Showing posts with label Coursera. Show all posts
Showing posts with label Coursera. Show all posts

Sunday 28 April 2024

Natural Language Processing Specialization

 

What you'll learn

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.

Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.

Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

Join Free: Natural Language Processing Specialization

Specialization - 4 course series

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. 

This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an 

AI-powered future

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. 

Younes Bensouda Mourri

 is an Instructor of AI at Stanford University who also helped build the 

Deep Learning Specialization

Łukasz Kaiser

 is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper. 

Applied Learning Project

This Specialization will equip you with machine learning basics and state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:

• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, translate words, and use locality-sensitive hashing to approximate nearest neighbors.

• Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.

• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions. 

• Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering, and to build chatbots. Learn T5, BERT, transformer, reformer, and more with 🤗  Transformers!

Friday 26 April 2024

Practical Time Series Analysis

 


There are 6 modules in this course

Welcome to Practical Time Series Analysis!

Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training.  This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. 

In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more.  We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.

Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.

You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!

Join Free: Practical Time Series Analysis

Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

An Intuitive Introduction to Probability

 


There are 5 modules in this course

This course will provide you with a basic, intuitive and practical introduction into Probability Theory. You will be able to learn how to apply Probability Theory in different scenarios and you will earn a "toolbox" of methods to deal with uncertainty in your daily life. 

The course is split in 5 modules. In each module you will first have an easy introduction into the topic, which will serve as a basis to further develop your knowledge about the topic and acquire the "tools" to deal with uncertainty. Additionally, you will have the opportunity to complete 5 exercise sessions to reflect about the content learned in each module and start applying your earned knowledge right away. 

The topics covered are: "Probability", "Conditional Probability", "Applications", "Random Variables", and "Normal Distribution".

Join Free : An Intuitive Introduction to Probability

You will see how the modules are taught in a lively way, focusing on having an entertaining and useful learning experience! We are looking forward to see you online!

Friday 8 March 2024

Fractal Data Science Professional Certificate

 


What you'll learn

 Apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios.   

Utilize SQL proficiency to retrieve, manipulate data and employ data visualization skills using Power BI to communicate insights.

Apply Python expertise for data manipulation, analysis and implement machine learning algorithms to create predictive models for applications.

Create compelling data stories to influence your audience and master the art of critically analyzing data while making decisions and recommendations.

Join Free: Fractal Data Science Professional Certificate

Professional Certificate - 8 course series

Data science is projected to create 11.5 1 million global job openings by 2026 and offers many of the remote 2 job opportunities in the industry.

Prepare for a new career in this high-demand field with a Professional Certificate from Fractal Analytics. Whether you're a recent graduate seeking a rewarding career shift or a professional aiming to upskill, this program will equip you with the essential skills demanded by the industry.

This curriculum is designed with a problem-solving approach at the center to equip and enable you with the skills, required to solve data science problems, instead of just focusing on the tools and applications.

Through hands-on courses you'll master Python programming, harness the power of machine learning, cultivate expertise in data manipulation, and build understanding of cognitive factors affecting decisions. You will also learn the direct application of tools like SQL, PowerBI, and Python to real-world scenarios.

Upon completion, you will earn a Professional Certificate, which will help to make your profile standout in your career journey.

Fractal Data Science Professional Certificate is one of the preferred qualifications for entry-level data science jobs at Fractal. Complete this certificate to make your profile standout from other candidates while applying for job openings at Fractal.

Applied Learning Project

Learners will be able to apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios and utilize SQL proficiency to retrieve and manipulate data and employ data visualization skills using Power BI to communicate insights. Becoming experts at Python programming to manipulate and analyze data. Learners will implement machine learning algorithms to create predictive models for diverse applications. And create compelling data stories to influence and inform your audience and master the art of critically analyzing data while making decisions and recommendations.

CertNexus Certified Data Science Practitioner Professional Certificate

 


Advance your career with in-demand skills

Receive professional-level training from CertNexus

Demonstrate your technical proficiency

Earn an employer-recognized certificate from CertNexus

Prepare for an industry certification exam

Join Free: CertNexus Certified Data Science Practitioner Professional Certificate

Professional Certificate - 5 course series

The field of Data Science has topped the Linked In Emerging Jobs list for the last 3 years with a projected growth of 28% annually and the World Economic Forum lists Data Analytics and Scientists as the top emerging job for 2022. 

Data can reveal insights and inform business—by guiding decisions and influencing day-to-day operations. This specialization will teach learners how to analyze, understand, manipulate, and present data within an effective and repeatable process framework and will enable you to bring value to the business by putting data science concepts into practice. 

This course is designed for business professionals that want to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.

Certified Data Science Practitioner (CDSP)  will prepare learners for the CertNexus CDSP certification exam. 

To complete your journey to the CDSP Certification

Complete the Coursera Certified Data Science Practitioner Professional Certificate.

Review the CDSP Exam Blueprint
.

Purchase your CDSP Exam Voucher

Register for your CDSP Exam.

Applied Learning Project

At the conclusion of each course, learners will have the opportunity to complete a project which can be added to their portfolio of work.  Projects include: 

Address a Business Issue with Data Science 

Extract, Transform, and Load Data

Data Analysis

Training a Machine Learning Model

Presenting a Data Science Project

IBM Data Engineering Professional Certificate

 


What you'll learn

Master the most up-to-date practical skills and knowledge data engineers use in their daily roles

Learn to create, design, & manage relational databases & apply database administration (DBA) concepts to RDBMSs such as MySQL, PostgreSQL, & IBM Db2 

Develop working knowledge of NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, and Spark Streaming 

Implement ETL & Data Pipelines with Bash, Airflow & Kafka; architect, populate, deploy Data Warehouses; create BI reports & interactive dashboards 

Join Free: IBM Data Engineering Professional Certificate

Professional Certificate - 13 course series

Prepare for a career in the high-growth field of data engineering. In this program, you’ll learn in-demand skills like Python, SQL, and Databases to get job-ready in less than 5 months.

Data engineering is building systems to gather data, process and organize raw data into usable information, and manage data. The work data engineers do provides the foundational information that data scientists and business intelligence (BI) analysts use to make recommendations and decisions.

This program will teach you the foundational data engineering skills employers are seeking for entry level data engineering roles, including Python, one of the most widely used programming languages. You’ll also master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data, and Spark with hands-on labs and projects.

You’ll learn to use Python programming language and Linux/UNIX shell scripts to extract, transform and load (ETL) data. You’ll also work with Relational Databases (RDBMS) and query data using SQL statements and use NoSQL databases as well as unstructured data. 

When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support. 

This program is ACE® recommended—when you complete, you can earn up to 12 college credits.

Applied Learning Project

Throughout this Professional Certificate, you will complete hands-on labs and projects to help you gain practical experience with Python, SQL, relational databases, NoSQL databases, Apache Spark, building data pipelines, managing databases, and working with data warehouses.

Design a relational database to help a coffee franchise improve operations.

Use SQL to query census, crime, and school demographic data sets.

Write a Bash shell script on Linux that backups changed files.

Set up, test, and optimize a data platform that contains MySQL, PostgreSQL, and IBM Db2 databases.

Analyze road traffic data to perform ETL and create a pipeline using Airflow and Kafka.

Design and implement a data warehouse for a solid-waste management company.

Move, query, and analyze data in MongoDB, Cassandra, and Cloudant NoSQL databases.

Train a machine learning model by creating an Apache Spark application.

This program is FIBAA recommended—when you complete, you can earn up to 8 ECTS credits.

Wednesday 6 March 2024

Data Analysis and Visualization Foundations Specialization

 


What you'll learn

Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools required for successful data analysis

Explain basic functionality of spreadsheets and utilize Excel to perform a variety of data analysis tasks like data wrangling and data mining

List various types of charts and plots and create them in Excel as well as work with Cognos Analytics to generate interactive dashboards

Join Free: Data Analysis and Visualization Foundations Specialization

Specialization - 4 course series

Deriving insights from data and communicating findings has become an increasingly important part of virtually every profession. This Specialization prepares you for this data-driven transformation by teaching you the core principles of data analysis and visualization and by giving you the tools and hands-on practice to communicate the results of your data discoveries effectively.  

You will be introduced to the modern data ecosystem. You will learn the skills required to successfully start data analysis tasks by becoming familiar with spreadsheets like Excel. You will examine different data sets, load them into the spreadsheet, and employ techniques like summarization, sorting, filtering, & creating pivot tables.

Creating stunning visualizations is a critical part of communicating your data analysis results. You will use Excel spreadsheets to create the many different types of data visualizations such as line plots, bar charts, pie charts. You will also create advanced visualizations such as treemaps, scatter charts & map charts. You will then build interactive dashboards. 

This Specialization is designed for learners interested in starting a career in the field of Data or Business Analytics, as well as those in other professions, who need basic data analysis and visualization skills to supplement their primary job tasks.

This program is ACE® recommended—when you complete, you can earn up to 9 college credits.  

Applied Learning Project

Build your data analytics portfolio as you gain practical experience from producing artifacts in the interactive labs and projects throughout this program. Each course has a culminating project to apply your newfound skills:

In the first course, create visualizations to detect fraud by analyzing credit card data.

In the second course, import, clean, and analyze fleet vehicle inventory with Excel pivot tables.

In the third course, use car sales key performance indicator (KPI) data to create an interactive dashboard with stunning visualizations using Excel and IBM Cognos Analytics.

Only a modern web browser is required to complete these practical exercises and projects — no need to download or install anything on your device.

Introduction to Data Science Specialization

 


What you'll learn

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists  

Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio 

Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems

Write SQL statements and query Cloud databases using Python from Jupyter notebooks

Join Free: Introduction to Data Science Specialization

Specialization - 4 course series

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.  

This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.

You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations.

This Specialization can also be applied toward the 
IBM Data Science Professional Certificate. 

Applied Learning Project

All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets and tools like Jupyter, GitHub, and R Studio. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Creating and sharing a Jupyter Notebook containing code blocks and markdown

Devising a problem that can be solved by applying the data science methodology and explain how to apply each stage of the methodology to solve it

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

Saturday 2 March 2024

Data Analysis with Python

 


What you'll learn

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Join Free: Data Analysis with Python

There are 6 modules in this course
Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.  

Topics covered include:  
- collecting and importing data 
- cleaning, preparing & formatting data 
- data frame manipulation 
- summarizing data 
- building machine learning regression models 
- model refinement 
- creating data pipelines 

You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.  

In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions.  


Get Started with Python by Google

 


What you'll learn

Explain how Python is used by data professionals 

Explore basic Python building blocks, including syntax and semantics

Understand loops, control statements, and string manipulation

Use data structures to store and organize data 

Join Free : Get Started with Python

There are 5 modules in this course

This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures. 

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.    

By the end of this course, you will:

-Define what a programming language is and why Python is used by data scientists

-Create Python scripts to display data and perform operations

-Control the flow of programs using conditions and functions

-Utilize different types of loops when performing repeated operations

-Identify data types such as integers, floats, strings, and booleans

-Manipulate data structures such as , lists, tuples, dictionaries, and sets

-Import and use Python libraries such as NumPy and pandas

Thursday 29 February 2024

Probabilistic Graphical Models 1: Representation

 


Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Probabilistic Graphical Models 1: Representation

There are 7 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

Evaluations of AI Applications in Healthcare

 


What you'll learn

Principles and practical considerations for integrating AI into clinical workflows

Best practices of AI applications to promote fair and equitable healthcare solutions

Challenges of regulation of AI applications and which components of a model can be regulated

What standard evaluation metrics do and do not provide

Join Free: Evaluations of AI Applications in Healthcare

There are 7 modules in this course

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Monday 26 February 2024

IBM Data Analytics with Excel and R Professional Certificate

 


What you'll learn

Master the most up-to-date practical skills and knowledge data analysts use in their daily roles

Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter

Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, & creating charts

Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny

Join Free: IBM Data Analytics with Excel and R Professional Certificate

Professional Certificate - 9 course series

Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months.

Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language. If you’re interested in using Python, please explore the IBM Data Analyst PC. 

This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.

You’ll learn the latest skills and tools used by professional data analysts and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter Notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process,  including data preparation, statistical analysis, data visualization, predictive modeling and creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.

This program is ACE® and FIBAA recommended—when you complete, you can earn up to 15 college credits and 4 ECTS credits.

Applied Learning Project

You will complete hands-on labs to build your portfolio and  gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

Projects include:

Analyzing fleet vehicle inventory data using pivot tables.

Using key performance indicator (KPI) data from car sales to create an interactive dashboard.

Identifying patterns in countries’ COVID-19 testing data rates using R.

Using SQL with the RODBC R package to analyze foreign grain markets.

Creating linear and polynomial regression models and comparing them with weather station data to predict precipitation.

Using the R Shiny package to create a dashboard that examines trends in census data.

Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and a dynamic Leaflet map widget to investigate how weather affects bike-sharing demand.

Predict Sales Revenue with scikit-learn

 


What you'll learn

Build simple linear regression models in Python

Apply scikit-learn and statsmodels to regression problems

Employ explorartory data analysis (EDA) with seaborn and pandas

Explain linear regression to both technical and non-technical audiences

Join Free: Predict Sales Revenue with scikit-learn

About this Guided Project

In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. 

By the end of this course, you will be able to:

- Explain the core ideas of linear regression to technical and non-technical audiences
- Build a simple linear regression model in Python with scikit-learn
- Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas
- Evaluate a simple linear regression model using appropriate metrics

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Jupyter and Python 3.7 with all the necessary libraries pre-installed.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- 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.

Generative AI: Enhance your Data Analytics Career

 


What you'll learn

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 

 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Join Free: Generative AI: Enhance your Data Analytics Career

There are 3 modules in this course

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. 

In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries.

You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more.

Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. 

This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.

Data Analyst Career Guide and Interview Preparation

 


What you'll learn

Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Join Free: Data Analyst Career Guide and Interview Preparation

There are 4 modules in this course

Data analytics professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field?

This course will prepare you to enter the job market as a great candidate for a data analyst position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. 

The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a take-home challenges and get to practice completing them. Additionally, it provides information about the regular functions and tasks of data analysts, as well as the opportunities of the profession and some options for career development.

You will get guidance from a number of experts in the data industry through the course. They will discuss their own career paths and talk about what they have learned about networking, interviewing, solving coding problems, and fielding other questions you may encounter as a candidate. Let seasoned data analysis professionals share their experience to help you get ahead and land the job you want.

Machine Learning With Big Data

 


Build your subject-matter expertise

This course is part of the Big Data Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Machine Learning With Big Data

There are 7 modules in this course

Want to make sense of the volumes of data you have collected?  Need to incorporate data-driven decisions into your process?  This course provides an overview of machine learning techniques to explore, analyze, and leverage data.  You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

At the end of the course, you will be able to:

Design an approach to leverage data using the steps in the machine learning process.
Apply machine learning techniques to explore and prepare data for modeling.
Identify the type of machine learning problem in order to apply the appropriate set of techniques.
Construct models that learn from data using widely available open source tools.
Analyze big data problems using scalable machine learning algorithms on Spark.

Tuesday 20 February 2024

Cybersecurity Attack and Defense Fundamentals Specialization

 


What you'll learn

Information security threats, vulnerabilities, and attacks.

Network security assessment techniques and tools.

Computer forensics fundaments, digital evidence, and forensic investigation phases.

Join Free: Cybersecurity Attack and Defense Fundamentals Specialization

Specialization - 3 course series

This Specialization can be taken by students, IT professionals, IT managers, career changers, and anyone who seeks a cybersecurity career or aspires to advance their current role. This course is ideal for those entering the cybersecurity workforce, providing foundational, hands-on skills to solve the most common security issues organizations face today.


This 3-course Specialization will help you gain core cybersecurity skills needed to protect critical data, networks, and digital assets. You will learn to build the foundation that enables individuals to grow their skills in specialized domains like penetration testing, security consulting, auditing, and system and network administration. 

Applied Learning Project

Learn to troubleshoots  network security problems, monitor alerts, and follow policies, procedures, and standards to protect information assets. You will gain practical skills cybersecurity professionals need in Information Security, Network Security, Computer Forensics, Risk Management, Incident Handling, and the industry best practices.

Cybersecurity: Developing a Program for Your Business Specialization

 


Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from University System of Georgia

Join Free: Cybersecurity: Developing a Program for Your Business Specialization

Specialization - 4 course series

Cybersecurity is an essential business skill for the evolving workplace. For-profit companies, government agencies, and not-for-profit organizations all need technologically proficient, business-savvy information technology security professionals. In this Specialization, you will learn about  a variety of processes for protecting business assets through policy, education and training, and technology best practices. You’ll develop an awareness of the risks and cyber threats or attacks associated with modern information usage, and explore key technical and managerial topics required for a balanced approach to information protection. Topics will include mobility, the Internet of Things, the human factor,  governance and management practices.

Enterprise and Infrastructure Security

 


Build your subject-matter expertise

This course is part of the Introduction to Cyber Security Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Enterprise and Infrastructure Security

There are 4 modules in this course

This course introduces a series of advanced and current topics in cyber security, many of which are especially relevant in modern enterprise and infrastructure settings. The basics of enterprise compliance frameworks are provided with introduction to NIST and PCI. Hybrid cloud architectures are shown to provide an opportunity to fix many of the security weaknesses in modern perimeter local area networks.

Emerging security issues in blockchain, blinding algorithms, Internet of Things (IoT), and critical infrastructure protection are also described for learners in the context of cyber risk. Mobile security and cloud security hyper-resilience approaches are also introduced. The course completes with some practical advice for learners on how to plan careers in cyber security.

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