How to find a job with Data Processing skills

1. Processing Data with Python

Processing data is used in virtually every field these days. It is used for analyzing web traffic to determine personal preferences, gathering scientific data for biological analysis, analyzing weather patterns, business practices, and on. Data can take on many different forms and come from many different sources. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. It has libraries that can be used to parse and quickly analyze the data in whatever form it comes in, whether it be in XML, CSV, or JSON format. Data cleaning is an important aspect of processing data, particularly in the field of data science. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...

2. Serverless Data Processing with Dataflow

It is becoming harder and harder to maintain a technology stack that can keep up with the growing demands of a data-driven business. Every Big Data practitioner is familiar with the three V’s of Big Data: volume, velocity, and variety. What if there was a scale-proof technology that was designed to meet these demands?\n\nEnter Google Cloud Dataflow. Google Cloud Dataflow simplifies data processing by unifying batch & stream processing and providing a serverless experience that allows users to focus on analytics, not infrastructure. This specialization is intended for customers & partners that are looking to further their understanding of Dataflow to advance their data processing applications.\n\nThis specialization contains three courses:\n\nFoundations, which explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in\n\nDevelop Pipelines, which covers how you convert our business logic into data processing applications that can run on Dataflow\n\nOperations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability...

3. Big Data Integration and Processing

At the end of the course, you will be able to: *Retrieve data from example database and big data management systems *Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications *Identify when a big data problem needs data integration *Execute simple big data integration and processing on Hadoop and Spark platforms This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+...

4. Data-Driven Process Improvement

By the end of this course, learners are empowered to implement data-driven process improvement objectives at their organization. The course covers: the business case for IoT (Internet of Things), the strategic importance of aligning operations and performance goals, best practices for collecting data, and facilitating a process mapping activity to visualize and analyze a process’s flow of materials and information. Learners are prepared to focus efforts around business needs, evaluate what the organization should measure, discern between different types of IoT data and collect key performance indicators (KPIs) using IoT technology. Learners have the opportunity to implement process improvement objectives in a mock scenario and consider how the knowledge can be transferred to their own organizational contexts. Material includes online lectures, videos, demos, project work, readings and discussions. This course is ideal for individuals keen on developing a data-driven mindset that derives powerful insights useful for improving a company’s bottom line. It is helpful if learners have some familiarity with reading reports, gathering and using data, and interpreting visualizations. It is the first course in the Data-Driven Decision Making (DDDM) specialization. To learn more about the specialization, check out a video overview at https://www.youtube.com/watch?v=Oi4mmeSWcVc&list=PLQvThJe-IglyYljMrdqwfsDzk56ncfoLx&index=11...

5. Data Warehouse Development Process

4.3

(790)

Data is the new asset for the enterprises. And, Data Warehouse store the data for better insights and knowledge using Business Intelligence. Development of an Enterprise Data Warehouse has more challenges compared to any other software projects because of the  Challenges with data structuresThe way data is evaluated for it's qualityComplex business rules/validationsDifferent development methods (various SDLC models like Water Fall model, V model, Agile Model, Incremental model, Iterative model)Regulatory requirements for various domains like finance, telecom, insurance, Retail and IMECompliance from third party governing bodiesExtracting data for various visualization purposes In this course, we talk about the specific aspects of the Data Warehouse Development process taking real time practical situations and how to handle them better using best practices for sustainable, scalable and robust implementations...

6. Serverless Data Processing with Dataflow: Foundations

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow. Prerequisites: The Serverless Data Processing with Dataflow course series builds on the concepts covered in the Data Engineering specialization. We recommend the following prerequisite courses: (i)Building batch data pipelines on Google Cloud : covers core Dataflow principles (ii)Building Resilient Streaming Analytics Systems on Google Cloud : covers streaming basics concepts like windowing, triggers, and watermarks >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<...

7. Serverless Data Processing with Dataflow: Operations

In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances...

8. Data Collection and Processing with Python

This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You'll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site. The course is well-suited for you if you have already taken the "Python Basics" and "Python Functions, Files, and Dictionaries" courses (courses 1 and 2 of the Python 3 Programming Specialization). If you are already familiar with Python fundamentals but want practice at retrieving and processing complex nested data from Internet services, you can also benefit from this course without taking the previous two. This is the third of five courses in the Python 3 Programming Specialization...

9. Elevation Data Processing In GIS

4.1

(80)

If you are searching for the GIS project related to elevation data, this course is definitely for made for you. In this course you will learn about the lots of project based idea such as drainage management, surface analysis using DEM, volume calculation for hydrological study, remote sensing analysis etc. You also get the change to learn about the LiDAR technique. At the completion of this course, you built your confident to working any of the Digital Elevation datasets...

10. Serverless Data Processing with Dataflow: Develop Pipelines

In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks...

11. Alteryx - Data processing, Data Manipulation and Analytics

4.2

(104)

In this course we will make you a champion in Alteryx tool. From data extraction to business analytics this course will give you a good direction to kick start with your aspirations of delivering in Business Intelligence and/or Data Analytics. We will be majorly focusing on Business Intelligence and will add concepts of Analytics. You will learn how to extract data from raw data sources, manipulate data and utilize data for running linear and logistic regression models besides machine learning models like SVM and DT. You will also understand forecasting for a time series driven data...

12. Data Pre-Processing for Data Analytics and Data Science

4.8

(61)

The Data Pre-processing for Data Analytics and Data Science course provides students with a comprehensive understanding of the crucial steps involved in preparing raw data for analysis. Data pre- processing is a fundamental stage in the data science workflow, as it involves transforming, cleaning, and integrating data to ensure its quality and usability for subsequent analysis. Throughout this course, students will learn various techniques and strategies for handling real-world data, which is often messy, inconsistent, and incomplete. They will gain hands-on experience with popular tools and libraries used for data pre-processing, such as Python and its data manipulation libraries (e. g., Pandas), and explore practical examples to reinforce their learning. Key topics covered in this course include: Introduction to Data Pre-processing:- Understanding the importance of data pre-processing in data analytics and data science- Overview of the data pre-processing pipeline- Data Cleaning Techniques: Identifying and handling missing values:- Dealing with outliers and noisy data- Resolving inconsistencies and errors in the data- Data Transformation: Feature scaling and normalization:- Handling categorical variables through encoding techniques- Dimensionality reduction methods (e. g., Principal Component Analysis)- Data Integration and Aggregation: Merging and joining datasets:- Handling data from multiple sources- Aggregating data for analysis and visualization- Handling Text and Time-Series Data: Text preprocessing techniques (e. g., tokenization, stemming, stop-word removal):- Time-series data cleaning and feature extraction- Data Quality Assessment: Data profiling and exploratory data analysis- Data quality metrics and assessment techniques- Best Practices and Tools: Effective data cleaning and pre- processing strategies:- Introduction to popular data pre-processing libraries and tools (e. g., Pandas, NumPy)...

13. Data Processing with Logstash (and Filebeat)

4.4

(2,659)

Want to learn how to process events with Logstash? Then you have come to the right place; this course is by far the most comprehensive course on Logstash here at Udemy! This course specifically covers Logstash, meaning than we can go into much more detail than if this course covered the entire Elastic Stack. So if you want to learn Logstash specifically, then this course is for you! This course assumes no prior knowledge of or experience with Logstash. We start from the very basics and gradually transition into more advanced topics. The course is designed so that you can follow along the whole time step by step, and you can find all of the configuration files within a GitHub repository. The course covers topics such as handling Apache web server logs (both access and error logs), data enrichment, sending data to Elasticsearch, visualizing data with Kibana, along with covering a number of popular use cases that you are likely to come across. Upon completing this course, you will know all of the most important aspects of Logstash, and will be able to build complex pipeline configurations and process many different kinds of events and data. What is Logstash?In case you don't know what Logstash is all about, it is an event processing engine developed by the company behind Elasticsearch, Kibana, and more. Logstash is often used as a key part of the ELK stack or Elastic Stack, so it offers a strong synergy with these technologies. You can use Logstash for processing many different kinds of events, and an event can be many things. You can process access or error logs from a web server, or you can send events to Logstash from an e-commerce application, such as when an order was received or a payment was processed. You can ingest data from files (flat files, JSON, XML, CSV, etc.), receive data over HTTP or TCP, retrieve data from databases, and more. Logstash then enables you to process and manipulate the events before sending them to a destination of your choice, such as Elasticsearch, OpenSearch, e-mail, or Slack. Why do we need Logstash?Because by sending events to Logstash, you decouple things. You effectively move event processing out of the web application and into Logstash, representing the entire data pipeline, or perhaps just a part of it. This means that if you need to change how events are processed, you don't need to deploy a new version of a web application, for instance. The event processing and its configuration is centralized within Logstash instead of every place you trigger events. This means that all the web application needs to do, is to send an event to Logstash; it doesn't need to know anything about what happens to the event afterwards and where it ends up. This improves your architecture and lets Logstash do what it does best; process events. Let's get started! I hope that you are ready to begin learning Logstash. Have a look around the curriculum if you want to check out the course content in more details. I look forward to seeing you inside the course!...

14. R Data Pre-Processing & Data Management - Shape your Data!

4.9

(651)

Let's get your data in shape! Data Pre-Processing is the very first step in data analytics. You cannot escape it, it is too important. Unfortunately this topic is widely overlooked and information is hard to find. With this course I will change this! Data Pre-Processing as taught in this course has the following steps: 1.       Data Import: this might sound trivial but if you consider all the different data formats out there you can imagine that this can be confusing. In the course we will take a look at a standard way of importing csv files, we will learn about the very fast fread method and I will show you what you can do if you have more exotic file formats to handle. 2.       Selecting the object class: a standard data. frame might be fine for easy standard tasks, but there are more advanced classes out there like the data. table. Especially with those huge datasets nowadays, a data. frame might not do it anymore. Alternatives will be demonstrated in this course. 3.       Getting your data in a tidy form: a tidy dataset has 1 row for each observation and 1 column for each variable. This might sound trivial, but in your daily work you will find instances where this simple rule is not followed. Often times you will not even notice that the dataset is not tidy in its layout. We will learn how tidyr can help you in getting your data into a clean and tidy format. 4.       Querying and filtering: when you have a huge dataset you need to filter for the desired parameters. We will learn about the combination of parameters and implementation of advanced filtering methods. Especially data. table has proven effective for that sort of querying on huge datasets, therefore we will focus on this package in the querying section. 5.       Data joins: when your data is spread over 2 different tables but you want to join them together based on given criteria, you will need joins for that. There are several methods of data joins in R, but here we will take a look at dplyr and the 2 table verbs which are such a great tool to work with 2 tables at the same time. 6.       Integrating and interacting with SQL: R is great at interacting with SQL. And SQL is of course the leading database language, which you will have to learn sooner or later as a data scientist. I will show you how to use SQL code within R and there is even a R to SQL translator for standard R code. And we will set up a SQLite database from within R. 7.  Outlier detection: Datasets often contain values outside a plausible range. Faulty data generation or entry happens regularly. Statistical methods of outlier detection help to identify these values. We will take a look at the implemention of these.8. Character strings as well as dates and time have their own rules when it comes to pre-processing. In this course we will also take a look at these types of data and how to effectively handle it in R. How do you best prepare yourself for this course? You only need a basic knowledge of R to fully benefit from this course. Once you know the basics of RStudio and R you are ready to follow along with the course material. Of course you will also get the R scripts which makes it even easier. The screencasts are made in RStudio so you should get this program on top of R. Add on packages required are listed in the course. Again, if you want to make sure that you have proper data with a tidy format, take a look at this course. It will make your analytics with R much easier!...

15. Data Science:Data Mining & Natural Language Processing in R

4.7

(387)

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.                                    LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.                                   NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You'll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you'll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data.  I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you'll know it all: visualization, stats, machine learning, data mining, and neural networks!   The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.  HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.    (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.    (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation.   (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.    (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniquesData Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data. How to Pre-Process and "Wrangle" your R data by removing NAs/No data, handling conditional data, grouping by attributes.. etcCreating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MOREStatistical analysis, statistical inference, and the relationships between variables. Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and RegressionWeb-Scraping using RExtracting text data from Twitter and Facebook using APIsText miningCommon Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW!...

16. Storing, Retrieving, and Processing JSON data with Python

By the end of this project, you will learn how to work with JSON data in python. we will learn what is an API and how we can access the data using HTTP requests in Python. We are going to retrieve the data and use TKinter module in python to develop a desktop application for browsing characters rolled in Rick and Morty series. During this project, you will learn what a JSON API is and how it works. you will learn about how to send an HTTP request to the server to retrieve the JSON data and at the end, we are going to learn how to use this data to develop a desktop application using python and TKinter...

17. Capstone: Retrieving, Processing, and Visualizing Data with Python

In the capstone, students will build a series of applications to retrieve, process and visualize data using Python. The projects will involve all the elements of the specialization. In the first part of the capstone, students will do some visualizations to become familiar with the technologies in use and then will pursue their own project to visualize some other data that they have or can find. Chapters 15 and 16 from the book “Python for Everybody” will serve as the backbone for the capstone. This course covers Python 3...

18. Designing Data Visualizations: Getting Started with Processing

Join Nicholas Felton – author of the Feltron Annual Reports one of the lead designers of Facebook’s timeline and co-founder of Daytum – to explore the world with data and design. This 90-minute class walks through the process of investigating a large amount of data using Processing to project onto a map and polishing the visual appearance in Illustrator. It’s a great introduction to Processing and provides a data set for you to get started with right away making this class perfect for anyone interested in design data geometry or minimalism. Follow your curiosity and become fluent in presenting the data surrounding us every day...

19. Spring Cloud Data Flow - Cloud Native Data Stream Processing

3.9

(179)

Understand the technical architecture along with installation and configuration of Spring Cloud Data Flow Applications. Create basic to advanced Streaming applications like time logger to TensorFlow Image Detection Stream Flow. You will learn the following as part of this course. Architecture of Spring Cloud Data FlowComponents of Spring Cloud Data Flow like Skipper Server, Spring Cloud Data Flow Server, Data Flow ShellUsing Data Flow Shell and Domain Specific Language (DSL)Configuring and usage of message brokers like RabbitMQ, KafkaInstallation and configuration of Spring Cloud Data Flow Ecosystem in Amazon Web Service (AWS) EC2 InstancesConfiguring Grafana Dashboard for Stream visualizationConfiguration of Source, Sink and ProcessorCreating custom Source, Sink and Processor applicationCoding using Spring Tool Suite (STS) for custom code developmentWorking with Spring Data Flow WebUI and analyzing logs on runtimesThis course is designed to cover all aspects of Spring Cloud Data Flow from basic installation to configuration in Docker as well as creating all type of Streaming applications like ETL, import/export, Predictive Analytics, Streaming Event processing etc., Few working examples/usecases are covered to have better understanding like Data extracting and interaction with JDBC databaseExtracting Twitter Data (Tweets) from TwitterSentiment analysis, Language Analysis and HashTag Analysis on Tweets from TwitterObject Detection/Prediction using TensorFlow processorPose Prediction using TensorFlow Processor...