandasCreate AWS IAM Policy for Secret and associate with Redshift RoleCreate AWS Redshift Cluster using AWS IAM Role with permissions on secretCreate AWS Redshift External Schema to Postgres DatabaseUpdate AWS Redshift Cluster Network Settings for Federated QueriesPerforming ETL using AWS Redshift Federated QueriesClean up resources added for AWS Redshift Federated QueriesGrant Access on AWS Glue Data Catalog to AWS Redshift Cluster for SpectrumSetup AWS Redshift Clusters to run queries using SpectrumQuick Recap of AWS Glue Catalog Database and Tables for AWS Redshift SpectrumCreate External Schema using AWS Redshift SpectrumRun Queries using AWS Redshift SpectrumCleanup the AWS Redshift Cluster...

17. SQL for Data Analytics

udemy
4.5
(173)

Welcome to SQL for Data Analytics! There are literally thousands of SQL courses online, but most of them don't prepare you for using it in the real world. In this course, instead of just learning about the basic fundamentals of SQL, I will teach you how to use SQL and data to develop your Data Analytics mindset. Together, we will learn how to apply SQL and Data Analysis to real-life business use cases, to draw insights from data in order to make data-informed decisions. We will also learn about the advanced concept in SQL like Self-join and Window Analytics Functions to solve complex business problems. We even go further to present and communicate that insight effectively with interactive visualization dashboard. This course requires absolutely no programming experience. By the end of this course, you will be well equipped to pass all of the SQL interviews questions in the data world: whether you are looking for a job as a data scientist, data analyst, or data engineer. Feel free to look through the preview videos of this course to see if it's a good fit for you! Remember I offer a 30-day money-back guarantee, so you can join me for an entire month, risk-free, and decide whether to keep going or not. I'll see you inside the course! Best, Tuan Vu...

18. Health Information Literacy for Data Analytics

coursera

This Specialization is intended for data and technology professionals with no previous healthcare experience who are seeking an industry change to work with healthcare data. Through four courses, you will identify the types, sources, and challenges of healthcare data along with methods for selecting and preparing data for analysis. You will examine the range of healthcare data sources and compare terminology, including administrative, clinical, insurance claims, patient-reported and external data. You will complete a series of hands-on assignments to model data and to evaluate questions of efficiency and effectiveness in healthcare. This Specialization will prepare you to be able to transform raw healthcare data into actionable information...

19. Python Data Products for Predictive Analytics

coursera

Python data products are powering the AI revolution. Top companies like Google, Facebook, and Netflix use predictive analytics to improve the products and services we use every day. Take your Python skills to the next level and learn to make accurate predictions with data-driven systems and deploy machine learning models with this four-course Specialization from UC San Diego.\n\nThis Specialization is for learners who are proficient with the basics of Python. You’ll start by creating your first data strategy. You’ll also develop statistical models, devise data-driven workflows, and learn to make meaningful predictions for a wide-range of business and research purposes. Finally, you’ll use design thinking methodology and data science techniques to extract insights from a wide range of data sources. This is your chance to master one of the technology industry’s most in-demand skills.\n\nPython Data Products for Predictive Analytics is taught by Professor Ilkay Altintas, Ph.D. and Julian McAuley. Dr. Alintas is a prominent figure in the data science community and the designer of the highly-popular Big Data Specialization on Coursera. She has helped educate hundreds of thousands of learners on how to unlock value from massive datasets...

20. Introduction to Data Analytics for Business

coursera

This course will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish. What you learn in this course will give you a strong foundation in all the areas that support analytics and will help you to better position yourself for success within your organization. You’ll develop skills and a perspective that will make you more productive faster and allow you to become a valuable asset to your organization. This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization...